Jobs Data Scientist

98
  • Β· 31 views Β· 5 applications Β· 22d

    Game Mathematician

    Countries of Europe or Ukraine Β· Product Β· 3 years of experience Β· English - None
    Hello, future colleague! At DreamPlay, we create pixel-perfect slot games powered by our own engine. We are reinventing the gambling experience by delivering unique, high-quality games to the market. We are a team of professionals who value quality,...

    Hello, future colleague!
     

    At DreamPlay, we create pixel-perfect slot games powered by our own engine. We are reinventing the gambling experience by delivering unique, high-quality games to the market.
    We are a team of professionals who value quality, ownership, transparency, and collaboration. We believe in a results-driven environment where everyone has the space to grow, contribute, and make an impact.

    We’re currently looking for a Game Mathematician to join our team and help shape the core mechanics behind our games.

     

    Requirements:

    • Experience in developing mathematics for casino slots.
    • Strong analytical and problem-solving skills with a high level of attention to detail
    • Solid background in Combinatorics, Probability Theory, and Statistics
    • Advanced proficiency in MS Excel, including building and adapting large, complex spreadsheets
    • Strong critical thinking skills and the ability to manage multiple tasks simultaneously
       

    Key Responsibilities:

    • Test and validate mathematical outcomes to ensure accuracy and quality (using MS Excel, programming, and proprietary tools).
    • Design and maintain high-quality mathematical documentation, including math models, game logic, PAR sheets, and customer-facing materials.
    • Analyze and balance game mechanics to ensure fairness, performance, and regulatory compliance.
    • Run simulations and optimize mathematical algorithms to improve game performance and player engagement.
    • Maintain clear technical documentation to support collaboration across teams and meet compliance requirements.
    • Stay up to date with industry trends, emerging technologies, and competitor practices to continuously improve game design strategies.
       

    We Offer:

    • Opportunity to work remotely or from our Kyiv office.
    • Flexible working hours β€” you choose when to start your day.
    • Modern Mac equipment.
    • Career growth within a team of iGaming professionals.
    • Supportive, transparent team culture with minimal bureaucracy.
    • Time-off policy that fits real life (paid vacation, sick leave, public holiday).
    • Benefits for employees.
    More
  • Β· 39 views Β· 3 applications Β· 23d

    Machine Learning Engineer (Real-Time Inference Systems)

    Full Remote Β· Countries of Europe or Ukraine Β· Product Β· 6 years of experience Β· English - C2
    Our client is a leading mobile marketing and audience platform empowering the global app ecosystem with advanced solutions in mobile marketing, audience building, and monetization. With direct integrations into 500,000+ mobile apps worldwide, they process...

    Our client is a leading mobile marketing and audience platform empowering the global app ecosystem with advanced solutions in mobile marketing, audience building, and monetization.

    With direct integrations into 500,000+ mobile apps worldwide, they process massive volumes of first-party data to deliver intelligent, real-time, and scalable advertising decisions. Their platform operates at extreme scale, serving billions of requests per day under strict latency and performance constraints.

    About the Role

    We are looking for a highly skilled, independent, and driven Machine Learning Engineer to own and lead the design and development of our next-generation real-time inference services.

    This is a rare opportunity to take ownership of mission-critical systems on a massive scale, working at the intersection of machine learning, large-scale backend engineering, and business logic.

    You will build robust, low-latency services that seamlessly combine predictive models with dynamic decision logic β€” while meeting extreme requirements for performance, reliability, and scalability.

    Responsibilities

    • Own and lead the design and development of low-latency inference services handling billions of requests per day
    • Build and scale real-time decision-making engines, integrating ML models with business logic under strict SLAs
    • Collaborate closely with Data Science teams to deploy models reliably into production
    • Design and operate systems for model versioning, shadowing, and A/B testing in runtime
    • Ensure high availability, scalability, and observability of production services
    • Continuously optimize latency, throughput, and cost efficiency
    • Work independently while collaborating with stakeholders across Algo, Infra, Product, Engineering, Business Analytics, and Business teams

    Requirements

    • B.Sc. or M.Sc. in Computer Science, Software Engineering, or a related technical field
    • 5+ years of experience building high-performance backend or ML inference systems
    • Strong expertise in Python
    • Hands-on experience with low-latency APIs and real-time serving frameworks
      (FastAPI, Triton Inference Server, TorchServe, BentoML)
    • Experience designing scalable service architectures
    • Strong knowledge of async processing, message queues, and streaming systems
      (Kafka, Pub/Sub, SQS, RabbitMQ, Kinesis)
    • Solid understanding of model deployment, online/offline feature parity, and real-time monitoring
    • Experience with cloud platforms (AWS, GCP, or OCI)
    • Strong hands-on experience with Kubernetes
    • Experience with in-memory / NoSQL databases
      (Aerospike, Redis, Bigtable)
    • Familiarity with observability stacks: Prometheus, Grafana, OpenTelemetry
    • Strong sense of ownership and ability to drive solutions end-to-end
    • Passion for performance, clean architecture, and impactful systems
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  • Β· 19 views Β· 0 applications Β· 24d

    Senior Data Scientist

    Ukraine Β· Product Β· 5 years of experience Β· English - B2
    Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of...

    Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.

    About the client:
    The company is a trailblazer in the world of data-driven advertising, known for its innovative approach to optimizing ad placements and campaign effectiveness through advanced analytics and machine learning techniques. Our mission is to revolutionize the advertising sector by enabling brands to reach their audiences more effectively.

    About the role:
    We are seeking an experienced and motivated Senior Data Scientist to join our dynamic team. The ideal candidate will have deep expertise in supervised learning, reinforcement learning, and optimization techniques. You will play a pivotal role in developing and implementing advanced machine learning models, driving actionable insights, and optimizing our advertising solutions.
    This position is based in Ukraine. The team primarily works remotely, with occasional in-person meetings in the Kyiv or Lviv office.

    Responsibilities:
    - Develop and implement advanced supervised and reinforcement learning models to improve ad targeting and campaign performance.
    - Collaborate with cross-functional teams to identify opportunities for leveraging machine learning and optimization techniques to solve business problems.
    - Conduct extensive data analysis and feature engineering to prepare datasets for machine learning tasks.
    - Apply optimization algorithms to enhance the effectiveness and efficiency of advertising campaigns.
    - Evaluate and refine existing models to enhance their accuracy, efficiency, and scalability.
    - Utilize statistical techniques and machine learning algorithms to analyze large and complex datasets.
    - Communicate findings and recommendations effectively to both technical and non-technical stakeholders.
    - Stay updated with the latest advancements in machine learning, reinforcement learning, and optimization techniques.
    - Work with engineering teams to integrate models into production systems.
    - Monitor, troubleshoot, and improve the performance of deployed models.
    - Mentor junior data scientists and contribute to the continuous improvement of the data science practice within the company.

    Requirements:
    - 5+ years of experience in data science or machine learning roles, with a strong focus on supervised learning, reinforcement learning, and optimization techniques.
    - Technical Skills:
    - Proficiency in Python.
    - Strong understanding of working with relational databases and SQL.
    - Experience with machine learning libraries such as scikit-learn, TensorFlow, PyTorch, or similar.
    - Deep understanding of statistical modeling and supervised learning algorithms (e.g., linear regression, logistic regression, decision trees, random forests, SVMs, gradient boosting, neural networks).
    - Hands-on experience with reinforcement learning algorithms and frameworks like OpenAI Gym.
    - Practical experience with optimization algorithms (linear, non-linear, combinatorial, etc.).
    - Hands-on experience with data manipulation tools and libraries (e.g., pandas, NumPy).
    - Familiarity with cloud services, specifically AWS, is a plus.
    - Practical experience building and managing cloud-based ML pipelines using AWS services (e.g. SageMaker, Bedrock) is a plus.
    - Education:
    - Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Engineering, or a related field. A PhD is a plus.

    Other Skills:
    - Strong analytical and problem-solving skills.
    - Excellent communication skills, with the ability to clearly articulate complex concepts to diverse audiences.
    - Ability to work in a fast-paced environment and manage multiple priorities.
    - Strong organizational skills and attention to detail.
    - Ability to mentor and guide junior data scientists.
    - Must be able to communicate with U.S.-based teams

    The company offers:
    - An opportunity to be at the forefront of advertising technology, impacting major marketing decisions.
    - A collaborative, innovative environment where your contributions make a difference.
    - The chance to work with a passionate team of data scientists, engineers, product managers, and designers.
    - A culture that values learning, growth, and the pursuit of excellence.

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  • Β· 44 views Β· 7 applications Β· 24d

    Data Scientist to $6000

    Full Remote Β· Countries of Europe or Ukraine Β· 5 years of experience Β· English - B2
    We are looking for an experienced Data Scientist to join our team. Requirements: 5–8+ years of experience in Data Science/Analytics Strong background in Mathematics, Statistics, or related field Solid knowledge of statistical inference and hypothesis...

    We are looking for an experienced Data Scientist to join our team.

     

    Requirements:

    • 5–8+ years of experience in Data Science/Analytics
    • Strong background in Mathematics, Statistics, or related field
    • Solid knowledge of statistical inference and hypothesis testing (p-values, Z-tests, Chi-Square)
    • Experience with machine learning for insight generation (e.g., clustering, segmentation, prediction)
    • Strong skills in Python and SQL
    • Strong stakeholder management and communication skills
    • Proven experience creating executive-ready reports and PowerPoint presentations
    • Ability to explain complex analytics to non-technical audiences
    • Upper-Intermediate or higher level of English (B2+)

     

    Responsibilities

    • Lead end-to-end analytics: from problem definition to insights and recommendations
    • Apply statistical analysis and machine learning for segmentation, trend analysis, and predictive insights
    • Design and interpret statistical tests (p-values, Z-tests, Chi-Square, confidence intervals)
    • Translate analytical results into clear business narratives
    • Prepare analytical reports and executive-level PowerPoint presentations (core part of the role)Partner with business teams to align analytics with commercial objectives
    • Query, clean, and analyze data using SQL and Python
    • Act as a trusted analytics partner; mentor junior team members when needed

     

    We offer:

    • A full-time job and a long-term contract
    • Flexible working hours
    • Paid vacation and sick leave
    • Managing your taxes and accounting
    • Career and professional growth opportunities
    • Optional benefits package that includes Health insurance, Gym membership, English courses, compensation of certification, courses, and training
    • Creative and lively team of IT specialists, adequate management, and zero unnecessary bureaucracy
    More
  • Β· 23 views Β· 0 applications Β· 25d

    Data Architect (Azure Platform)

    Full Remote Β· Ukraine Β· 10 years of experience Β· English - B2
    Description As the Data Architect, you will be the senior technical visionary for the Data Platform. You will be responsible for the high-level design of the entire solution, ensuring it is scalable, secure, and aligned with the company’s long-term...

    Description

    As the Data Architect, you will be the senior technical visionary for the Data Platform. You will be responsible for the high-level design of the entire solution, ensuring it is scalable, secure, and aligned with the company’s long-term strategic goals. Your decisions will form the technical foundation upon which the entire platform is built, from initial batch processing to future real-time streaming capabilities.

    Requirements

    Required Skills (Must-Haves)

    – Cloud Architecture: Extensive experience designing and implementing large-scale data platforms on Microsoft Azure.
    – Expert Technical Knowledge: Deep, expert-level understanding of the Azure data stack, including ADF, Databricks, ADLS, Synapse, and Purview.
    – Data Concepts: Mastery of data warehousing, data modeling (star schemas), data lakes, and both batch and streaming architectural patterns.
    – Strategic Thinking: Ability to align technical solutions with long-term business strategy.

    Nice-to-Have Skills:

    – Hands-on Coding Ability: Proficiency in Python/PySpark, allowing for the creation of architectural proofs-of-concept.
    – DevOps & IaC Acumen: Deep understanding of CI/CD for data platforms and experience with Infrastructure as Code (Bicep/Terraform)/Experience with AzureDevOps for BigData services
    – Azure Cost Management: Experience with FinOps and optimizing the cost of Azure data services.

    Job responsibilities

    – End-to-End Architecture Design: Design and document the complete, end-to-end data architecture, encompassing data ingestion, processing, storage, and analytics serving layers.
    – Technology Selection & Strategy: Make strategic decisions on the use of Azure services (ADF, Databricks, Synapse, Event Hubs) to meet both immediate MVP needs and future scalability requirements.
    – Define Standards & Best Practices: Establish data modeling standards, development best practices, and governance policies for the engineering team to follow.
    – Technical Leadership: Provide expert technical guidance and mentorship to the data engineers and BI developers, helping them solve the most complex technical challenges.
    – Stakeholder Communication: Clearly articulate the architectural vision, benefits, and trade-offs to technical teams, project managers, and senior business leaders.

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  • Β· 63 views Β· 5 applications Β· 25d

    Machine Learning Engineer

    Part-time Β· Full Remote Β· Countries of Europe or Ukraine Β· 3 years of experience Β· English - B2
    Responsibilities Model Fine-Tuning and Deployment: Fine-tune pre-trained models (e.g., BERT, GPT) for specific tasks and deploy them using Amazon SageMaker and Bedrock. RAG Workflows: Establish Retrieval-Augmented Generation (RAG) workflows that...

    Responsibilities

     

    Model Fine-Tuning and Deployment:

    Fine-tune pre-trained models (e.g., BERT, GPT) for specific tasks and deploy them using Amazon SageMaker and Bedrock.

    RAG Workflows:

    Establish Retrieval-Augmented Generation (RAG) workflows that leverage knowledge bases built on Kendra or OpenSearch. This includes integrating various data sources, such as corporate documents, inspection checklists, and real-time external data feeds.

    MLOps Integration:

    The project includes a comprehensive MLOps framework to manage the end-to-end lifecycle of machine learning models. This includes continuous integration and delivery (CI/CD) pipelines for model training, versioning, deployment, and monitoring. Automated workflows ensure that models are kept up-to-date with the latest data and are optimized for performance in production environments.

    Scalable and Customizable Solutions:

    Ensure that both the template and ingestion pipelines are scalable, allowing for adjustments to meet specific customer needs and environments. This involves setting up RAG workflows, knowledge bases using Kendra/OpenSearch, and seamless integration with customer data sources.

    End-to-End Workflow Automation:

    Automate the end-to-end process from user input to response generation, ensuring that the solution leverages AWS services like Bedrock Agents, CloudWatch, and QuickSight for real-time monitoring and analytics.

    Advanced Monitoring and Analytics:

    Integrated with AWS CloudWatch, QuickSight, and other monitoring tools, the accelerator provides real-time insights into performance metrics, user interactions, and system health. This allows for continuous optimization of service delivery and rapid identification of any issues.

    Model Monitoring and Maintenance:

    Implement model monitoring to track performance metrics and trigger retraining as necessary.

    Collaboration:

    Work closely with data engineers and DevOps engineers to ensure seamless integration of models into the production pipeline.

    Documentation:

    Document model development processes, deployment procedures, and monitoring setups for knowledge sharing and future reference.

     

    Must-Have Skills

     

    Machine Learning: Strong experience with machine learning frameworks such as TensorFlow, PyTorch, or Hugging Face Transformers.

    MLOps Tools: Proficiency with Amazon SageMaker for model training, deployment, and monitoring.

    Document processing: Experience with document processing for Word, PDF, images.

    OCR: Experience with OCR tools like Tesseract / AWS Textract (preferred)

    Programming: Proficiency in Python, including libraries such as Pandas, NumPy, and Scikit-Learn.

    Model Deployment: Experience with deploying and managing machine learning models in production environments.

    Version Control: Familiarity with version control systems like Git.

    Automation: Experience with automating ML workflows using tools like AWS Step Functions or Apache Airflow.

    Agile Methodologies: Experience working in Agile environments using tools like Jira and Confluence.

     

    Nice-to-Have Skills

     

    LLM: Experience with LLM / GenAI models, LLM Services (Bedrock or OpenAI), LLM abstraction like (Dify, Langchain, FlowiseAI), agent frameworks, rag.

    Deep Learning: Experience with deep learning models and techniques.

    Data Engineering: Basic understanding of data pipelines and ETL processes.

    Containerization: Experience with Docker and Kubernetes (EKS).

    Serverless Architectures: Experience with AWS Lambda and Step Functions.

    Rule engine frameworks: Like Drools or similar

     

    If you are a motivated individual with a passion for ML and a desire to contribute to a dynamic team environment, we encourage you to apply for this exciting opportunity. Join us in shaping the future of infrastructure and driving innovation in software delivery processes.

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  • Β· 95 views Β· 36 applications Β· 30d

    Data Scientist

    Full Remote Β· Worldwide Β· Product Β· 3 years of experience Β· English - B1
    Almus is looking for a Data Scientist to join our Analytics team and build production-grade machine learning models that directly impact marketing and business performance. You will work on end-to-end ML solutions, from data and features to deployment and...

    Almus is looking for a Data Scientist to join our Analytics team and build production-grade machine learning models that directly impact marketing and business performance.

    You will work on end-to-end ML solutions, from data and features to deployment and monitoring, focusing on improving LTV prediction quality, optimizing ML-driven costs, and driving key metrics such as LTV, ROAS, retention, and CAC. This is an individual contributor role with strong ownership, close collaboration with Marketing, Product, and Data teams, and a clear focus on real business impact.

    Apply to join Almus and take ownership of high-impact data initiatives!

     

    Responsibilities

    • Design, develop, and deploy machine learning models to production
    • Improve product and business decision-making through data-driven approaches
    • Build and evolve end-to-end ML pipelines (data β†’ features β†’ model β†’ inference β†’ monitoring)
    • Drive measurable impact on key product and commercial metrics
    • Standardize ML approaches within the team (best practices, documentation, reproducibility)
    • Provide technical input to the architecture of analytics and ML infrastructure
    • Develop and deploy models that drive growth in LTV, ROAS, retention, and CAC
    • Influence performance and lifecycle marketing strategy
    • Act as a domain expert and collaborate closely with Marketing, Product, and Data Engineering teams

     

    What We Look For

    • 3+ years of experience as a Data Scientist / ML Engineer
    • Experience working with mobile subscription-based products
    • Strong Python skills (production-level code)
    • Solid knowledge of classical machine learning algorithms and practical experience applying them
    • Experience with feature engineering, model evaluation, and bias–variance trade-offs
    • Hands-on experience with marketing models such as LTV, churn, cohort, and funnel modeling
    • Experience with attribution, incrementality, and uplift modeling
    • Strong SQL skills and experience working with analytical datasets
    • Experience with production ML systems and A/B testing
    • English level: Intermediate+

       

    Nice to have

    • Experience with BigQuery
    • MLOps experience (Docker, CI/CD, model registres)
    • Experience working with performance marketing data (Meta, Google Ads, Adjust)
    • Knowledge of causal inference
    • Experience with AutoML and Bayesian models

       

    We Offer

    • Exciting challenges and growth prospects together with an international company
    • High decision-making speed and diverse projects
    • Flexibility in approaches, no processes for the sake of processes
    • Effective and friendly communication at any level
    • Highly competitive compensation package that recognizes your expertise and experience, Performance Review practice to exchange feedback and discuss terms of cooperation
    • Flexible schedule, opportunity to work in a stylish and comfortable office or remotely
    • Respect for work-life balance (holidays, sick days - of course)
    • Bright corporate events and gifts for employees
    • Additional medical insurance
    • Compensation for specialized training and conference attendance
    • Restaurant lunches at the company's expense for those working in the office, endless supplies of delicious food all year round
       
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  • Β· 43 views Β· 8 applications Β· 30d

    Data Scientist

    Countries of Europe or Ukraine Β· Product Β· 4 years of experience Β· English - None
    Join Burny Games β€” a Ukrainian company that creates mobile puzzle games. Our mission is to create top-notch innovative games to challenge players’ minds daily. What makes us proud? In just two years, we’ve launched two successful mobile games worldwide:...

    Join Burny Games β€” a Ukrainian company that creates mobile puzzle games. Our mission is to create top-notch innovative games to challenge players’ minds daily.

    What makes us proud?

    • In just two years, we’ve launched two successful mobile games worldwide: Playdoku and Colorwood Sort. We have paused some projects to focus on making our games better and helping our team improve.
    • Our games have been enjoyed by over 45 million players worldwide, and we keep attracting more players.
    • We’ve created a culture where we make decisions based on data, which helps us grow every month.
    • We believe in keeping things simple, focusing on creativity, and always searching for new and effective solutions.

    What are you working on?

    • Genres: Puzzle, Casual
    • Platforms: Mobile, iOS, Android, Social

    Team size and structure?

    130+ employees

    Key Responsibilities:

    • Build and maintain ML for product and marketing teams
    • Develop predictive systems for personalization, recommendations, and dynamic game content
    • Automate data workflows and create reliable, scalable ML pipelines from feature engineering to deployment
    • Monitor model performance, detect drift, and ensure ongoing accuracy and stability of ML systems
    • Partner with Product, Marketing, and Engineering to integrate ML solutions into live games and operational workflows
    • Own DS/ML projects end-to-end: from defining the problem to production deployment and iteration
    • Share knowledge, conduct code reviews, and promote best practices across the data team

    About You:

    • 4+ years of experience in Data Science or ML, with a track record of delivering production models (2+ years in gamedev or consumer apps businesses)
    • Strong background in statistical modeling, forecasting, and machine learning
    • Advanced programming skills in Python or R (pandas, numpy, scikit-learn, PyTorch/TensorFlow or tidyverse, caret, mlr), writing clean and maintainable code
    • Excellent SQL skills, confident with large-scale datasets and cloud data warehouses (BigQuery, Snowflake, Redshift)
    • Experience deploying, monitoring, and maintaining ML models in production environments
    • Strong problem-solving mindset, able to translate business and product goals into ML solutions
    • Clear communicator who can explain complex models and systems to both technical and non-technical teams
    • Passion for gaming and curiosity about player behavior

    Will Be a Plus:

    • Experience building user-level LTV forecasting models
    • Background in recommender systems, personalization, or contextual bandits
    • Familiarity with MLOps practices and tools
    • Experience with ETL/orchestration frameworks (dbt, Dataform, Airflow)
    • We run on GCP β€” experience with BigQuery, Vertex AI, Pub/Sub, and Cloud Run/Functions

    What we offer:

    • 100% payment of vacations and sick leave [20 days vacation, 22 days sick leave], medical insurance.
    • A team of the best professionals in the games industry.
    • Flexible schedule [start of work from 8 to 11, 8 hours/day].
    • L&D center with courses.
    • Self-learning library, access to paid courses.
    • Stable payments.

    The recruitment process:

    CV review β†’ Interview with TA manager β†’ Interview with Head of Analytics β†’ Final Enterview β†’ Job offer

    If you share our goals and values and are eager to join a team of dedicated professionals, we invite you to take the next step.

    More
  • Β· 74 views Β· 3 applications Β· 2d

    Senior Data Scientist

    Hybrid Remote Β· Ukraine Β· Product Β· 5 years of experience Β· English - B1 MilTech πŸͺ–
    ΠŸΡ€ΠΎ ΠΊΠΎΠΌΠ°Π½Π΄Ρƒ Ми β€” Ρ–Π½Ρ‚Π΅Π»Π΅ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½ΠΎ-Π°Π½Π°Π»Ρ–Ρ‚ΠΈΡ‡Π½ΠΈΠΉ ΠΏΡ–Π΄Ρ€ΠΎΠ·Π΄Ρ–Π», який ΠΏΡ€Π°Ρ†ΡŽΡ” Π· Π²Π΅Π»ΠΈΠΊΠΈΠΌΠΈ Ρ‚Π° складними масивами Π΄Π°Π½ΠΈΡ… Ρƒ високочутливому контСксті. Наша Ρ€ΠΎΠ±ΠΎΡ‚Π° зосСрСдТСна Π½Π° ΠΏΡ–Π΄Ρ‚Ρ€ΠΈΠΌΡ†Ρ– складних ΠΎΠΏΠ΅Ρ€Π°Ρ†Ρ–ΠΉΠ½ΠΈΡ… Ρ€Ρ–ΡˆΠ΅Π½ΡŒ, Π΄Π΅ Ρ‚ΠΎΡ‡Π½Ρ–ΡΡ‚ΡŒ, ΡˆΠ²ΠΈΠ΄ΠΊΡ–ΡΡ‚ΡŒ Π°Π½Π°Π»Ρ–Π·Ρƒ Ρ‚Π° систСмнС мислСння...

    ΠŸΡ€ΠΎ ΠΊΠΎΠΌΠ°Π½Π΄Ρƒ

    Ми β€” Ρ–Π½Ρ‚Π΅Π»Π΅ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½ΠΎ-Π°Π½Π°Π»Ρ–Ρ‚ΠΈΡ‡Π½ΠΈΠΉ ΠΏΡ–Π΄Ρ€ΠΎΠ·Π΄Ρ–Π», який ΠΏΡ€Π°Ρ†ΡŽΡ” Π· Π²Π΅Π»ΠΈΠΊΠΈΠΌΠΈ Ρ‚Π° складними масивами Π΄Π°Π½ΠΈΡ… Ρƒ високочутливому контСксті. Наша Ρ€ΠΎΠ±ΠΎΡ‚Π° зосСрСдТСна Π½Π° ΠΏΡ–Π΄Ρ‚Ρ€ΠΈΠΌΡ†Ρ– складних ΠΎΠΏΠ΅Ρ€Π°Ρ†Ρ–ΠΉΠ½ΠΈΡ… Ρ€Ρ–ΡˆΠ΅Π½ΡŒ, Π΄Π΅ Ρ‚ΠΎΡ‡Π½Ρ–ΡΡ‚ΡŒ, ΡˆΠ²ΠΈΠ΄ΠΊΡ–ΡΡ‚ΡŒ Π°Π½Π°Π»Ρ–Π·Ρƒ Ρ‚Π° систСмнС мислСння ΠΌΠ°ΡŽΡ‚ΡŒ ΠΊΡ€ΠΈΡ‚ΠΈΡ‡Π½Π΅ значСння.

    Частина Π·Π°Π΄Π°Ρ‡ ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ пов’язана Π· подіями Ρ‚Π° опСраціями, ΠΏΡ€ΠΎ які Π²ΠΈ час Π²Ρ–Π΄ часу ΠΌΠΎΠΆΠ΅Ρ‚Π΅ Ρ‡ΠΈΡ‚Π°Ρ‚ΠΈ Π² Π½ΠΎΠ²ΠΈΠ½Π°Ρ… β€” Ρ…ΠΎΡ‡Π° Π±Ρ–Π»ΡŒΡˆΡ–ΡΡ‚ΡŒ Π΄Π΅Ρ‚Π°Π»Π΅ΠΉ Π·Π°Π»ΠΈΡˆΠ°Ρ”Ρ‚ΡŒΡΡ Π½Π΅ΠΏΡƒΠ±Π»Ρ–Ρ‡Π½ΠΎΡŽ.
     

    ΠŸΡ€ΠΎ Ρ€ΠΎΠ»ΡŒ

    Ми ΡˆΡƒΠΊΠ°Ρ”ΠΌΠΎ Senior Data Scientist, який Π·Π΄Π°Ρ‚Π΅Π½ Π½Π΅ просто Π±ΡƒΠ΄ΡƒΠ²Π°Ρ‚ΠΈ ΠΌΠΎΠ΄Π΅Π»Ρ–, Π° ΡΡ‚Π²ΠΎΡ€ΡŽΠ²Π°Ρ‚ΠΈ Π°Π½Π°Π»Ρ–Ρ‚ΠΈΡ‡Π½Ρ– Ρ€Ρ–ΡˆΠ΅Π½Π½Ρ Π· ΠΏΡ€ΠΈΠΊΠ»Π°Π΄Π½ΠΈΠΌ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΎΠΌ. Роль ΠΏΠ΅Ρ€Π΅Π΄Π±Π°Ρ‡Π°Ρ” Ρ€ΠΎΠ±ΠΎΡ‚Ρƒ Π² ΡƒΠΌΠΎΠ²Π°Ρ… нСвизначСності, самостійнС формування Π³Ρ–ΠΏΠΎΡ‚Π΅Π· Ρ– Π²Ρ–Π΄ΠΏΠΎΠ²Ρ–Π΄Π°Π»ΡŒΠ½Ρ–ΡΡ‚ΡŒ Π·Π° ΠΊΡ–Π½Ρ†Π΅Π²ΠΈΠΉ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚.
     

    ΠžΡΠ½ΠΎΠ²Π½Ρ– обов’язки

    • ΠŸΠΎΠ±ΡƒΠ΄ΠΎΠ²Π° Ρ‚Π° впровадТСння ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·Π½ΠΈΡ… Ρ– Π°Π½Π°Π»Ρ–Ρ‚ΠΈΡ‡Π½ΠΈΡ… ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ для Ρ€ΠΎΠ±ΠΎΡ‚ΠΈ Π·Ρ– складними датасСтами.
    • ΠŸΠΎΡˆΡƒΠΊ інсайтів Ρƒ структурованих Ρ– нСструктурованих Π΄Π°Π½ΠΈΡ….
    • Використання сучасних ΠΏΡ–Π΄Ρ…ΠΎΠ΄Ρ–Π², Π·ΠΎΠΊΡ€Π΅ΠΌΠ° NLP Ρ‚Π° LLM, для Π°Π½Π°Π»Ρ–Π·Ρƒ ΠΉ ΠΎΠ±Ρ€ΠΎΠ±ΠΊΠΈ тСкстової Ρ–Π½Ρ„ΠΎΡ€ΠΌΠ°Ρ†Ρ–Ρ—.
    • ΠŸΠ΅Ρ€Π΅Ρ‚Π²ΠΎΡ€Π΅Π½Π½Ρ Ρ‚Π΅Ρ…Π½Ρ–Ρ‡Π½ΠΈΡ… Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ–Π² Π°Π½Π°Π»Ρ–Π·Ρƒ Ρƒ Π·Ρ€ΠΎΠ·ΡƒΠΌΡ–Π»Ρ– Π·Π²Ρ–Ρ‚ΠΈ Ρ‚Π° Π²Ρ–Π·ΡƒΠ°Π»Ρ–Π·Π°Ρ†Ρ–Ρ— для ΠΊΠΎΠΌΠ°Π½Π΄Π½ΠΈΡ… Ρ– ΡƒΠΏΡ€Π°Π²Π»Ρ–Π½ΡΡŒΠΊΠΈΡ… Ρ€Ρ–ΡˆΠ΅Π½ΡŒ.
    • ВиявлСння Π½Π΅ΠΎΡ‡Π΅Π²ΠΈΠ΄Π½ΠΈΡ… зв’язків Ρ– ΠΏΠ°Ρ‚Π΅Ρ€Π½Ρ–Π², Ρ‰ΠΎ ΠΌΠ°ΡŽΡ‚ΡŒ Π²ΠΏΠ»ΠΈΠ² Π½Π° Π±Π΅Π·ΠΏΠ΅ΠΊΡƒ Ρ‚Π° Π΅Ρ„Π΅ΠΊΡ‚ΠΈΠ²Π½Ρ–ΡΡ‚ΡŒ ΠΎΠΏΠ΅Ρ€Π°Ρ†Ρ–ΠΉ.
    • Π£Ρ‡Π°ΡΡ‚ΡŒ Ρƒ Ρ„ΠΎΡ€ΠΌΡƒΠ²Π°Π½Π½Ρ– Ρ‚Π° Ρ€ΠΎΠ·Π²ΠΈΡ‚ΠΊΡƒ Π°Ρ€Ρ…Ρ–Ρ‚Π΅ΠΊΡ‚ΡƒΡ€ΠΈ Π°Π½Π°Π»Ρ–Ρ‚ΠΈΡ‡Π½ΠΈΡ… Ρ€Ρ–ΡˆΠ΅Π½ΡŒ.
       

    Π’ΠΈΠΌΠΎΠ³ΠΈ

    • 5+ Ρ€ΠΎΠΊΡ–Π² ΠΏΡ€Π°ΠΊΡ‚ΠΈΡ‡Π½ΠΎΠ³ΠΎ досвіду Ρƒ Data Science / ML / Π°Π½Π°Π»Ρ–Ρ‚ΠΈΡ†Ρ– Π΄Π°Π½ΠΈΡ….
    • Π’ΠΏΠ΅Π²Π½Π΅Π½Π΅ володіння Python, SQL, класичним ML-стСком.
    • Досвід Ρ€ΠΎΠ±ΠΎΡ‚ΠΈ Π² ΡƒΠΌΠΎΠ²Π°Ρ… високої нСвизначСності Ρ‚Π° вміння самостійно Ρ„ΠΎΡ€ΠΌΡƒΠ»ΡŽΠ²Π°Ρ‚ΠΈ Π·Π°Π΄Π°Ρ‡Ρ–.
    • ΠŸΡ€Π°ΠΊΡ‚ΠΈΡ‡Π½Ρ– Π½Π°Π²ΠΈΡ‡ΠΊΠΈ Π²Ρ–Π·ΡƒΠ°Π»Ρ–Π·Π°Ρ†Ρ–Ρ— Π΄Π°Π½ΠΈΡ… (Tableau, Power BI Π°Π±ΠΎ кастомні Ρ€Ρ–ΡˆΠ΅Π½Π½Ρ).
    • Розуміння ΠΏΡ€ΠΈΠ½Ρ†ΠΈΠΏΡ–Π² Ρ€ΠΎΠ±ΠΎΡ‚ΠΈ ΠΌΠΎΠ²Π½ΠΈΡ… ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ (LLM) Ρ‚Π° досвід Ρ—Ρ… ΠΏΡ€ΠΈΠΊΠ»Π°Π΄Π½ΠΎΠ³ΠΎ використання.
    • АналітичнС мислСння, систСмний ΠΏΡ–Π΄Ρ…Ρ–Π΄, Π²Ρ–Π΄ΠΏΠΎΠ²Ρ–Π΄Π°Π»ΡŒΠ½Ρ–ΡΡ‚ΡŒ.
       

    Π€ΠΎΡ€ΠΌΠ°Ρ‚ Ρ‚Π° ΡƒΠΌΠΎΠ²ΠΈ

    • Π€ΠΎΡ€ΠΌΠ°Ρ‚ Ρ€ΠΎΠ±ΠΎΡ‚ΠΈ: Π³Ρ–Π±Ρ€ΠΈΠ΄Π½ΠΈΠΉ (ΠšΠΈΡ—Π², офіс + частково Π²Ρ–Π΄Π΄Π°Π»Π΅Π½ΠΎ).
    • Локація: розглядаємо ΠΊΠ°Π½Π΄ΠΈΠ΄Π°Ρ‚Ρ–Π², які ΠΏΠ΅Ρ€Π΅Π±ΡƒΠ²Π°ΡŽΡ‚ΡŒ Ρƒ ΠšΠΈΡ”Π²Ρ– Ρ‚Π° області.
    • Π“Ρ€Π°Ρ„Ρ–ΠΊ: стандартний Ρ€ΠΎΠ±ΠΎΡ‡ΠΈΠΉ дСнь.
    • Π€ΠΎΡ€ΠΌΠ°Ρ‚ співпраці: ΠΊΠΎΠ½Ρ‚Ρ€Π°ΠΊΡ‚ Π°Π±ΠΎ мобілізація.
       

    Ми ΠΏΡ€ΠΎΠΏΠΎΠ½ΡƒΡ”ΠΌΠΎ

    • Π ΠΎΠ±ΠΎΡ‚Ρƒ Π½Π°Π΄ Ρ€Π΅Π°Π»ΡŒΠ½ΠΈΠΌΠΈ Π·Π°Π΄Π°Ρ‡Π°ΠΌΠΈ Π· ΠΏΡ€ΠΈΠΊΠ»Π°Π΄Π½ΠΈΠΌ Π²ΠΏΠ»ΠΈΠ²ΠΎΠΌ.
    • Π‘ΠΈΠ»ΡŒΠ½Ρƒ ΠΊΠΎΠΌΠ°Π½Π΄Ρƒ профСсіоналів.
    • ЗабСзпСчСння Π½Π΅ΠΎΠ±Ρ…Ρ–Π΄Π½ΠΈΠΌ спорядТСнням.
    • Навчання Ρ‚Π° постійний профСсійний Ρ€ΠΎΠ·Π²ΠΈΡ‚ΠΎΠΊ.
    • Π‘Π΅Ρ€Π΅Π΄ΠΎΠ²ΠΈΡ‰Π΅, Π΄Π΅ Π²Π°ΠΆΠ»ΠΈΠ²ΠΈΠΉ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚, Π° Π½Π΅ Ρ„ΠΎΡ€ΠΌΠ°Π»ΡŒΠ½ΠΎΡΡ‚Ρ–.
    • ΠŸΠŸΠ” β€” ΠšΠΈΡ—Π² Ρ‚Π° ΠΎΠ±Π»Π°ΡΡ‚ΡŒ.
       

    Π— вас β€” систСмнС мислСння Ρ‚Π° Π²Ρ–Π΄ΠΏΠΎΠ²Ρ–Π΄Π°Π»ΡŒΠ½Ρ–ΡΡ‚ΡŒ.
    Π— нас β€” складні, змістовні ΠΉ ΠΏΠΎ-ΡΠΏΡ€Π°Π²ΠΆΠ½ΡŒΠΎΠΌΡƒ Π²Π°ΠΆΠ»ΠΈΠ²Ρ– Π·Π°Π΄Π°Ρ‡Ρ–.

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  • Β· 48 views Β· 1 application Β· 2d

    Trainee Risk manager

    Office Work Β· Ukraine (Kyiv) Β· Product Β· English - None
    SoftSvit β€” ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚ΠΎΠ²Π° IT-компанія, яка Π·Π°ΠΉΠΌΠ°Ρ”Ρ‚ΡŒΡΡ Π½Π΅ Ρ‚Ρ–Π»ΡŒΠΊΠΈ Ρ€ΠΎΠ·Ρ€ΠΎΠ±ΠΊΠΎΡŽ, Π°Π»Π΅ Ρ– просуванням власного ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚Ρƒ. Π—Π°Ρ€Π°Π· ΠΌΠΈ ΡˆΡƒΠΊΠ°Ρ”ΠΌΠΎ Ρƒ свою ΠΊΠΎΠΌΠ°Π½Π΄Ρƒ Risk manager-a. Π’ΠΈΠΌΠΎΠ³ΠΈ: Досвід Ρ€ΠΎΠ±ΠΎΡ‚ΠΈ Π² Π΅-ΠΊΠΎΠΌΠ΅Ρ€Ρ†Ρ–Ρ— Ρ‡ΠΈ Π°Π½Ρ‚ΠΈΡ„Ρ€ΠΎΠ΄Ρ– Π±ΡƒΠ΄Π΅ плюсом; Π’Ρ–Π΄ΠΏΠΎΠ²Ρ–Π΄Π°Π»ΡŒΠ½Ρ–ΡΡ‚ΡŒ; Вміння Π½Π° Π±Π°Π·ΠΎΠ²ΠΎΠΌΡƒ...

    SoftSvit β€” ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚ΠΎΠ²Π° IT-компанія, яка Π·Π°ΠΉΠΌΠ°Ρ”Ρ‚ΡŒΡΡ Π½Π΅ Ρ‚Ρ–Π»ΡŒΠΊΠΈ Ρ€ΠΎΠ·Ρ€ΠΎΠ±ΠΊΠΎΡŽ, Π°Π»Π΅ Ρ– просуванням власного ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚Ρƒ. Π—Π°Ρ€Π°Π· ΠΌΠΈ ΡˆΡƒΠΊΠ°Ρ”ΠΌΠΎ Ρƒ ΡΠ²ΠΎΡŽ ΠΊΠΎΠΌΠ°Π½Π΄Ρƒ Risk manager-a.

    Π’ΠΈΠΌΠΎΠ³ΠΈ:

    • Досвід Ρ€ΠΎΠ±ΠΎΡ‚ΠΈ Π² Π΅-ΠΊΠΎΠΌΠ΅Ρ€Ρ†Ρ–Ρ— Ρ‡ΠΈ Π°Π½Ρ‚ΠΈΡ„Ρ€ΠΎΠ΄Ρ– Π±ΡƒΠ΄Π΅ плюсом;
    • Π’Ρ–Π΄ΠΏΠΎΠ²Ρ–Π΄Π°Π»ΡŒΠ½Ρ–ΡΡ‚ΡŒ;
    • Вміння Π½Π° Π±Π°Π·ΠΎΠ²ΠΎΠΌΡƒ Ρ€Ρ–Π²Π½Ρ– ΠΏΡ€Π°Ρ†ΡŽΠ²Π°Ρ‚ΠΈ Π· Excel Ρ‚Π° Google Π”ΠΎΠΊΡƒΠΌΠ΅Π½Ρ‚Π°ΠΌΠΈ;
    • Π“Π½ΡƒΡ‡ΠΊΠ΅ мислСння, ΡƒΠ²Π°ΠΆΠ½Ρ–ΡΡ‚ΡŒ;
    • Досвід Ρ€ΠΎΠ±ΠΎΡ‚ΠΈ Π· Π²Π΅Π»ΠΈΠΊΠΈΠΌ обсягом Ρ–Π½Ρ„ΠΎΡ€ΠΌΠ°Ρ†Ρ–Ρ—;
    • Π—Π΄Π°Ρ‚Π½Ρ–ΡΡ‚ΡŒ Π°Π½Π°Π»Ρ–Π·ΡƒΠ²Π°Ρ‚ΠΈ ΠΎΡ‚Ρ€ΠΈΠΌΠ°Π½Ρ– Π΄Π°Π½Ρ– Ρ‚Π° Ρ€ΠΎΠ·ΡΡ‚авляти ΠΏΡ€Ρ–ΠΎΡ€ΠΈΡ‚Π΅Ρ‚ΠΈ.

    ΠžΠ±ΠΎΠ²β€™ΡΠ·ΠΊΠΈ:

    • ΠžΠΏΠ΅Ρ€Π°Ρ‚ΠΈΠ²Π½Π° ΠΏΠ΅Ρ€Π΅Π²Ρ–Ρ€ΠΊΠ° Π·Π°ΠΏΠΈΡ‚Ρ–Π² Ρ‰ΠΎΠ΄ΠΎ Π²ΠΈΠΏΠ»Π°Ρ‚ΠΈ ΠΊΠΎΡˆΡ‚Ρ–Π² ΠΊΠ»Ρ–Ρ”Π½Ρ‚Π°ΠΌ;
    • Аналіз Π»ΠΎΠ³Ρ–Π², виявлСння нСстандартних закономірностСй Ρ‚Π° Ρ€ΠΎΠ·Π±Ρ–ТностСй;
    • ІдСнтифікація особи ΠΊΠ»Ρ–Ρ”Π½Ρ‚Ρ–Π²;
    • ΠšΠΎΠΌΡƒΠ½Ρ–ΠΊΠ°Ρ†Ρ–Ρ Π· ΡΠ°ΠΏΠΏΠΎΡ€Ρ‚ΠΎΠΌ Ρ–Π· ΠΏΠ»Π°Ρ‚Ρ–ΠΆΠ½ΠΈΡ… Ρ‚Ρ€ΡƒΠ΄Π½ΠΎΡ‰Ρ–Π² Ρ‚Π° ΠΏΠΈΡ‚Π°Π½ΡŒ ΠΊΠ»Ρ–Ρ”Π½Ρ‚Ρ–Π²;
    • ΠœΠΎΠ½Ρ–Ρ‚ΠΎΡ€ΠΈΠ½Π³ статистичних ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊΡ–Π².

    Ми ΠΏΡ€ΠΎΠΏΠΎΠ½ΡƒΡ”ΠΌΠΎ:

    • Бучасний офіс ΠΏΠΎΡ€ΡƒΡ‡ Π· ΠΌΠ΅Ρ‚Ρ€ΠΎ;
    • Π“Ρ€Π°Ρ„Ρ–ΠΊ Ρ€ΠΎΠ±ΠΎΡ‚ΠΈ: 2/2 Π· 9:00β€”21:00 (2 Π΄Π΅Π½Π½Ρ– Π·ΠΌΡ–Π½ΠΈ, 2 Π²ΠΈΡ…Ρ–Π΄Π½ΠΈΡ…, 2 Π½Ρ–Ρ‡Π½Ρ– Π·ΠΌΡ–Π½ΠΈ);
    • ΠšΠ°Ρ„Π΅ Π½Π° Ρ‚Π΅Ρ€ΠΈΡ‚ΠΎΡ€Ρ–Ρ— офісу;
    • ΠœΠΎΠΆΠ»ΠΈΠ²Ρ–ΡΡ‚ΡŒ ΠΊΠ°Ρ€'Ρ”Ρ€Π½ΠΎΠ³ΠΎ Ρ– профСсійного зростання;
    • ΠžΡ„Ρ–Ρ†Ρ–ΠΉΠ½Π΅ оформлСння, ΠΎΠΏΠ»Π°Ρ‡ΡƒΠ²Π°Π½Π° відпустка Ρ‚Π° лікарняні;
    • ΠŸΡ–Ρ†Π°, Π½Π°ΠΏΠΎΡ—, Π½Π°ΡΡ‚Ρ–Π»ΡŒΠ½Ρ– Ρ–Π³Ρ€ΠΈ Π·Π° Ρ€Π°Ρ…ΡƒΠ½ΠΎΠΊ ΠΊΠΎΠΌΠΏΠ°Π½Ρ–Ρ—;
    • Ми Π·Π° ΡΠΏΠΎΡ€Ρ‚, Ρ‚ΠΎΠΌΡƒ щотиТня Π³Ρ€Π°Ρ”ΠΌΠΎ Ρƒ Π²ΠΎΠ»Π΅ΠΉΠ±ΠΎΠ»;
    • Кава, Ρ‡Π°ΠΉ, ΠΏΠ΅Ρ‡ΠΈΠ²ΠΎ, Ρ„Ρ€ΡƒΠΊΡ‚ΠΈ Ρ‚Π° Π³Π°Ρ€Π½ΠΈΠΉ настрій щодня.
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  • Β· 40 views Β· 0 applications Β· 3d

    ML / Computer Vision Engineer

    Hybrid Remote Β· Ukraine Β· Product Β· 1 year of experience Β· English - B1
    Universe Group ΡˆΡƒΠΊΠ°Ρ” ML / Computer Vision Engineer Ρ‡Π΅Ρ€Π΅Π· Π°ΠΊΡ‚ΠΈΠ²Π½Π΅ зростання R&D-напряму Ρ‚Π° ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚Ρƒ Visify. Visify (AI Photo Enhancer) - ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚, який ΠΌΠ°Ρ” користувачів Π· ΠΏΠΎΠ½Π°Π΄ 180 ΠΊΡ€Π°Ρ—Π½ світу Ρ– Π½Π΅Ρ‰ΠΎΠ΄Π°Π²Π½ΠΎ став 4-ΠΌ застосунком Ρƒ світі Π·Π° Ρ€Π΅ΠΉΡ‚ΠΈΠ½Π³ΠΎΠΌ App...

    Universe Group ΡˆΡƒΠΊΠ°Ρ” ML / Computer Vision Engineer Ρ‡Π΅Ρ€Π΅Π· Π°ΠΊΡ‚ΠΈΠ²Π½Π΅ зростання R&D-напряму Ρ‚Π° ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚Ρƒ Visify.

     

    Visify (AI Photo Enhancer) - ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚, який ΠΌΠ°Ρ” користувачів Π· ΠΏΠΎΠ½Π°Π΄ 180 ΠΊΡ€Π°Ρ—Π½ світу Ρ– Π½Π΅Ρ‰ΠΎΠ΄Π°Π²Π½ΠΎ став 4-ΠΌ застосунком Ρƒ світі Π·Π° Ρ€Π΅ΠΉΡ‚ΠΈΠ½Π³ΠΎΠΌ App Store. Команда Π²ΠΆΠ΅ Ρ€ΠΎΠ·Π²ΠΈΠ²Π°Ρ” ML-частину ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚Ρƒ, запускає Π½ΠΎΠ²Ρ– ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚ΠΈ Ρ‚Π° Π²Π΅Π΄Π΅ Π΄ΠΎΡΠ»Ρ–Π΄Π½ΠΈΡ†ΡŒΠΊΡ– ΠΏΡ€ΠΎΡ”ΠΊΡ‚ΠΈ. Ми ΠΏΠ»Π°Π½ΡƒΡ”ΠΌΠΎ підсилСння ML-ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ, яка Π΄ΠΎΠΏΠΎΠΌΠΎΠΆΠ΅ ΠΏΡ€ΠΈΡˆΠ²ΠΈΠ΄ΡˆΠΈΡ‚ΠΈ Ρ€ΠΎΠ·Ρ€ΠΎΠ±ΠΊΡƒ Ρ‚Π° Ρ€Π°Π·ΠΎΠΌ Ρ–Π· сильними Ρ–Π½ΠΆΠ΅Π½Π΅Ρ€Π°ΠΌΠΈ Π·Π°Π½ΡƒΡ€ΠΈΡ‚ΠΈΡΡŒ Ρƒ складні, Π½Π΅Ρ‚Ρ€ΠΈΠ²Ρ–Π°Π»ΡŒΠ½Ρ– Π·Π°Π΄Π°Ρ‡Ρ–.

     

    А Ρ‰ΠΎΠ± Π²Ρ–Π΄Ρ‡ΡƒΡ‚ΠΈ, як Ρ†Π΅ β€” ΠΏΡ€Π°Ρ†ΡŽΠ²Π°Ρ‚ΠΈ Ρƒ ΠΊΠΎΠΌβ€™ΡŽΠ½Ρ–Ρ‚Ρ–, якС об’єднує ΠΎΠ΄Π½ΠΎΠ΄ΡƒΠΌΡ†Ρ–Π² Ρ–Π· Π°ΠΌΠ±Ρ–Ρ†Ρ–Ρ”ΡŽ Π±ΡƒΠ΄ΡƒΠ²Π°Ρ‚ΠΈ ΡŽΠ½Ρ–ΠΊoΡ€Π½Π° Π· Π£ΠΊΡ€Π°Ρ—Π½ΠΈ, β€” подивись Π²Ρ–Π΄Π΅ΠΎ ΠΏΡ€ΠΎ Guru Apps.

     

    Π’ΠΎΠ±Ρ– Ρ‚ΠΎΡ‡Π½ΠΎ Π΄ΠΎ нас, якщо:

    • Π₯ΠΎΡ‡Π΅Ρˆ ΠΏΡ€Π°Ρ†ΡŽΠ²Π°Ρ‚ΠΈ Π½Π°Π΄ ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚ΠΎΠΌ, яким ΠΊΠΎΡ€ΠΈΡΡ‚ΡƒΡŽΡ‚ΡŒΡΡ Ρ€Π΅Π°Π»ΡŒΠ½Ρ– користувачі, Π° Π½Π΅ Β«Π΄Π΅ΠΌΠΎ-ΠΏΡ€ΠΎΡ”ΠΊΡ‚ΠΈΒ».
    • Для Ρ‚Π΅Π±Π΅ Π²Π°ΠΆΠ»ΠΈΠ²ΠΎ швидко рости: ΠΏΠΎΡ€ΡƒΡ‡ Π΄Π²Π° ΡΠΈΠ»ΡŒΠ½Ρ– ΡΠ΅Π½ΡŒΠΉΠΎΡ€Π½Ρ– ML-Ρ–Π½ΠΆΠ΅Π½Π΅Ρ€ΠΈ Ρ‚Π° ΠΆΠΈΠ²ΠΈΠΉ R&D.
    • Π¦Ρ–ΠΊΠ°Π²Π»ΡΡ‚ΡŒ Π·Π°Π΄Π°Ρ‡Ρ– Π· Computer Vision, Π°Π²Π°Ρ‚Π°Ρ€Π°ΠΌΠΈ, зобраТСннями, Π°Π½Ρ–ΠΌΠ°Ρ†Ρ–Ρ”ΡŽ Ρ‚Π° ML Ρƒ ΠΏΡ€ΠΎΠ΄Π°ΠΊΡˆΠ΅Π½Ρ–.
    • Π₯ΠΎΡ‡Π΅Ρˆ Π±ΡƒΡ‚ΠΈ Π² ΠΊΠΎΠΌΠΏΠ°Π½Ρ–Ρ—, яка Π±ΡƒΠ΄ΡƒΡ” Π³Π»ΠΎΠ±Π°Π»ΡŒΠ½Ρ– ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚ΠΈ ΠΉ Ρ€ΡƒΡ…Π°Ρ”Ρ‚ΡŒΡΡ Π΄ΠΎ статусу ΡŽΠ½Ρ–ΠΊΠΎΡ€Π½Π°.
    • Π’ΠΎΠ±Ρ– ΠΊΠΎΠΌΡ„ΠΎΡ€Ρ‚Π½ΠΎ Π² Π΄ΠΈΠ½Π°ΠΌΡ–Ρ‡Π½ΠΎΠΌΡƒ сСрСдовищі Π±Π΅Π· Π±ΡŽΡ€ΠΎΠΊΡ€Π°Ρ‚Ρ–Ρ—, Π· ΠΊΠ°Π½Π±Π°Π½ΠΎΠΌ Ρ– Π³Π½ΡƒΡ‡ΠΊΠΈΠΌΠΈ процСсами.
    • Π’ΠΈ Ρ†Ρ–ΠΊΠ°Π²ΠΈΡˆΡΡ Π½ΠΎΠ²ΠΈΠ½ΠΊΠ°ΠΌΠΈ індустрії Ρ–  любиш тСстувати усС Π½Π° ΠΏΡ€Π°ΠΊΡ‚ΠΈΡ†Ρ–.

     

    Π’Π²ΠΎΡ— ΠΌΠ°ΠΉΠ±ΡƒΡ‚Π½Ρ– Π·Π°Π΄Π°Ρ‡Ρ–:

    • Π ΠΎΠ±ΠΎΡ‚Π° Π· ML-Ρ‡Π°ΡΡ‚ΠΈΠ½ΠΎΡŽ ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚Ρƒ Visify Ρ‚Π° R&D-ΠΏΡ€ΠΎΡ”ΠΊΡ‚Π°ΠΌΠΈ: оптимізація Ρ–ΡΠ½ΡƒΡŽΡ‡ΠΈΡ… ML ΠΏΠ°ΠΉΠΏΠ»Π°ΠΉΠ½Ρ–Π² (LoRA, instruction tuning, adapters), Π° Ρ‚Π°ΠΊΠΎΠΆ ΠΏΠΎΠ²Π½ΠΈΠΉ Ρ†ΠΈΠΊΠ» Ρ€ΠΎΠ·Ρ€ΠΎΠ±ΠΊΠΈ Π½ΠΎΠ²ΠΈΡ… АІ ΠΏΠ°ΠΉΠΏΠ»Π°ΠΉΠ½Ρ–Π² Π· нуля.
    • Π ΠΎΠ·Ρ€ΠΎΠ±ΠΊΠ° Ρ‚Π° ΠΏΡ–Π΄Ρ‚Ρ€ΠΈΠΌΠΊΠ° Ρ€Ρ–ΡˆΠ΅Π½ΡŒ Ρƒ сфСрі Computer Vision.
    • Π ΠΎΠ±ΠΎΡ‚Π° Π· Π·Π°Π΄Π°Ρ‡Π°ΠΌΠΈ, пов’язаними Π· LLM / NLP (RAG-систСми, ΠΎΠ±Ρ€ΠΎΠ±ΠΊΠ° тСксту, Ρ€Π΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°Ρ†Ρ–ΠΉΠ½Ρ– систСми) β€” Π· ΠΌΠΎΠΆΠ»ΠΈΠ²Ρ–ΡΡ‚ΡŽ навчання Π² процСсі.
    • ΠŸΡ–Π΄ΠΊΠ»ΡŽΡ‡Π΅Π½Π½Ρ сторонніх AI-сСрвісів Ρ‚Π° оптимізація інфСрСнсу (quantization, batching, GPU/CPU Ρ€Π΅ΠΆΠΈΠΌΠΈ).
    • Взаємодія Π· Π±Π΅ΠΊΠ΅Π½Π΄ΠΎΠΌ, ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚ΠΎΠ²ΠΈΠΌΠΈ ΠΌΠ΅Π½Π΅Π΄ΠΆΠ΅Ρ€Π°ΠΌΠΈ, Π΄ΠΈΠ·Π°ΠΉΠ½Π΅Ρ€Π°ΠΌΠΈ Ρ‚Π° QA Π½Π° Ρ‰ΠΎΠ΄Π΅Π½Π½Ρ–ΠΉ основі.

     

    Π©ΠΎ ΠΎΡ‡Ρ–ΠΊΡƒΡ”ΠΌΠΎ Π²Ρ–Π΄ ΠΊΠ°Π½Π΄ΠΈΠ΄Π°Ρ‚Π°:

    Досвід Ρ– Π½Π°Π²ΠΈΡ‡ΠΊΠΈ:

    • 1-3 Ρ€ΠΎΠΊΠΈ ΠΊΠΎΠΌΠ΅Ρ€Ρ†Ρ–ΠΉΠ½ΠΎΠ³ΠΎ досвіду Π½Π° посаді ML Engineer / CV Engineer / AI Research Specialist (ΡˆΡƒΠΊΠ°Ρ”ΠΌΠΎ Π΄ΠΆΡƒΠ½Ρ–ΠΎΡ€ Ρ‚Π° ΠΌΡ–Π΄Π» спСціалістів).
    • ΠŸΡ€Π°ΠΊΡ‚ΠΈΡ‡Π½ΠΈΠΉ досвід Π· Python.
    • ΠŸΡ€Π°ΠΊΡ‚ΠΈΡ‡Π½ΠΈΠΉ досвід Π· популярними AI-Ρ„Ρ€Π΅ΠΉΠΌΠ²ΠΎΡ€ΠΊΠ°ΠΌΠΈ (PyTorch, Diffusers, LangChain, etc.).
    • Знання Ρ‚Π° Ρ€Π΅Π°Π»ΡŒΠ½ΠΈΠΉ досвід Ρƒ Computer Vision (CNN, Transformer-based, Stable Diffusion) Ρ‚Π° VideoGen (Kling, Veo3, Runway, Pika Labs, Luma AI, etc.).
    • ΠŸΡ€Π°ΠΊΡ‚ΠΈΡ‡Π½ΠΈΠΉ досвід Π· Π΄Π΅ΠΏΠ»ΠΎΡ”ΠΌ AI-сСрвісів (AWS, Docker, Π±ΡƒΠ΄Π΅ плюсом k8s).
    • ΠΠ½Π³Π»Ρ–ΠΉΡΡŒΠΊΠ° Π’1+.

     

    Π‘ΡƒΠ΄Π΅ ΠΏΠ΅Ρ€Π΅Π²Π°Π³ΠΎΡŽ:

    • Досвід ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠ½ΠΎΡ— Ρ€ΠΎΠ±ΠΎΡ‚ΠΈ Π· LLM / NLP (Π½Π΅ лишС як Ρ‡Π°Ρ‚, Π° API, ΠΏΠ°ΠΉΠΏΠ»Π°ΠΉΠ½ΠΈ, RAG Ρ‚ΠΎΡ‰ΠΎ).
    • Досвід Ρƒ ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚ΠΎΠ²Ρ–ΠΉ ΠΊΠΎΠΌΠΏΠ°Π½Ρ–Ρ—.
    • Π’Π΅Ρ…Π½Ρ–Ρ‡Π½Π° освіта.

     

    ΠžΡΠΎΠ±ΠΈΡΡ‚Ρ– якості:

    • Π’Ρ–Π΄ΠΊΡ€ΠΈΡ‚Ρ–ΡΡ‚ΡŒ Π΄ΠΎ Π½ΠΎΠ²ΠΎΠ³ΠΎ Ρ‚Π° Ρ‰ΠΈΡ€Π΅ баТання Ρ€ΠΎΠ·Π²ΠΈΠ²Π°Ρ‚ΠΈΡΡŒ.
    • Π—Π²ΠΈΡ‡ΠΊΠ° слідкувати Π·Π° Ρ‚Ρ€Π΅Π½Π΄Π°ΠΌΠΈ ΠΉ тСстувати Π½ΠΎΠ²Ρ– ΠΌΠΎΠ΄Π΅Π»Ρ– Ρ‚Π° інструмСнти.
    • Високий Ρ€Ρ–Π²Π΅Π½ΡŒ Π΅Π½Π΅Ρ€Π³Ρ–Ρ—, Π΄Ρ€Π°ΠΉΠ² Ρ– Π²Ρ–Π΄ΠΏΠΎΠ²Ρ–Π΄Π°Π»ΡŒΠ½Ρ–ΡΡ‚ΡŒ Π·Π° свої Π·Π°Π΄Π°Ρ‡Ρ–.
    • Уміння ΠΏΡ€Π°Ρ†ΡŽΠ²Π°Ρ‚ΠΈ як Π°Π²Ρ‚ΠΎΠ½ΠΎΠΌΠ½ΠΎ, Ρ‚Π°ΠΊ Ρ– Π² ΠΊΠΎΠΌΠ°Π½Π΄Ρ–.

     

    Universe Group Π±ΡƒΠ΄ΡƒΡ” tech-бізнСси, ΠΏΠ΅Ρ€Π΅Ρ‚Π²ΠΎΡ€ΡŽΡŽΡ‡ΠΈ Ρ–Π΄Π΅Ρ— Π½Π° Π³Π»ΠΎΠ±Π°Π»ΡŒΠ½Ρ– ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚ΠΈ. Π”ΠΎ Π³Ρ€ΡƒΠΏΠΈ Π²Ρ…ΠΎΠ΄ΡΡ‚ΡŒ Ρ‚Ρ€ΠΈ ΠΊΠΎΠΌΠΏΠ°Π½Ρ–Ρ—: Guru Apps, FORMA Ρ‚Π° Wisey. Π‡Ρ…Π½Ρ– ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚ΠΈ ΠΎΠ±β€™Ρ”Π΄Π½ΡƒΡŽΡ‚ΡŒ ΠΏΠΎΠ½Π°Π΄ 400 ΠΌΡ–Π»ΡŒΠΉΠΎΠ½Ρ–Π² користувачів Π·Ρ– 186 ΠΊΡ€Π°Ρ—Π½ світу, ΡΠΏΡ€ΠΎΡ‰ΡƒΡŽΡ‡ΠΈ Π±ΡƒΠ΄Π΅Π½Π½Ρ–ΡΡ‚ΡŒ Ρ– ΡΡ‚Π²ΠΎΡ€ΡŽΡŽΡ‡ΠΈ Π½ΠΎΠ²Ρ– моТливості для Ρ€ΠΎΠ·Π²ΠΈΡ‚ΠΊΡƒ. Universe Group Ρ‚Π°ΠΊΠΎΠΆ Ρ€ΠΎΠ·Π²ΠΈΠ²Π°Ρ” Π½ΠΎΠ²Ρ– бізнСси, Ρ‰ΠΎ Π±ΡƒΠ΄ΡƒΡ‚ΡŒ Ρ‡Π°ΡΡ‚ΠΈΠ½ΠΎΡŽ глобального Ρ‚Π΅Ρ…Π½ΠΎΠ»ΠΎΠ³Ρ–Ρ‡Π½ΠΎΠ³ΠΎ Ρ€ΠΈΠ½ΠΊΡƒ.

     

    Π©ΠΎ ΠΌΠΈ ΠΏΡ€ΠΎΠΏΠΎΠ½ΡƒΡ”ΠΌΠΎ:

    •  πŸ”Π ΠΎΠ·Π²ΠΈΡ‚ΠΎΠΊ Ρ‚Π° навчання – Ρ‚Π²Ρ–ΠΉ ріст Π²ΠΈΠ·Π½Π°Ρ‡Π°Ρ” успіх ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ. Π’Π½ΡƒΡ‚Ρ€Ρ–ΡˆΠ½Ρ– Ρ‚Ρ€Π΅Π½Ρ–Π½Π³ΠΈ Ρ‚Π° ΠΊΡ€Π°Ρ‰Ρ– СкспСрти Π· Π£ΠΊΡ€Π°Ρ—Π½ΠΈ Ρ‚Π° світу Π΄ΠΎΠΏΠΎΠΌΠΎΠΆΡƒΡ‚ΡŒ швидко ΠΏΡ€ΠΎΠΊΠ°Ρ‡Π°Ρ‚ΠΈ Π½Π°Π²ΠΈΡ‡ΠΊΠΈ.
    • β†—οΈΠšΠ°Ρ€β€™Ρ”Ρ€Π½Π΅ зростання – Ρƒ нас ΠΊΡƒΠ»ΡŒΡ‚ΡƒΡ€Π° швидкого Ρ€ΠΎΠ·Π²ΠΈΡ‚ΠΊΡƒ: Π΄ΠΎ 10 ΠΌΠ΅Π½Π΅Π΄ΠΆΠ΅Ρ€Ρ–Π² Ρ‰ΠΎΡ€ΠΎΠΊΡƒ ΠΎΡ‚Ρ€ΠΈΠΌΡƒΡŽΡ‚ΡŒ підвищСння. ВсС Π·Π°Π»Π΅ΠΆΠΈΡ‚ΡŒ Π²Ρ–Π΄ Ρ‚Π²ΠΎΠ³ΠΎ баТання Ρ‚Π° Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ–Π².
    • πŸ“All-inclusive офіс Ρƒ ΠšΠΈΡ”Π²Ρ– β€” Ρƒ нас Ρ” всС для Ρ‚Π²ΠΎΡ”Ρ— ΠΊΠΎΠΌΡ„ΠΎΡ€Ρ‚Π½ΠΎΡ— Ρ€ΠΎΠ±ΠΎΡ‚ΠΈ, Π° самС: сніданки, ΠΎΠ±Ρ–Π΄ΠΈ, Π΄ΠΎΡ€Ρ–ΠΆΠΊΠΈ для ходіння, silent room для фокуса ΡƒΠ²Π°Π³ΠΈ β€” Ρ†Π΅ Π΄Π°Π»Π΅ΠΊΠΎ Π½Π΅ всС, Ρ‰ΠΎ Ρ‡Π΅ΠΊΠ°Ρ” Π½Π° Ρ‚Π΅Π±Π΅ Π² Π½Π°ΡˆΠΎΠΌΡƒ спСйсі.
    • πŸ§³Π Π΅Π»ΠΎΠΊΠ°Ρ†Ρ–ΠΉΠ½ΠΈΠΉ ΠΏΠ°ΠΊΠ΅Ρ‚ – ΠΊΠΎΠΌΡ„ΠΎΡ€Ρ‚Π½ΠΈΠΉ ΠΏΠ΅Ρ€Π΅Ρ—Π·Π΄ Π΄ΠΎ ΠšΠΈΡ”Π²Π° Π°Π±ΠΎ Π’Π°Ρ€ΡˆΠ°Π²ΠΈ Π· Ρ„Ρ–Π½Π°Π½ΡΠΎΠ²ΠΎΡŽ ΠΏΡ–Π΄Ρ‚Ρ€ΠΈΠΌΠΊΠΎΡŽ, допомогою Ρ€Ρ–Ρ”Π»Ρ‚ΠΎΡ€Ρ–Π² Ρ‚Π° Π°Π΄Π°ΠΏΡ‚Π°Ρ†Ρ–Ρ”ΡŽ Π² Π½ΠΎΠ²ΠΎΠΌΡƒ місті.
    • πŸ€œπŸ»πŸ€›πŸ»ΠžΠ΄ΠΈΠ½ Ρ–Π· ΠΊΡ€Π°Ρ‰ΠΈΡ… соцпакСтів – Π²ΠΈΠ½Π°Π³ΠΎΡ€ΠΎΠ΄Π° Π²Ρ–Π΄ΠΏΠΎΠ²Ρ–Π΄Π°Ρ” Π·Π°ΠΏΠΈΡ‚Π°ΠΌ Ρ€ΠΈΠ½ΠΊΡƒ, 20 Π΄Π½Ρ–Π² ΠΎΠΏΠ»Π°Ρ‡ΡƒΠ²Π°Π½ΠΎΠ³ΠΎ Π²Ρ–Π΄ΠΏΠΎΡ‡ΠΈΠ½ΠΊΡƒ, співпраця Ρ‡Π΅Ρ€Π΅Π· ЀОП Π°Π±ΠΎ Дія.City, ΠΎΠΏΠ»Π°Ρ‡ΡƒΠ²Π°Π½Ρ– лікарняні, мСдстрахування Ρ‚Π° Π·Π°Π±Π΅Π·ΠΏΠ΅Ρ‡ΡƒΡ”ΠΌΠΎ харчуванням Π² офісах Ρƒ ΠšΠΈΡ”Π²Ρ– Ρ‚Π° Π’Π°Ρ€ΡˆΠ°Π²Ρ–.
    • πŸ’›Well-being program – ΠΌΠΈ турбуємося ΠΏΡ€ΠΎ ΠΌΠ΅Π½Ρ‚Π°Π»ΡŒΠ½Π΅ здоровʼя ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ, Ρ‚ΠΎΠΌΡƒ компСнсуємо Ρ€ΠΎΠ±ΠΎΡ‚Ρƒ Π· психологом, Π° Ρ‚Π°ΠΊΠΎΠΆ ΠΏΡ€ΠΎΠ²ΠΎΠ΄ΠΈΠΌΠΎ класи Π· ΠΉΠΎΠ³ΠΈ Ρ‚Π° ΠΌΠ΅Π΄ΠΈΡ‚Π°Ρ†Ρ–Ρ— Π² офісі.  
    • πŸ‡ΊπŸ‡¦ΠŸΡ–Π΄Ρ‚Ρ€ΠΈΠΌΠΊΠ° ΠΏΡ–Π΄ час Π²Ρ–ΠΉΠ½ΠΈ – Π±Π΅Π·ΠΏΠ΅ΠΊΠ° Ρ‚Π° ΡΡ‚Π°Π±Ρ–Π»ΡŒΠ½Ρ–ΡΡ‚ΡŒ для Ρ‚Π΅Π±Π΅ Ρ‚Π° Ρ‚Π²ΠΎΡ—Ρ… Ρ€Ρ–Π΄Π½ΠΈΡ…. Π—Π°Π±Π΅Π·ΠΏΠ΅Ρ‡ΡƒΡ”ΠΌΠΎ всім Π½Π΅ΠΎΠ±Ρ…Ρ–Π΄Π½ΠΈΠΌ для Π±Π΅Π·ΠΏΠ΅Ρ€Π΅Π±Ρ–ΠΉΠ½ΠΎΡ— Ρ€ΠΎΠ±ΠΎΡ‚ΠΈ Ρ‚Π° Π΄ΠΎΠ»ΡƒΡ‡Π°Ρ”ΠΌΠΎΡΡŒ Π΄ΠΎ Ρ–Π½Ρ–Ρ†Ρ–Π°Ρ‚ΠΈΠ² Ρ–Π· відновлСння Π£ΠΊΡ€Π°Ρ—Π½ΠΈ.
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  • Β· 16 views Β· 0 applications Β· 4d

    Computer Vision Engineer

    Office Work Β· Ukraine (Kyiv) Β· Product Β· 3 years of experience Β· English - None MilTech πŸͺ–
    Ми β€” ΡƒΠΊΡ€Π°Ρ—Π½ΡΡŒΠΊΠ° miltech-компанія, яка розробляє ΠΏΠ΅Ρ€Π΅Π΄ΠΎΠ²Ρ– Ρ‚Π΅Ρ…Π½ΠΎΠ»ΠΎΠ³Ρ–Ρ— для Ρ‚ΠΎΡ‡Π½ΠΎΠ³ΠΎ ураТСння Π²ΠΎΡ€ΠΎΠΆΠΈΡ… об’єктів. Ми ΡΡ‚Π²ΠΎΡ€ΡŽΡ”ΠΌΠΎ Ρ‚Π΅ΠΏΠ»ΠΎΠ²Ρ–Π·Ρ–ΠΉΠ½ΠΎ-ΠΎΠΏΡ‚ΠΈΡ‡Π½Ρ– систСми. Π ΠΎΠ·ΡˆΠΈΡ€ΡŽΡ”ΠΌΠΎ ΠΊΠΎΠΌΠ°Π½Π΄Ρƒ Ρ‚Π° ΡˆΡƒΠΊΠ°Ρ”ΠΌΠΎ досвідчСного Senior Computer Vision Engineer, який Π΄ΠΎΠΏΠΎΠΌΠΎΠΆΠ΅ Π±ΡƒΠ΄ΡƒΠ²Π°Ρ‚ΠΈ...

    Ми β€” ΡƒΠΊΡ€Π°Ρ—Π½ΡΡŒΠΊΠ° miltech-компанія, яка розробляє ΠΏΠ΅Ρ€Π΅Π΄ΠΎΠ²Ρ– Ρ‚Π΅Ρ…Π½ΠΎΠ»ΠΎΠ³Ρ–Ρ— для Ρ‚ΠΎΡ‡Π½ΠΎΠ³ΠΎ ураТСння Π²ΠΎΡ€ΠΎΠΆΠΈΡ… об’єктів. Ми ΡΡ‚Π²ΠΎΡ€ΡŽΡ”ΠΌΠΎ Ρ‚Π΅ΠΏΠ»ΠΎΠ²Ρ–Π·Ρ–ΠΉΠ½ΠΎ-ΠΎΠΏΡ‚ΠΈΡ‡Π½Ρ– систСми.

    Π ΠΎΠ·ΡˆΠΈΡ€ΡŽΡ”ΠΌΠΎ ΠΊΠΎΠΌΠ°Π½Π΄Ρƒ Ρ‚Π° ΡˆΡƒΠΊΠ°Ρ”ΠΌΠΎ досвідчСного Senior Computer Vision Engineer, який Π΄ΠΎΠΏΠΎΠΌΠΎΠΆΠ΅ Π±ΡƒΠ΄ΡƒΠ²Π°Ρ‚ΠΈ Ρ–Π½Ρ‚Π΅Π»Π΅ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½Ρ– систСми сприйняття для Π½ΠΎΠ²ΠΎΠ³ΠΎ покоління Π±Π΅Π·ΠΏΡ–Π»ΠΎΡ‚Π½ΠΈΡ… Ρ€Ρ–ΡˆΠ΅Π½ΡŒ. Π¦Π΅ Ρ€ΠΎΠ»ΡŒ для Ρ–Π½ΠΆΠ΅Π½Π΅Ρ€Π°, який Ρ…ΠΎΡ‡Π΅ ΠΏΡ€Π°Ρ†ΡŽΠ²Π°Ρ‚ΠΈ Π½Π° стику Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΡ–Π², Π°ΠΏΠ°Ρ€Π°Ρ‚Π½ΠΈΡ… сСнсорів Ρ– Ρ€Π΅Π°Π»ΡŒΠ½ΠΈΡ… ΠΏΠΎΠ»ΡŒΠΎΡ‚Π½ΠΈΡ… систСм β€” Π²Ρ–Π΄ Π°Ρ€Ρ…Ρ–Ρ‚Π΅ΠΊΡ‚ΡƒΡ€ΠΈ perception-підсистСм Π΄ΠΎ Ρ—Ρ… Ρ–Π½Ρ‚Π΅Π³Ρ€Π°Ρ†Ρ–Ρ— Ρ‚Π° тСстування Ρƒ складі ΠΏΠΎΠ²Π½ΠΎΡ†Ρ–Π½Π½ΠΎΠ³ΠΎ Π°ΠΏΠ°Ρ€Π°Ρ‚Ρƒ.

    Π©ΠΎ Π±ΡƒΠ΄Π΅ Ρƒ фокусі Ρ€ΠΎΠ»Ρ–

    Π ΠΎΠ·Π²ΠΈΡ‚ΠΎΠΊ Π°Π²Ρ‚ΠΎΠ½ΠΎΠΌΠ½ΠΈΡ… систСм
    БтворСння Π°Ρ€Ρ…Ρ–Ρ‚Π΅ΠΊΡ‚ΡƒΡ€ΠΈ ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠ½ΠΎΡ— частини Ρ‚Π° Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΡ–Ρ‡Π½ΠΈΡ… ΠΏΡ–Π΄Ρ…ΠΎΠ΄Ρ–Π², Ρ‰ΠΎ Π·Π°Π±Π΅Π·ΠΏΠ΅Ρ‡ΡƒΡŽΡ‚ΡŒ ΠΏΠΎΠ²Π½ΠΈΠΉ Ρ†ΠΈΠΊΠ» Π°Π²Ρ‚ΠΎΠ½ΠΎΠΌΠ½ΠΎΡ— Ρ€ΠΎΠ±ΠΎΡ‚ΠΈ БпЛА β€” Π²Ρ–Π΄ старту ΠΉ Π½Π°Π²Ρ–Π³Π°Ρ†Ρ–Ρ— Π΄ΠΎ виконання Ρ†Ρ–Π»ΡŒΠΎΠ²ΠΈΡ… сцСнаріїв.

    Алгоритми Π½Π°Π²Ρ–Π³Π°Ρ†Ρ–Ρ— Ρ‚Π° ΠΊΠΎΠΌΠΏβ€™ΡŽΡ‚Π΅Ρ€Π½ΠΎΠ³ΠΎ Π·ΠΎΡ€Ρƒ
    ΠŸΠΎΡ”Π΄Π½Π°Π½Π½Ρ класичних ΠΌΠ°Ρ‚Π΅ΠΌΠ°Ρ‚ΠΈΡ‡Π½ΠΈΡ… ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ Ρ–Π· сучасними ML-ΠΏΡ–Π΄Ρ…ΠΎΠ΄Π°ΠΌΠΈ для ΠΏΠΎΠ±ΡƒΠ΄ΠΎΠ²ΠΈ Π³Ρ–Π±Ρ€ΠΈΠ΄Π½ΠΈΡ… систСм ΠΎΡ€Ρ–Ρ”Π½Ρ‚Π°Ρ†Ρ–Ρ— Ρƒ просторі, Π΄Π΅ Computer Vision Π΄ΠΎΠΏΠΎΠ²Π½ΡŽΡ” Ρ–Π½Π΅Ρ€Ρ†Ρ–ΠΉΠ½Ρ– ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΈ.

    Π ΠΎΠ±ΠΎΡ‚Π° Π· Π²Ρ–Π·ΡƒΠ°Π»ΡŒΠ½ΠΈΠΌΠΈ Π΄Π°Π½ΠΈΠΌΠΈ
    Π ΠΎΠ·Ρ€ΠΎΠ±ΠΊΠ° Ρ‚Π° оптимізація систСм розпізнавання, класифікації Ρ‚Π° Ρ‚Ρ€Π΅ΠΊΡ–Π½Π³Ρƒ об’єктів Ρ–Π· фокусом Π½Π° ΡΡ‚Π°Π±Ρ–Π»ΡŒΠ½Ρƒ Ρ€ΠΎΠ±ΠΎΡ‚Ρƒ Π² Ρ€Π΅Π°Π»ΡŒΠ½ΠΎΠΌΡƒ часі Ρ‚Π° ΠΎΠ±ΠΌΠ΅ΠΆΠ΅Π½ΠΈΡ… ΠΎΠ±Ρ‡ΠΈΡΠ»ΡŽΠ²Π°Π»ΡŒΠ½ΠΈΡ… рСсурсах.

    Π’Ρ–Ρ€Ρ‚ΡƒΠ°Π»ΡŒΠ½Π΅ сСрСдовищС Ρ‚Π° тСстування
    БтворСння ΠΏΠ°ΠΉΠΏΠ»Π°ΠΉΠ½Ρ–Π² симуляції Ρ‚Π° ΠΏΠ΅Ρ€Π΅Π²Ρ–Ρ€ΠΊΠΈ Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΡ–Π²: Π²Ρ–Π΄ Ρ†ΠΈΡ„Ρ€ΠΎΠ²ΠΎΠ³ΠΎ модСлювання Π΄ΠΎ Hardware-in-the-Loop сцСнаріїв для ΠΏΠ΅Ρ€Π΅Π²Ρ–Ρ€ΠΊΠΈ ΠΏΠΎΠ²Π΅Π΄Ρ–Π½ΠΊΠΈ систСм Ρƒ складних ΡƒΠΌΠΎΠ²Π°Ρ….

    Π ΠΎΠ±ΠΎΡ‚Π° Π· польовими Π΄Π°Π½ΠΈΠΌΠΈ
    Бпівпраця Π· flight-командою ΠΏΡ–Π΄ час Π²ΠΈΠΏΡ€ΠΎΠ±ΡƒΠ²Π°Π½ΡŒ, формування Π²ΠΈΠΌΠΎΠ³ Π΄ΠΎ тСстових сцСнаріїв, Π°Π½Π°Π»Ρ–Π· Ρ‚Π΅Π»Π΅ΠΌΠ΅Ρ‚Ρ€Ρ–Ρ— Ρ‚Π° швидка адаптація Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΡ–Π² Π½Π° основі ΠΎΡ‚Ρ€ΠΈΠΌΠ°Π½ΠΎΠ³ΠΎ Ρ„Ρ–Π΄Π±Π΅ΠΊΡƒ.

    ΠΠ°Π΄Ρ–ΠΉΠ½Ρ–ΡΡ‚ΡŒ Ρ– Π±Π΅Π·ΠΏΠ΅Ρ‡Π½Ρ–ΡΡ‚ΡŒ
    ΠŸΡ€ΠΎΡ”ΠΊΡ‚ΡƒΠ²Π°Π½Π½Ρ ΠΌΠ΅Ρ…Π°Π½Ρ–Π·ΠΌΡ–Π² fault detection, Π»ΠΎΠ³Ρ–ΠΊΠΈ Π°Π²Π°Ρ€Ρ–ΠΉΠ½ΠΎΠ³ΠΎ рСагування Ρ‚Π° систСм, які Π·Π°Π±Π΅Π·ΠΏΠ΅Ρ‡ΡƒΡŽΡ‚ΡŒ ΡΡ‚Π°Π±Ρ–Π»ΡŒΠ½Ρƒ Ρ€ΠΎΠ±ΠΎΡ‚Ρƒ Π² нСстандартних Π°Π±ΠΎ ΠΊΡ€ΠΈΡ‚ΠΈΡ‡Π½ΠΈΡ… ситуаціях.

    Π©ΠΎ ΠΎΡ‡Ρ–ΠΊΡƒΡ”Ρ‚ΡŒΡΡ Π²Ρ–Π΄ ΠΊΠ°Π½Π΄ΠΈΠ΄Π°Ρ‚Π°

    • ΠšΠΎΠΌΠ΅Ρ€Ρ†Ρ–ΠΉΠ½ΠΈΠΉ досвід Ρ€ΠΎΠ·Ρ€ΠΎΠ±ΠΊΠΈ Π½Π° Python Ρ‚Π°/Π°Π±ΠΎ C++ Ρƒ сСрСдовищі Linux.
    • ΠŸΡ€Π°ΠΊΡ‚ΠΈΡ‡Π½Π° Ρ€ΠΎΠ±ΠΎΡ‚Π° Π· PyTorch Π°Π±ΠΎ TensorFlow, Π° Ρ‚Π°ΠΊΠΎΠΆ інструмСнтами Computer Vision (OpenCV).
    • Розуміння сучасних ΠΏΡ–Π΄Ρ…ΠΎΠ΄Ρ–Π² Π΄ΠΎ Object Detection, image matching Ρ‚Π° ΠΏΠΎΡˆΡƒΠΊΡƒ схоТих Π·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΡŒ.
    • Бильна Π±Π°Π·Π° Π² Π³Π΅ΠΎΠΌΠ΅Ρ‚Ρ€Ρ–Ρ— Ρ‚Π° ΠΌΠ°Ρ‚Π΅ΠΌΠ°Ρ‚ΠΈΡ†Ρ–: 3D-ΠΏΡ€ΠΎΠ΅ΠΊΡ†Ρ–Ρ—, projective geometry, досвід Ρ–Π· SLAM-ΠΏΡ–Π΄Ρ…ΠΎΠ΄Π°ΠΌΠΈ.
    • Досвід запуску CV-Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΡ–Π² Π½Π° edge-ΠΏΠ»Π°Ρ‚Ρ„ΠΎΡ€ΠΌΠ°Ρ… Ρ‚Π° калібрування сСнсорних систСм (ΠΊΠ°ΠΌΠ΅Ρ€Π° + IMU).
    • Навички ΠΎΠΏΡ‚ΠΈΠΌΡ–Π·Π°Ρ†Ρ–Ρ— ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ для embedded-сСрСдовища: ONNX, TensorRT, квантизація Π°Π±ΠΎ Ρ–Π½ΡˆΡ– Ρ‚Π΅Ρ…Π½Ρ–ΠΊΠΈ.
    • ΠžΡ€Ρ–Ρ”Π½Ρ‚Π°Ρ†Ρ–Ρ Π½Π° ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚ Ρ– Π³ΠΎΡ‚ΠΎΠ²Π½Ρ–ΡΡ‚ΡŒ швидко пСрСвіряти Π³Ρ–ΠΏΠΎΡ‚Π΅Π·ΠΈ Ρ‡Π΅Ρ€Π΅Π· ΠΏΡ€ΠΎΡ‚ΠΎΡ‚ΠΈΠΏΠΈ.

    Π‘ΡƒΠ΄Π΅ Π΄ΠΎΠ΄Π°Ρ‚ΠΊΠΎΠ²ΠΈΠΌ плюсом

    • Π ΠΎΠ±ΠΎΡ‚Π° Π· PX4, ArduPilot, ROS Π°Π±ΠΎ MAVLink.
    • Досвід Ρ–Π· remote sensing Π°Π±ΠΎ супутниковими Π΄Π°Π½ΠΈΠΌΠΈ.
    • ΠŸΡ€Π°ΠΊΡ‚ΠΈΠΊΠ° sensor fusion Ρ–Π· LIDAR, RGB-D Ρ‡ΠΈ ΠΌΡƒΠ»ΡŒΡ‚ΠΈΡΠΏΠ΅ΠΊΡ‚Ρ€Π°Π»ΡŒΠ½ΠΈΠΌΠΈ ΠΊΠ°ΠΌΠ΅Ρ€Π°ΠΌΠΈ.
    • Розуміння ΠΏΡ€ΠΈΠ½Ρ†ΠΈΠΏΡ–Π² GNC Ρ‚Π° Ρ„Ρ–Π·ΠΈΠΊΠΈ ΠΏΠΎΠ»ΡŒΠΎΡ‚Ρƒ.
    • MSc Π°Π±ΠΎ PhD Ρƒ Computer Science, Machine Learning Ρ‡ΠΈ Ρ€ΠΎΠ±ΠΎΡ‚ΠΎΡ‚Π΅Ρ…Π½Ρ–Ρ†Ρ–. 
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  • Β· 70 views Β· 8 applications Β· 4d

    Middle/Senior Data Scientist

    Full Remote Β· Countries of Europe or Ukraine Β· Product Β· 1 year of experience Β· English - None
    ΠŸΡ€ΠΈΠ²Π°Ρ‚ Π‘Π°Π½ΠΊ β€” Ρ” Π½Π°ΠΉΠ±Ρ–Π»ΡŒΡˆΠΈΠΌ Π±Π°Π½ΠΊΠΎΠΌ Π£ΠΊΡ€Π°Ρ—Π½ΠΈ Ρ‚Π° ΠΎΠ΄Π½ΠΈΠΌ Π· Π½Π°ΠΉΠ±Ρ–Π»ΡŒΡˆ Ρ–Π½Π½ΠΎΠ²Π°Ρ†Ρ–ΠΉΠ½ΠΈΡ… Π±Π°Π½ΠΊΡ–Π² світу. Π—Π°ΠΉΠΌΠ°Ρ” Π»Ρ–Π΄ΠΈΡ€ΡƒΡŽΡ‡Ρ– ΠΏΠΎΠ·ΠΈΡ†Ρ–Ρ— Π·Π° всіма фінансовими ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊΠ°ΠΌΠΈ Π² Π³Π°Π»ΡƒΠ·Ρ– Ρ‚Π° складає близько Ρ‡Π²Π΅Ρ€Ρ‚Ρ– всієї Π±Π°Π½ΠΊΡ–Π²ΡΡŒΠΊΠΎΡ— систСми ΠΊΡ€Π°Ρ—Π½ΠΈ. Ми Π²ΠΎΠ»ΠΎΠ΄Ρ–Ρ”ΠΌΠΎ Π²Π΅Π»ΠΈΡ‡Π΅Π·Π½ΠΈΠΌΠΈ масивами Π΄Π°Π½ΠΈΡ…...

    ΠŸΡ€ΠΈΠ²Π°Ρ‚ Π‘Π°Π½ΠΊ β€” Ρ” Π½Π°ΠΉΠ±Ρ–Π»ΡŒΡˆΠΈΠΌ Π±Π°Π½ΠΊΠΎΠΌ Π£ΠΊΡ€Π°Ρ—Π½ΠΈ Ρ‚Π° ΠΎΠ΄Π½ΠΈΠΌ Π· Π½Π°ΠΉΠ±Ρ–Π»ΡŒΡˆ Ρ–Π½Π½ΠΎΠ²Π°Ρ†Ρ–ΠΉΠ½ΠΈΡ… Π±Π°Π½ΠΊΡ–Π² світу. Π—Π°ΠΉΠΌΠ°Ρ” Π»Ρ–Π΄ΠΈΡ€ΡƒΡŽΡ‡Ρ– ΠΏΠΎΠ·ΠΈΡ†Ρ–Ρ— Π·Π° всіма фінансовими ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊΠ°ΠΌΠΈ Π² Π³Π°Π»ΡƒΠ·Ρ– Ρ‚Π° складає близько Ρ‡Π²Π΅Ρ€Ρ‚Ρ– всієї Π±Π°Π½ΠΊΡ–Π²ΡΡŒΠΊΠΎΡ— систСми ΠΊΡ€Π°Ρ—Π½ΠΈ.

    Ми Π²ΠΎΠ»ΠΎΠ΄Ρ–Ρ”ΠΌΠΎ Π²Π΅Π»ΠΈΡ‡Π΅Π·Π½ΠΈΠΌΠΈ масивами Π΄Π°Π½ΠΈΡ… Ρ– ΡˆΡƒΠΊΠ°Ρ”ΠΌΠΎ Middle/Senior Data Scientist, який Π΄ΠΎΠΏΠΎΠΌΠΎΠΆΠ΅ ΠΏΠ΅Ρ€Π΅Ρ‚Π²ΠΎΡ€ΡŽΠ²Π°Ρ‚ΠΈ Ρ—Ρ… Π½Π° Ρ–Π½Ρ‚Π΅Π»Π΅ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½Ρ– сСрвіси Π·Π° допомогою класичного машинного навчання Ρ‚Π° сучасних DeepLearning-ΠΏΡ–Π΄Ρ…ΠΎΠ΄Ρ–Π².

     

    ΠœΠ΅Ρ‚Π° посади: Π°Π½Π°Π»Ρ–Π· Π΄Π°Π½ΠΈΡ…, ΠΏΠΎΠ±ΡƒΠ΄ΠΎΠ²Π° Ρ‚Π° впровадТСння ML ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ

     

    ΠžΡΠ½ΠΎΠ²Π½Ρ– Π·Π°Π΄Π°Ρ‡Ρ–:

    • End-to-end Ρ€ΠΎΠ·Ρ€ΠΎΠ±ΠΊΠ°: ΠΏΡ€ΠΎΠ΅ΠΊΡ‚ування, навчання Ρ‚Π° впровадТСння ML/DL ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ (Π²Ρ–Π΄ дослідТСння Π΄ΠΎ ΠΏΡ€ΠΎΠ΄Π°ΠΊΡˆΠ΅Π½Ρƒ)
    • ΠšΠ»Π°ΡΠΈΡ‡Π½ΠΈΠΉ ML: Ρ€ΠΎΠ±ΠΎΡ‚Π° Π· Ρ‚Π°Π±Π»ΠΈΡ‡Π½ΠΈΠΌΠΈ Π΄Π°Π½ΠΈΠΌΠΈ, EDA, ΠΏΡ–Π΄Π³ΠΎΡ‚ΠΎΠ²ΠΊΠ° ΠΎΠ·Π½Π°ΠΊ (feature engineering) Ρ‚Π° ΠΏΠΎΠ±ΡƒΠ΄ΠΎΠ²Π° ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ класифікації/рСгрСсії
    • Deep Learning: створСння ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ для Ρ€ΠΎΠ±ΠΎΡ‚ΠΈ Π· тСкстом, зобраТСннями Ρ‚Π° Π·Π²ΡƒΠΊΠΎΠΌ Π·Π° ΠΏΠΎΡ‚Ρ€Π΅Π±ΠΈ бізнСсу
    • MLOps: контСйнСризація ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ (Docker), Π½Π°Π»Π°ΡˆΡ‚ΡƒΠ²Π°Π½Π½Ρ ΠΌΠΎΠ½Ρ–Ρ‚ΠΎΡ€ΠΈΠ½Π³Ρƒ Ρ—Ρ… якості Ρ‚Π° інтСграція Ρ‡Π΅Ρ€Π΅Π· API (FastAPI/Flask)
    • ДокумСнтування: вСдСння Ρ‚Π΅Ρ…Π½Ρ–Ρ‡Π½ΠΎΡ— Π΄ΠΎΠΊΡƒΠΌΠ΅Π½Ρ‚Π°Ρ†Ρ–Ρ— Ρ‚Π° прСзСнтація Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ–Π² (ΠΌΠ΅Ρ‚Ρ€ΠΈΠΊΠΈ, бізнСс-Π΅Ρ„Π΅ΠΊΡ‚) стСйкхолдСрам
       

    ΠžΡ‡Ρ–ΠΊΡƒΡ”ΠΌΠΎ Π²Ρ–Π΄ вас:

    • Досвід: 1+ Ρ€ΠΎΠΊΠΈ Π² Ρ€ΠΎΠ»Ρ– Data Scientist (ΠΊΠΎΠΌΠ΅Ρ€Ρ†Ρ–ΠΉΠ½ΠΈΠΉ досвід)
    • Π‘Ρ‚Π΅ΠΊ для ML: Π³Π»ΠΈΠ±ΠΎΠΊΠ΅ знання Python, Pandas, Scikit-learn, XGBoost/LightGBM
    • Π‘Ρ‚Π΅ΠΊ для DL: Π²ΠΏΠ΅Π²Π½Π΅Π½Π΅ володіння PyTorch Π°Π±ΠΎ TensorFlow/Keras
    • Π ΠΎΠ±ΠΎΡ‚Π° Π· Π΄Π°Π½ΠΈΠΌΠΈ: просунутий SQL (складні Π·Π°ΠΏΠΈΡ‚ΠΈ, Π°Π½Π°Π»Ρ–Ρ‚ΠΈΡ‡Π½Ρ– Ρ„ΡƒΠ½ΠΊΡ†Ρ–Ρ—)
    • Π†Π½ΠΆΠ΅Π½Π΅Ρ€Π½Ρ– Π½Π°Π²ΠΈΡ‡ΠΊΠΈ: Git, CI/CD, Docker, Ρ€ΠΎΠ±ΠΎΡ‚Π° Π· Ρ…ΠΌΠ°Ρ€Π½ΠΈΠΌΠΈ ΠΏΠ»Π°Ρ‚Ρ„ΠΎΡ€ΠΌΠ°ΠΌΠΈ (AWS/GCP)
    • ΠœΠ°Ρ‚Π΅ΠΌΠ°Ρ‚ΠΈΡ‡Π½Π° Π±Π°Π·Π°: розуміння статистики, Ρ‚Π΅ΠΎΡ€Ρ–Ρ— ймовірностСй Ρ‚Π° ΠΏΡ€ΠΈΠ½Ρ†ΠΈΠΏΡ–Π² Ρ€ΠΎΠ±ΠΎΡ‚ΠΈ Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΡ–Π² "ΠΏΡ–Π΄ ΠΊΠ°ΠΏΠΎΡ‚ΠΎΠΌ"
    • Візуалізація: вміння Π±ΡƒΠ΄ΡƒΠ²Π°Ρ‚ΠΈ Π΄Π°ΡˆΠ±ΠΎΡ€Π΄ΠΈ Π² BI-інструмСнтах Ρ‚Π° змістовні Π³Ρ€Π°Ρ„Ρ–ΠΊΠΈ

     

    Π‘ΡƒΠ΄Π΅ Π·Π½Π°Ρ‡Π½ΠΎΡŽ ΠΏΠ΅Ρ€Π΅Π²Π°Π³ΠΎΡŽ:

    • Досвід Π· GenAI Ρ‚Π° NLP
    • Досвід Ρƒ Π·Π°Π΄Π°Ρ‡Π°Ρ… Computer Vision
    • Досвід Π² Ρ–Π½ΡˆΠΈΡ… ΠΏΡ€ΠΎΠ΄Π²ΠΈΠ½ΡƒΡ‚ΠΈΡ… DeepLearning Π°Ρ€Ρ…Ρ–Ρ‚Π΅ΠΊΡ‚ΡƒΡ€Π°Ρ…

     

    Ми ΠΏΡ€ΠΎΠΏΠΎΠ½ΡƒΡ”ΠΌΠΎ:

    • Π ΠΎΠ±ΠΎΡ‚Ρƒ Π² Π½Π°ΠΉΠ±Ρ–Π»ΡŒΡˆΠΎΠΌΡƒ Ρ‚Π° Ρ–Π½Π½ΠΎΠ²Π°Ρ†Ρ–ΠΉΠ½ΠΎΠΌΡƒ Π±Π°Π½ΠΊΡƒ Π£ΠΊΡ€Π°Ρ—Π½ΠΈ
    • ΠžΡ„Ρ–Ρ†Ρ–ΠΉΠ½Π΅ ΠΏΡ€Π°Ρ†Π΅Π²Π»Π°ΡˆΡ‚ΡƒΠ²Π°Π½Π½Ρ Ρ‚Π° 24+4 ΠΊΠ°Π»Π΅Π½Π΄Π°Ρ€Π½ΠΈΡ… Π΄Π½Ρ– відпустки
    • ΠšΠΎΠ½ΠΊΡƒΡ€Π΅Π½Ρ‚Π½Ρƒ Π·Π°Ρ€ΠΎΠ±Ρ–Ρ‚Π½Ρƒ ΠΏΠ»Π°Ρ‚Ρƒ
    • ΠœΠ΅Π΄ΠΈΡ‡Π½Π΅ страхування Ρ‚Π° ΠΊΠΎΡ€ΠΏΠΎΡ€Π°Ρ‚ΠΈΠ²Π½ΠΈΠΉ ΠΌΠΎΠ±Ρ–Π»ΡŒΠ½ΠΈΠΉ зв’язок
    • ΠšΠΎΡ€ΠΏΠΎΡ€Π°Ρ‚ΠΈΠ²Π½Π΅ навчання
    • Π’Ρ–Π΄Π΄Π°Π»Π΅Π½Ρƒ Ρ€ΠΎΠ±ΠΎΡ‚Ρƒ Π°Π±ΠΎ сучасні офіси Π² ΠšΠΈΡ”Π²Ρ–, Π”Π½Ρ–ΠΏΡ€Ρ– Ρ‚Π° Π›ΡŒΠ²ΠΎΠ²Ρ–, оснащСні Π³Π΅Π½Π΅Ρ€Π°Ρ‚ΠΎΡ€Π°ΠΌΠΈ Ρ‚Π° Starlink
    • Π¦Ρ–ΠΊΠ°Π²Ρ– ΠΏΡ€ΠΎΡ”ΠΊΡ‚ΠΈ, Π°ΠΌΠ±Ρ–Ρ†Ρ–ΠΉΠ½Ρ– Π·Π°Π΄Π°Ρ‡Ρ– Ρ‚Π° Π΄ΠΈΠ½Π°ΠΌΡ–Ρ‡Π½ΠΈΠΉ Ρ€ΠΎΠ·Π²ΠΈΡ‚ΠΎΠΊ
    • Π”Ρ€ΡƒΠΆΠ½Ρ–ΠΉ профСсійний ΠΊΠΎΠ»Π΅ΠΊΡ‚ΠΈΠ² Ρ‚Π° ΡΠΈΠ»ΡŒΠ½Ρƒ ΠΊΠΎΠΌΠ°Π½Π΄Ρƒ
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  • Β· 82 views Β· 12 applications Β· 4d

    Machine Learning Engineer (Analytics , NLP)

    Full Remote Β· Worldwide Β· Product Β· 1 year of experience Β· English - B1
    Ми ΡˆΡƒΠΊΠ°Ρ”ΠΌΠΎ Machine Learning Engineer (Analytics & NLP), який/яка Π΄ΠΎΠΏΠΎΠΌΠΎΠΆΠ΅ ΠΏΠΎΠ±ΡƒΠ΄ΡƒΠ²Π°Ρ‚ΠΈ Ρ‚Π° ΠΌΠ°ΡΡˆΡ‚Π°Π±ΡƒΠ²Π°Ρ‚ΠΈ ML-Ρ€Ρ–ΡˆΠ΅Π½Π½Ρ для Π°Π½Π°Π»Ρ–Ρ‚ΠΈΠΊΠΈ ΠΎΡΠ²Ρ–Ρ‚Π½ΡŒΠΎΠ³ΠΎ ΠΊΠΎΠ½Ρ‚Π΅Π½Ρ‚Ρƒ Ρ‚Π° ΠΏΠΎΠ²Π΅Π΄Ρ–Π½ΠΊΠΈ користувачів. Роль ΠΏΠ΅Ρ€Π΅Π΄Π±Π°Ρ‡Π°Ρ” Ρ€ΠΎΠ±ΠΎΡ‚Ρƒ Π· ΠΏΠΎΠ²Π½ΠΈΠΌ ML-Ρ†ΠΈΠΊΠ»ΠΎΠΌ β€” Π²Ρ–Π΄ ΠΏΡ–Π΄Π³ΠΎΡ‚ΠΎΠ²ΠΊΠΈ Π΄Π°Π½ΠΈΡ… Π΄ΠΎ ΠΏΡ€ΠΎΠ΄Π°ΠΊΡˆΠ΅Π½Ρƒ Ρ‚Π°...

    Ми ΡˆΡƒΠΊΠ°Ρ”ΠΌΠΎ Machine Learning Engineer (Analytics & NLP), який/яка Π΄ΠΎΠΏΠΎΠΌΠΎΠΆΠ΅ ΠΏΠΎΠ±ΡƒΠ΄ΡƒΠ²Π°Ρ‚ΠΈ Ρ‚Π° ΠΌΠ°ΡΡˆΡ‚Π°Π±ΡƒΠ²Π°Ρ‚ΠΈ ML-Ρ€Ρ–ΡˆΠ΅Π½Π½Ρ для Π°Π½Π°Π»Ρ–Ρ‚ΠΈΠΊΠΈ ΠΎΡΠ²Ρ–Ρ‚Π½ΡŒΠΎΠ³ΠΎ ΠΊΠΎΠ½Ρ‚Π΅Π½Ρ‚Ρƒ Ρ‚Π° ΠΏΠΎΠ²Π΅Π΄Ρ–Π½ΠΊΠΈ користувачів. Роль ΠΏΠ΅Ρ€Π΅Π΄Π±Π°Ρ‡Π°Ρ” Ρ€ΠΎΠ±ΠΎΡ‚Ρƒ Π· ΠΏΠΎΠ²Π½ΠΈΠΌ ML-Ρ†ΠΈΠΊΠ»ΠΎΠΌ β€” Π²Ρ–Π΄ ΠΏΡ–Π΄Π³ΠΎΡ‚ΠΎΠ²ΠΊΠΈ Π΄Π°Π½ΠΈΡ… Π΄ΠΎ ΠΏΡ€ΠΎΠ΄Π°ΠΊΡˆΠ΅Π½Ρƒ Ρ‚Π° Ρ–Π½Ρ‚Π΅Π³Ρ€Π°Ρ†Ρ–Ρ— Π² Π°Π½Π°Π»Ρ–Ρ‚ΠΈΡ‡Π½Ρƒ інфраструктуру.

    Π¦Ρ–Π»ΡŒ посади

    ΠΠ°Π»Π°ΡˆΡ‚ΡƒΠ²Π°Ρ‚ΠΈ, Ρ€ΠΎΠ·Π³ΠΎΡ€Π½ΡƒΡ‚ΠΈ Ρ‚Π° ΠΏΡ–Π΄Ρ‚Ρ€ΠΈΠΌΡƒΠ²Π°Ρ‚ΠΈ ML-ΠΌΠΎΠ΄Π΅Π»Ρ– для Π°Π½Π°Π»Ρ–Ρ‚ΠΈΠΊΠΈ ΠΊΠΎΠ½Ρ‚Π΅Π½Ρ‚Ρƒ Ρ– ΠΏΠΎΠ²Π΅Π΄Ρ–Π½ΠΊΠΈ користувачів, Π·Π°Π±Π΅Π·ΠΏΠ΅Ρ‡ΡƒΡŽΡ‡ΠΈ Ρ—Ρ…Π½ΡŽ ΡΡ‚Π°Π±Ρ–Π»ΡŒΠ½Ρƒ Ρ‚Π° Π΅Ρ„Π΅ΠΊΡ‚ΠΈΠ²Π½Ρƒ Ρ€ΠΎΠ±ΠΎΡ‚Ρƒ Ρƒ ΠΏΡ€ΠΎΠ΄Π°ΠΊΡˆΠ΅Π½Ρ–.

    ΠžΡΠ½ΠΎΠ²Π½Ρ– обов’язки

    • Π ΠΎΠ·Ρ€ΠΎΠ±ΠΊΠ° Ρ‚Π° Π²ΠΏΡ€ΠΎΠ²Π°Π΄ΠΆΠ΅Π½Π½Ρ ML-ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ для Π°Π½Π°Π»Ρ–Π·Ρƒ ΠΎΡΠ²Ρ–Ρ‚Π½ΡŒΠΎΠ³ΠΎ ΠΊΠΎΠ½Ρ‚Π΅Π½Ρ‚Ρƒ (тСксти, сцСнарії, Ρ„Ρ–Π΄Π±Π΅ΠΊ, ΠΌΠ΅Ρ‚Π°Π΄Π°Π½Ρ–).
    • ΠŸΠΎΠ±ΡƒΠ΄ΠΎΠ²Π° Ρ‚Π° ΠΏΡ–Π΄Ρ‚Ρ€ΠΈΠΌΠΊΠ° ΠΏΠΎΠ²Π½ΠΎΠ³ΠΎ ML-pipeline: ΠΏΡ–Π΄Π³ΠΎΡ‚ΠΎΠ²ΠΊΠ° Π΄Π°Π½ΠΈΡ…, embeddings, скоринг, inference Ρƒ ΠΏΡ€ΠΎΠ΄Π°ΠΊΡˆΠ΅Π½Ρ–.
    • БтворСння Ρ‚Π° ΠΏΡ–Π΄Ρ‚Ρ€ΠΈΠΌΠΊΠ° API (REST / WebSocket) для доступу Π΄ΠΎ Π°Π½Π°Π»Ρ–Ρ‚ΠΈΡ‡Π½ΠΈΡ… Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ–Π².
    • ІнтСграція ML-сСрвісів Π· Π°Π½Π°Π»Ρ–Ρ‚ΠΈΡ‡Π½ΠΈΠΌΠΈ сховищами Π΄Π°Π½ΠΈΡ… (PostgreSQL, ClickHouse, Π²Π΅ΠΊΡ‚ΠΎΡ€Π½Ρ– Π±Π°Π·ΠΈ Π΄Π°Π½ΠΈΡ…).
    • ΠšΠΎΠ½Ρ‚Π΅ΠΉΠ½Π΅Ρ€ΠΈΠ·Π°Ρ†Ρ–Ρ, Π΄Π΅ΠΏΠ»ΠΎΠΉ Ρ‚Π° ΠΌΠ°ΡΡˆΡ‚абування ML-сСрвісів Ρƒ Ρ…ΠΌΠ°Ρ€Π½ΠΎΠΌΡƒ сСрСдовищі (Docker).
    • ΠžΠΏΡ‚ΠΈΠΌΡ–Π·Π°Ρ†Ρ–Ρ продуктивності, ΡΡ‚Π°Π±Ρ–Π»ΡŒΠ½ΠΎΡΡ‚Ρ– Ρ‚Π° ΡΠΊΠΎΡΡ‚Ρ– ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ Ρƒ ΠΏΡ€ΠΎΠ΄Π°ΠΊΡˆΠ΅Π½Ρ–.
    • Π£Ρ‡Π°ΡΡ‚ΡŒ Ρƒ Ρ€ΠΎΠ·Π²ΠΈΡ‚ΠΊΡƒ Ρ‚Π° Π²Π΄ΠΎΡΠΊΠΎΠ½Π°Π»Π΅Π½Π½Ρ– Π°Π½Π°Π»Ρ–Ρ‚ΠΈΡ‡Π½ΠΎΡ— Ρ‚Π° ML-Π°Ρ€Ρ…Ρ–Ρ‚Π΅ΠΊΡ‚ΡƒΡ€ΠΈ.

    Ми ΠΎΡ‡Ρ–ΠΊΡƒΡ”ΠΌΠΎ

    • Досвід Ρ€ΠΎΠ±ΠΎΡ‚ΠΈ Π· ML / NLP-модСлями Ρƒ ΠΏΡ€ΠΎΠ΄Π°ΠΊΡˆΠ΅Π½Ρ–.
    • Розуміння ΠΏΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΆΠΈΡ‚Ρ‚Ρ”Π²ΠΎΠ³ΠΎ Ρ†ΠΈΠΊΠ»Ρƒ ML-Ρ€Ρ–ΡˆΠ΅Π½ΡŒ.
    • ΠŸΡ€Π°ΠΊΡ‚ΠΈΡ‡Π½ΠΈΠΉ досвід Ρ€ΠΎΠ±ΠΎΡ‚ΠΈ Π· API, Π±Π°Π·Π°ΠΌΠΈ Π΄Π°Π½ΠΈΡ… Ρ‚Π° ΠΊΠΎΠ½Ρ‚Π΅ΠΉΠ½Π΅Ρ€ΠΈΠ·Π°Ρ†Ρ–Ρ”ΡŽ.
    • БистСмнС мислСння Ρ‚Π° ΠΎΡ€Ρ–Ρ”Π½Ρ‚Π°Ρ†Ρ–ΡŽ Π½Π° ΡΠΊΡ–ΡΡ‚ΡŒ Ρ– ΠΌΠ°ΡΡˆΡ‚Π°Π±ΠΎΠ²Π°Π½Ρ–ΡΡ‚ΡŒ Ρ€Ρ–ΡˆΠ΅Π½ΡŒ.

    Ми ΠΏΡ€ΠΎΠΏΠΎΠ½ΡƒΡ”ΠΌΠΎ

    • Π£Ρ‡Π°ΡΡ‚ΡŒ Ρƒ ΠΏΠΎΠ±ΡƒΠ΄ΠΎΠ²Ρ– складних Π°Π½Π°Π»Ρ–Ρ‚ΠΈΡ‡Π½ΠΈΡ… ML-Ρ€Ρ–ΡˆΠ΅Π½ΡŒ Π· Ρ€Π΅Π°Π»ΡŒΠ½ΠΈΠΌ Π²ΠΏΠ»ΠΈΠ²ΠΎΠΌ Π½Π° ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚.
    • Π ΠΎΠ±ΠΎΡ‚Ρƒ Π· ΡΡƒΡ‡Π°ΡΠ½ΠΈΠΌ ML-стСком Ρ‚Π° Π΄Π°Π½ΠΈΠΌΠΈ.
    • ΠœΠΎΠΆΠ»ΠΈΠ²Ρ–ΡΡ‚ΡŒ Π²ΠΏΠ»ΠΈΠ²Π°Ρ‚ΠΈ Π½Π° Π°Ρ€Ρ…Ρ–Ρ‚Π΅ΠΊΡ‚ΡƒΡ€Π½Ρ– Ρ€Ρ–ΡˆΠ΅Π½Π½Ρ Ρ‚Π° Ρ€ΠΎΠ·Π²ΠΈΡ‚ΠΎΠΊ ML-напрямку.
    • ΠŸΡ€ΠΎΡ„Π΅ΡΡ–ΠΉΠ½ΠΈΠΉ Ρ€ΠΎΠ·Π²ΠΈΡ‚ΠΎΠΊ Ρƒ ΠΊΠΎΠΌΠ°Π½Π΄Ρ– Π· Ρ„окусом Π½Π° ΡΠΊΡ–ΡΡ‚ΡŒ Ρ– Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚.
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  • Β· 150 views Β· 8 applications Β· 4d

    Game Mathematician

    Part-time Β· Full Remote Β· Countries of Europe or Ukraine Β· Product Β· 2 years of experience Β· English - None
    Ми розробляємо ΠΌΠΎΠ±Ρ–Π»ΡŒΠ½Ρ– слотові Ρ–Π³Ρ€ΠΈ Π· Π°ΠΊΡ†Π΅Π½Ρ‚ΠΎΠΌ Π½Π° якісну ΠΌΠ°Ρ‚Π΅ΠΌΠ°Ρ‚ΠΈΠΊΡƒ, ΡΡ‚Π°Π±Ρ–Π»ΡŒΠ½Ρ– ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊΠΈ Ρ‚Π° ΠΏΡ€ΠΎΠ΄ΡƒΠΌΠ°Π½Ρƒ Ρ–Π³Ρ€ΠΎΠ²Ρƒ Π»ΠΎΠ³Ρ–ΠΊΡƒ. Наш фокус - створСння збалансованих ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ, Ρ‰ΠΎ ΠΏΠΎΡ”Π΄Π½ΡƒΡŽΡ‚ΡŒ бізнСс-Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΈ Ρ‚Π° ΠΊΠΎΠΌΡ„ΠΎΡ€Ρ‚ гравця. Π¨ΡƒΠΊΠ°Ρ”ΠΌΠΎ Game Mathematician’a, який...

    Ми розробляємо ΠΌΠΎΠ±Ρ–Π»ΡŒΠ½Ρ– слотові Ρ–Π³Ρ€ΠΈ Π· Π°ΠΊΡ†Π΅Π½Ρ‚ΠΎΠΌ Π½Π° якісну ΠΌΠ°Ρ‚Π΅ΠΌΠ°Ρ‚ΠΈΠΊΡƒ, ΡΡ‚Π°Π±Ρ–Π»ΡŒΠ½Ρ– ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊΠΈ Ρ‚Π° ΠΏΡ€ΠΎΠ΄ΡƒΠΌΠ°Π½Ρƒ Ρ–Π³Ρ€ΠΎΠ²Ρƒ Π»ΠΎΠ³Ρ–ΠΊΡƒ. Наш фокус - створСння збалансованих ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ, Ρ‰ΠΎ ΠΏΠΎΡ”Π΄Π½ΡƒΡŽΡ‚ΡŒ бізнСс-Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΈ Ρ‚Π° ΠΊΠΎΠΌΡ„ΠΎΡ€Ρ‚ гравця.

     

    Π¨ΡƒΠΊΠ°Ρ”ΠΌΠΎ Game Mathematician’a, який Π²Ρ–Π΄ΠΏΠΎΠ²Ρ–Π΄Π°Ρ‚ΠΈΠΌΠ΅ Π·Π° Ρ€ΠΎΠ·Ρ€ΠΎΠ±ΠΊΡƒ, модСлювання Ρ‚Π° ΠΎΠΏΡ‚ΠΈΠΌΡ–Π·Π°Ρ†Ρ–ΡŽ ΠΌΠ°Ρ‚Π΅ΠΌΠ°Ρ‚ΠΈΡ‡Π½ΠΈΡ… ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ слотів. 
    Нам ΠΏΠΎΡ‚Ρ€Ρ–Π±Π΅Π½ Ρ„Π°Ρ…Ρ–Π²Π΅Ρ†ΡŒ, який Π³Π»ΠΈΠ±ΠΎΠΊΠΎ Ρ€ΠΎΠ·ΡƒΠΌΡ–Ρ” ΠΌΠ΅Ρ…Π°Π½Ρ–ΠΊΠΈ слотів, Π°Π½Π°Π»Ρ–Ρ‚ΠΈΠΊΡƒ Ρ‚Π° ΠΌΠΎΠΆΠ΅ Π·Π°Π±Π΅Π·ΠΏΠ΅Ρ‡ΠΈΡ‚ΠΈ ΠΌΠ°Ρ‚Π΅ΠΌΠ°Ρ‚ΠΈΡ‡Π½Ρƒ Ρ†Ρ–Π»Ρ–ΡΠ½Ρ–ΡΡ‚ΡŒ ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚Ρƒ Π½Π° всіх Π΅Ρ‚Π°ΠΏΠ°Ρ… Ρ€ΠΎΠ·Ρ€ΠΎΠ±ΠΊΠΈ.
     

    Π©ΠΎ Ρ‚ΠΈ Π±ΡƒΠ΄Π΅Ρˆ Ρ€ΠΎΠ±ΠΈΡ‚ΠΈ:
    - Розробляти Ρ–Π³Ρ€ΠΎΠ²Ρƒ ΠΌΠ°Ρ‚Π΅ΠΌΠ°Ρ‚ΠΈΠΊΡƒ для слотів: RTP, Π²ΠΎΠ»Π°Ρ‚ΠΈΠ»ΡŒΠ½Ρ–ΡΡ‚ΡŒ, hit frequency, ΠΊΡ€ΠΈΠ²Ρ– Π²ΠΈΠΏΠ»Π°Ρ‚, бонусні систСми.
    - Π‘Ρ‚Π²ΠΎΡ€ΡŽΠ²Π°Ρ‚ΠΈ ΠΌΠ°Ρ‚Π΅ΠΌΠ°Ρ‚ΠΈΡ‡Π½Ρ– ΠΌΠΎΠ΄Π΅Π»Ρ– Ρ‚Π° симуляції для балансування слотів.
    - Аналізувати Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΈ симуляцій Ρ‚Π° ΠΎΠΏΡ‚ΠΈΠΌΡ–Π·ΡƒΠ²Π°Ρ‚ΠΈ payout tables Ρ– free spins.
    - Π‘ΠΏΡ–Π²ΠΏΡ€Π°Ρ†ΡŽΠ²Π°Ρ‚ΠΈ Π· Π³Π΅ΠΉΠΌ-Π΄ΠΈΠ·Π°ΠΉΠ½Π΅Ρ€Π°ΠΌΠΈ, ΠΏΡ€ΠΎΠ΄Π°ΠΊΡ‚-ΠΌΠ΅Π½Π΅Π΄ΠΆΠ΅Ρ€Π°ΠΌΠΈ Ρ‚Π° Ρ€ΠΎΠ·Ρ€ΠΎΠ±Π½ΠΈΠΊΠ°ΠΌΠΈ для створСння цілісного Ρ–Π³Ρ€ΠΎΠ²ΠΎΠ³ΠΎ досвіду.
    - Вносити Ρ–Π΄Π΅Ρ— Ρ‰ΠΎΠ΄ΠΎ Π½ΠΎΠ²ΠΈΡ… ΠΌΠ΅Ρ…Π°Π½Ρ–ΠΊ Ρ‚Π° ΠΏΠΎΠΊΡ€Π°Ρ‰Π΅Π½ΡŒ для підвищСння залучСності Π³Ρ€Π°Π²Ρ†Ρ–Π².
    - ΠŸΠ΅Ρ€Π΅Π²Ρ–Ρ€ΡΡ‚ΠΈ Ρ‚Π° Π΄ΠΎΠΊΡƒΠΌΠ΅Π½Ρ‚ΡƒΠ²Π°Ρ‚ΠΈ ΠΏΡ€Π°Π²ΠΈΠ»Π° слотів Ρ– ΠΌΠ°Ρ‚Π΅ΠΌΠ°Ρ‚ΠΈΡ‡Π½Ρ– ΠΌΠΎΠ΄Π΅Π»Ρ–.

     

    Π©ΠΎ ΠΌΠΈ ΠΎΡ‡Ρ–ΠΊΡƒΡ”ΠΌΠΎ Π²Ρ–Π΄ Ρ‚Π΅Π±Π΅:
    - Π’ΠΈΡ‰Π° освіта Π² ΠΌΠ°Ρ‚Π΅ΠΌΠ°Ρ‚ΠΈΡ†Ρ–, статистиці Π°Π±ΠΎ суміТних дисциплінах.
    - 2+ Ρ€ΠΎΠΊΠΈ досвіду Ρ€ΠΎΠ±ΠΎΡ‚ΠΈ Ρ–Π³Ρ€ΠΎΠ²ΠΈΠΌ ΠΌΠ°Ρ‚Π΅ΠΌΠ°Ρ‚ΠΈΠΊΠΎΠΌ Π°Π±ΠΎ Π½Π° суміТній ΠΏΠΎΠ·ΠΈΡ†Ρ–Ρ— Ρƒ Π³Π΅ΠΌΠ±Π»Ρ–Π½Π³Ρƒ / GameDev.
    - Π“Π»ΠΈΠ±ΠΎΠΊΠ΅ розуміння слот-ΠΌΠ΅Ρ…Π°Π½Ρ–ΠΊ, RTP, Π²ΠΎΠ»Π°Ρ‚ΠΈΠ»ΡŒΠ½ΠΎΡΡ‚Ρ– Ρ‚Π° бонусних систСм.
    - Володіння Excel / Google Sheets; знання Python для симуляцій β€” ΠΏΠ΅Ρ€Π΅Π²Π°Π³Π°.
    - АналітичнС мислСння, ΡƒΠ²Π°ΠΆΠ½Ρ–ΡΡ‚ΡŒ Π΄ΠΎ Π΄Π΅Ρ‚Π°Π»Π΅ΠΉ, Π·Π΄Π°Ρ‚Π½Ρ–ΡΡ‚ΡŒ Π²ΠΈΡ€Ρ–ΡˆΡƒΠ²Π°Ρ‚ΠΈ складні ΠΌΠ°Ρ‚Π΅ΠΌΠ°Ρ‚ΠΈΡ‡Π½Ρ– Π·Π°Π΄Π°Ρ‡Ρ–.
    - ΠšΡ€Π΅Π°Ρ‚ΠΈΠ²Π½Ρ–ΡΡ‚ΡŒ Ρ‚Π° Π·Π΄Π°Ρ‚Π½Ρ–ΡΡ‚ΡŒ Π³Π΅Π½Π΅Ρ€ΡƒΠ²Π°Ρ‚ΠΈ Π½ΠΎΠ²Ρ– Ρ–Π΄Π΅Ρ—.
    - Командна Ρ€ΠΎΠ±ΠΎΡ‚Π° Ρ‚Π° ΠΊΠΎΠΌΡƒΠ½Ρ–ΠΊΠ°Ρ†Ρ–ΠΉΠ½Ρ– Π½Π°Π²ΠΈΡ‡ΠΊΠΈ.

     

    Π‘ΡƒΠ΄Π΅ плюсом:

    - Досвід Π³Ρ€ΠΈ Π² слоти Π°Π±ΠΎ розуміння Ρ–Π³Ρ€ΠΎΠ²ΠΎΡ— ΠΏΠΎΠ²Π΅Π΄Ρ–Π½ΠΊΠΈ Π³Ρ€Π°Π²Ρ†Ρ–Π².

     

    Ми ΠΏΡ€ΠΎΠΏΠΎΠ½ΡƒΡ”ΠΌΠΎ:

    πŸ”Ή Повна Π³Π½ΡƒΡ‡ΠΊΡ–ΡΡ‚ΡŒ: ΠΏΡ€Π°Ρ†ΡŽΠΉ Π· Π΄ΠΎΠΌΡƒ, офісу Π² ΠšΠΈΡ”Π²Ρ– Π°Π±ΠΎ Π’Π°Ρ€ΡˆΠ°Π²Ρ– β€” як Π·Ρ€ΡƒΡ‡Π½ΠΎ Ρ‚ΠΎΠ±Ρ–.
    πŸ”Ή Π“Π½ΡƒΡ‡ΠΊΠΈΠΉ Π³Ρ€Π°Ρ„Ρ–ΠΊ β€” ΠΏΡ€Π°Ρ†ΡŽΡ”Ρˆ Ρƒ своєму Ρ€ΠΈΡ‚ΠΌΡ–.
    πŸ”Ή 27 ΠΎΠΏΠ»Π°Ρ‡ΡƒΠ²Π°Π½ΠΈΡ… Ρ€ΠΎΠ±ΠΎΡ‡ΠΈΡ… Π΄Π½Ρ–Π² Π²Ρ–Π΄ΠΏΠΎΡ‡ΠΈΠ½ΠΊΡƒ Π½Π° Ρ€Ρ–ΠΊ.
    πŸ”Ή ΠšΠΎΡ€ΠΏΠΎΡ€Π°Ρ‚ΠΈΠ²Π½Ρ– активності Ρ‚Π° Team Challenges, які Ρ€Π΅Π°Π»ΡŒΠ½ΠΎ Ρ†Ρ–ΠΊΠ°Π²Ρ–.
    πŸ”Ή ΠŸΡ€ΠΎΠ·ΠΎΡ€Ρƒ ΠΊΠΎΠΌΡƒΠ½Ρ–ΠΊΠ°Ρ†Ρ–ΡŽ, ΠΌΡ–Π½Ρ–ΠΌΡƒΠΌ Ρ„ΠΎΡ€ΠΌΠ°Π»ΡŒΠ½ΠΎΡΡ‚Π΅ΠΉ Ρ– справТній Π²ΠΏΠ»ΠΈΠ² Π½Π° ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚.

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