Jobs
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Β· 91 views Β· 8 applications Β· 25d
Data Scientist / Quantitative Researcher
Full Remote Β· Worldwide Β· Product Β· 3 years of experienceWe are Onicore β fintech company specializing in developing products for cryptocurrency operations. Registered in the USA, our company is powered by a talented Ukrainian team, working across the globe. Now weβre on the hunt for a specialist who will...We are Onicore β fintech company specializing in developing products for cryptocurrency operations.
Registered in the USA, our company is powered by a talented Ukrainian team, working across the globe.
π Now weβre on the hunt for a specialist who will drive the project of algorithmic trading.
Your skills:
- 3+ years of experience in Data Science;
- excellent command of Python, understanding of the principles of OOP;
- deep knowledge in linear algebra, probability theory and mathematical statistics;
- data collection and preprocessing (numpy, pandas, scikit-learn,ta-lib);
- experience working with all types of classical machine learning (Supervised Learning, Unsupervised Learning, Reinforcement Learning);
- development experience and deep understanding of the principles of the architectures: RNN, LSTM, GRU, CNN, Transformer in the field of analysis and prediction of time sequences (time series predictions);
- confident use of both high-level and low-level APIs for TensorFlow (writing custom training loops, custom metrics & loss_functions).
Knowledge of PyTorch is welcome;
- the ability to visualize the learning process using TensorBoard;
- boosting neural networks (Distributed XGBoost/LightGBM);
- visualization of results (matplotlib, seaborn).
Would be a plus:
- experience with currency markets;
- PhD degree in the field of data science / machine learning.
Your responsibilities:
β solving algorithmic trading problems: regression/autoregression, classification of timeseries/financial series, working with cryptocurrency quotes.
Whatβs in it for you?π₯ Health first: Comprehensive medical insurance.
π€ Keep growing: We cover courses, conferences, training sessions, and workshops.
πͺ Stay active mentally and physically : Sports / hobby / personal psychologist to fuel yourself.
πΌ We've got your back: Access to legal assistance when you need it.
π§ββοΈ Inspiring vibes: Join a motivated, goal-oriented team that supports each other.
π§βπ» Make a difference: Have a direct impact on shaping and growing the product.
π» Work smarter: Corporate laptops to help you do your best work.
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Join our team and help us level up! -
Β· 49 views Β· 8 applications Β· 17d
ML Computer Vision Engineer (machine learning engineer) to $5500
Full Remote Β· Countries of Europe or Ukraine Β· Product Β· 3 years of experience Β· Upper-IntermediateWe are elai.io, an innovative AI-driven startup specializing in video generation. Recently acquired by Panopto β a leader in interactive video solutions β weβre now part of a growing team of around 200 professionals focused on advancing learning through...We are elai.io, an innovative AI-driven startup specializing in video generation.
Recently acquired by Panopto β a leader in interactive video solutions β weβre now part of a growing team of around 200 professionals focused on advancing learning through powerful, interactive video technology.
What Youβll Do as a Computer Vision Engineer:
1. Research, design and implement appropriate computer vision algorithms for the main product (generating video);
2. Research, find and use appropriate datasets;
3. Define quality metrics, run machine learning experiments, and analyse results;
4. Be on top of industry trends, research and propose new technologies.Our ideal candidate has:
1. post-doc, PhD or Masterβs degree in Computer Science or similar field;
2. proven experience as a Machine Learning Engineer or similar role;
3. deep knowledge of maths, probability, linear algebra, computer vision and algorithms;
4. 3+ years of experience with Python;
5. 2+ years of experience with PyTorch (TensorFlow experience is a plus);
6. familiarity with state of the art networks, architectures and models in computer vision area (like UNet, Resnet, GAN, Transformers, Diffusion Models, NeRF, etc.);
7. experience in deploying machine learning algorithms in production Base knowledge: git, docker, linux, bash;
8. experience in 3d graphics or game development would be a great plus.We offer:
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1. Opportunity to work with a highly qualified international and friendly team
2. Decent and timely payment fixed in USD
3. The most flexible work schedule, including remote work
4. Unlimited time off -
Β· 26 views Β· 4 applications Β· 26d
Game Mathematician
Full Remote Β· EU Β· Product Β· 3 years of experience Β· Upper-IntermediateWe are looking for an experienced and driven Game Mathematician to join our client's Game Studio. What You Will Do: The Game Mathematician will work closely with the Senior Management and Game Design Teams to develop concepts and statistical documents...We are looking for an experienced and driven Game Mathematician to join our client's Game Studio.
What You Will Do:
The Game Mathematician will work closely with the Senior Management and Game Design Teams to develop concepts and statistical documents for unique gambling games. Such games may be traditional video slot games, table games or multiplayer casual-mobile style games that will contain a gambling element. This role will be working collaboratively with the other disciplines of the Games Inc team to ensure that slot games are not only entertaining but also fair and financially viable for the casino. Playing a vital role in balancing the interests of players and the casino while complying with industry regulations. A Game Mathematician will leverage their passion for playing various genres of games as well as developing their expert knowledge of player psychology to help devise unique and engaging Game content. You will help to create and maintain comprehensive documentation, create detailed experience flowcharts, wireframes, and detailed moment-to-moment gameplay experiences with distinct clarity. Additionally, the Game Mathematician will assist in the brainstorming, creation, and review of new game features for the Companyβs future product line.
Summary of Responsibilities:
- Collaborate with the slots team to assist in the design and creation of unique gambling games;
- Use mathematical models to create game rules, pay tables and volatility settings;
- Consistently demonstrate an increasing knowledge of the gaming market and an empathy for all player types;
- Help with the Mathematical Analysis of existing and new games to analyse the probability of winning and the expected return to the player for each game. Calculate the house edge and volatility of the games to ensure profitability for the casino. Optimising game parameters to achieve desired player engagement and revenue targets;
- Ensure the integrity of the random number generator (RNG) used in slot games to guarantee fair and unpredictable outcomes;
- Stay up-to-date with gaming regulations and ensure that all games meet legal requirements and standards;
- Collect and analyse data from slot games to make data-driven decisions for game improvements;
- Monitor and interpret player behaviour and game performance to identify areas for enhancement;
- Conduct playtesting and quality assurance to identify and address issues with game mechanics or payouts;
- Ensure that the game provides a satisfying player experience while adhering to the intended mathematical model;
- Maintain detailed documentation of game specifications, mathematical models, and testing results.
What you'll need to have:
- Ability to build a greater knowledge of real money casino games, player psychology and the ability to help in the creation of ideas and designs for specific markets and players;
- Understanding of the basic mathematical fundamentals of gambling games;
- Passion for games and mobile gaming, including an understanding of mobile gambling products and trends;
- Excellent written and verbal communication skills;
- Familiarity with software tools used for game development and analysis;
- Ability to work in a collaborative, multi-team environment, including product managers, engineers, artists, marketing, and support service personnel;
- Good organisational, problem-solving and interpersonal skills.
Other Duties and Responsibilities:
- Participation in team brainstorming;
- Contributing to the review of other designersβ games and concepts;
- Contributing to the evolution of the teamβs process and best practices;
- Market and data analysis of current trends;
- Assist with strategising future product plans and lines.
Qualifications:
- Experience designing games, including math or similar products that come into being through various channels, including original concepts, competitively relevant products, and business or market needs;
- Knowledge or experience with various game development pipelines & methodologies;
- Involvement within teams developing products is highly recommended;
- Knowledge about games and/or the casino industry, including the current market landscape
- Experience working with multiple disciplines, including artists, mathematicians, software developers, etc., in creating games or products.
The company offers:
- Time off: 25 days of annual leave per year are available;
- Sick Leave & Public Holidays: Entitlement includes UK public holidays and statutory sick leave;
- Flexible Working Hours: Flexible scheduling is supported to allow effective time management;
- Remote work: Remote work is a great benefit and offers flexibility, helps improve work-life balance, and supports productivity across different locations;
- Referral program: Great people know great people. Help grow the team by referring talented individuals who would be a strong fit!;
- Employee Education Initiative: Twice a year, the company provides an opportunity to explore new interests outside of daily work, fostering curiosity and personal development;
- Professional Development: Courses, conferences, workshops, and training programs that benefit both the employee and the company may be fully funded.
If you find this opportunity right for you, don't hesitate to apply or get in touch with us if you have any questions!
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Β· 42 views Β· 0 applications Β· 1d
Data Scientist (NLP + LLMs)
Full Remote Β· Ukraine Β· 3 years of experienceWe are looking for a Data Scientist (NLP & LLMs) to join our team and work on the development of AI-powered solutions using modern NLP, Deep Learning, and multi-agent systems. Project description: We are building AI-driven fintech solutions using...We are looking for a Data Scientist (NLP & LLMs) to join our team and work on the development of AI-powered solutions using modern NLP, Deep Learning, and multi-agent systems.
Project description:
We are building AI-driven fintech solutions using LLMs, RAG, and autonomous agents to automate compliance, contracts, and risk analysis β streamlining workflows and boosting decision-making with intelligent insights.
Requirements:
- 3+ years of experience as a Data Scientist or in a related role
- Strong knowledge of Deep Learning and Natural Language Processing (NLP)
- Hands-on experience with Large Language Models (LLMs), RAG, and multi-agent systems
- Proficiency in Python and relevant libraries such as PyTorch, TensorFlow, Transformers
- Solid foundation in Computer Science, Mathematics, or Statistics (Bachelorβs or higher)
Responsibilities:
- Develop NLP tools for automated contract generation, review, and compliance analysis
- Build AI agents to generate and update legal documents based on input and regulations
- Implement systems for legal clause classification and risk/highlight detection
- Create pipelines for legal request analysis and decision support (e.g., asset seizure)
- Collaborate with legal teams to fine-tune models and balance AI vs rule-based outputs
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Β· 21 views Β· 2 applications Β· 1d
Machine Learning Engineer
Full Remote Β· Worldwide Β· 3 years of experience Β· Upper-IntermediateWe are toogeza, a Ukrainian recruiting company that is focused on hiring talents and building teams for tech startups worldwide. People make a difference in the big game, we may help to find the right ones. Currently, we are looking for a ML Engineer for...We are toogeza, a Ukrainian recruiting company that is focused on hiring talents and building teams for tech startups worldwide. People make a difference in the big game, we may help to find the right ones.
Currently, we are looking for a ML Engineer for The Playa
Location: Remote
Job Type: Full-Time
About our client:The Playa helps iGaming platforms boost engagement, revenue, and ROMI by up to 25% by understanding and profiling player behavior, detecting positive and suspicious activities, and delivering tailored recommendations to each player.
More information about The Playa solutions can be found on www.theplaya.solutions
Role Overview:We are looking for an experienced Machine Learning Engineer to build, deploy, and maintain machine learning solutions that are ready for production. In this role, you will solve challenging problems, develop recommendation systems, and improve machine learning workflows to deliver real-world impact.
Responsibilities:- Design, create, and deploy machine learning models for regression, classification, and clustering.
- Develop and improve recommendation systems to meet business needs.
- Write clean, efficient, and scalable code in Python.
- Use AWS tools and services to build reliable, cloud-based machine learning solutions.
- Manage workflows with Airflow and handle containerized environments using Docker.
- Write and optimize SQL queries for data extraction, transformation, and analysis.
- Work with the team to follow best practices in version control (Git) and testing.
- Apply basic MLOps practices to improve machine learning processes.
Requirements:
Must-Have Skills:- At least 3 years of hands-on experience in machine learning and data science.
- Strong skills in Python, SQL, and Git.
- Hands-on experience with cloud platforms (preferably AWS), workflow orchestration using Airflow, and containerization with Docker.
- Good understanding of machine learning techniques, such as regression, classification, and clustering.
- Proven ability to deliver robust, scalable, and production-grade code.
- English proficiency at an upper-intermediate level or higher.
Nice-to-Have Skills:- Experience in building and deploying recommendation systems.
- Familiarity with testing and MLOps practices.
- A Masterβs degree in Computer Science, or a related field.
Benefits:- Education budget of $600 per year provided
- Professional English courses
- Medical Insurance
Interview process:- Recruiting Interview β (45 mins)
- Tech + Live Coding (60 mins)
- ML Design + Behavioral (60 mins)
- Cultural Fit interview β (60 mins)
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Thanks for your interest! In the case of your application, we will review it within 5 working days. If it meets the job requirements, we will arrange a call and will be happy to get to know each other better. Otherwise, weβd love to stay in touch waiting for other opportunities to become available. -
Β· 40 views Β· 4 applications Β· 29d
AI Data Architect β Agentic AI Platform for BFSI
Full Remote Β· Countries of Europe or Ukraine Β· 3 years of experience Β· Upper-IntermediateWe're seeking a talented Data Architect to join our innovative startup, developing Guppy (GenAI Unified Platform for Performance and Yield)βa groundbreaking platform designed specifically for Banking and Financial Services (BFSI) software engineering. At...We're seeking a talented Data Architect to join our innovative startup, developing Guppy (GenAI Unified Platform for Performance and Yield)βa groundbreaking platform designed specifically for Banking and Financial Services (BFSI) software engineering.
At Guppy, we leverage cutting-edge Agentic AI to significantly improve software development and deployment processes. Our platform meets the rigorous security, compliance, and performance standards demanded by regulated financial environments, offering AI-powered agents specialized in:
- Business Analyst Agents: managing requirements, documentation, and analysis.
- PMO Agents: optimizing project governance, coordination, and operational excellence.
We're growing fast, and now we're looking for an experienced Data Architect with deep AI expertise to join our core team, shaping Guppy's foundational data infrastructure and agent memory frameworks.
π― Your Mission at Guppy
You'll play a pivotal role in designing and implementing data infrastructure tailored for BFSI software engineering needs. Youβll directly collaborate with our developers to:
- Architect and integrate scalable data solutions within the Eliza AI framework.
- Develop sophisticated vector embedding systems for semantic knowledge management.
- Design multi-modal memory structures supporting episodic and semantic AI agent memory.
- Create efficient Retrieval-Augmented Generation (RAG) pipelines integrated with vector databases.
- Build advanced knowledge graph structures to track and link project entities and artifacts.
- Implement secure data partitions compliant with stringent financial industry standards.
- Develop APIs enabling seamless bidirectional integrations with enterprise tools (e.g., JIRA, Project Server).
- Establish robust observability systems for continuous monitoring of AI memory and retrieval performance.
π οΈ Technical Skills Required
- Solid expertise with the Eliza framework and agent coordination functionalities.
- Proven hands-on experience with vector databases (e.g., Pinecone, Weaviate, Milvus, Chroma).
- Practical knowledge of embedding models (OpenAI, Cohere, or similar open-source alternatives).
- Deep understanding of LangChain/LlamaIndex for AI agent memory and integration.
- Demonstrated experience in developing and scaling knowledge graph architectures.
- Strong proficiency in building semantic search systems and efficient RAG architectures.
- Experience managing Model Control Plane (MCP) for orchestration of LLMs and enterprise integrations.
- Advanced skills in Python, including async programming patterns and API design.
π What You Bring (Soft Skills)
- Ability to thrive and deliver in a dynamic, fast-paced startup environment.
- Strong analytical thinking and comfort tackling complex technical challenges.
- Excellent communication skills, capable of clearly articulating complex solutions to diverse stakeholders.
- Comfortable directly collaborating with founders and cross-functional teams.
ποΈ Why Join Guppy?
- Cutting-edge technology: Contribute to a sophisticated AI platform with real-world BFSI impact.
- Innovative environment: Engage in rapid iteration cycles and direct founder collaboration.
- Impact and ownership: Your contributions will directly shape our strategic growth and technical direction.
- Growth Potential: High likelihood of continued involvement and professional advancement.
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Β· 30 views Β· 1 application Β· 24d
Senior Machine Learning Ops Engineer
Full Remote Β· Ukraine, Poland Β· 3.5 years of experience Β· Upper-IntermediateDescription Who is our client: Our client is a global data products and technology company. They are on a mission to transform marketing by building the fastest, most connected data platform that bridges marketing strategy to scaled activation. They work...Description
Who is our client:
Our client is a global data products and technology company. They are on a mission to transform marketing by building the fastest, most connected data platform that bridges marketing strategy to scaled activation.
They work with agencies and clients to transform the value of data by bringing together technology, data and analytics capabilities. Delivering this through the AI-enabled media and data platform for the next era of advertising.
The client is endlessly curious. Their team of thinkers, builders, creators and problem solvers are over 1,000 strong, across 20 markets around the world. Our clientβs culture is based on mutual trust, sharing, building, and learning together. They value simplicity, maintainability, automation, and metrics.About this role:
Clientβs team consists of 100+ engineers, designers, data scientists, implementation, and product people, working in small inter-disciplinary teams closely with creative agencies, media agencies, and with our customers, to develop and scale our leading digital advertising optimization suite that delivers amazing outcomes for brands and audiences.
Clientβs platforms are built with Python, React, and Clojure, are deployed using CI/CD, heavily exploit automation, and run on AWS, GCP, k8s, Snowflake, BigQuery, and more. They serve 9 petabytes and 77 billion objects annually, optimize thousands of campaigns to maximise ROI, and deliver 20 billion ad impressions across the globe. Youβll play a leading role in significantly scaling this further.
As clientβs first Machine Learning Operations (MLOps) Engineer on the team, you will play a pivotal role in bridging the gap between platform engineering, data science, and software engineering, building systems that drive the deployment, monitoring, and scalability of machine learning models. You will design and implement pipelines, automate workflows, and optimise model performance in training and production environments. Youβll lead the creation of process, implementation of tools, and creation of solutions mature how we integrate machine learning solutions into our production systems, while maintaining reliability, security, and efficiency. Youβll additionally play a leading role in driving continuous improvement in model lifecycle management, from development to deployment and monitoring.Requirements
Technical Skills:
β’ Proficiency in Python for ML development; familiarity with additional languages like Clojure is a plus.
β’ Expertise in cloud platforms (AWS, GCP) and data warehouses like Snowflake or BigQuery.
β’ Strong knowledge of MLOps frameworks (e.g., Kubeflow, MLflow) and DevOps tools (e.g., Jenkins, GitLab, Flux)
β’ Experience with containerization (Docker) and orchestration (Kubernetes)
β’ Experience with infrastructure-as-code tools like Terraform
Machine Learning Knowledge:
β’ Solid understanding of machine learning principles, including model evaluation, explainability, and retraining workflows.
β’ Hands-on experience with ML frameworks such as TensorFlow or PyTorch
Big Data Handling:
β’ Proficiency in SQL/NoSQL databases and distributed computing systems like Dataprov, EMR, Spark, Hadoop
Soft Skills:
β’ Strong communication skills to collaborate across multidisciplinary teams.
β’ Problem-solving mindset with the ability to work in agile environments
Experience:
β’ At least 4+ years in platform, software, or MLOps engineering roles
β’ Proven track record of deploying scalable ML solutions in production environmentsJob responsibilities
Model Deployment and Operations:
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β’ Deploy, monitor, and maintain machine learning models in production environments.
β’ Automate model training, retraining, versioning, and governance processes.
β’ Monitor model performance, detect drift, and ensure scalability and reliability of ML workflows
Infrastructure and Pipeline Management:
β’ Design and implement scalable MLOps pipelines for data ingestion, transformation, and model deployment.
β’ Build infrastructure-as-code solutions using tools like Terraform to manage cloud environments (AWS, GCP)
Collaboration with Teams:
β’ Work closely with data scientists to operationalize machine learning models.
β’ Collaborate with software engineers to integrate ML systems into broader platforms
Cloud and Big Data Expertise:
β’ Utilize cloud services from AWS, GCP, and Snowflake for scalable data storage and processing.
DevOps Integration:
β’ Implement CI/CD pipelines and automations to streamline ML model deployment.
β’ Use containerization tools like Docker and orchestration platforms like Kubernetes for scalable deployments
β’ Use Observability platforms to monitor pipeline and operational health of model production, delivery and execution -
Β· 54 views Β· 14 applications Β· 16d
Senior Parsing and Data Extraction Engineer
Full Remote Β· Worldwide Β· 3 years of experienceAltss is the fastest-growing, AI-driven investor intelligence platform for alternative asset classes. We extract and structure data on LPs, funds, deals, and key people globally, at a scale and depth unmatched in the industry. What You'll Do Build...Altss is the fastest-growing, AI-driven investor intelligence platform for alternative asset classes. We extract and structure data on LPs, funds, deals, and key people globally, at a scale and depth unmatched in the industry.
What You'll Do
- Build advanced parsers for large-scale, real-time data extraction from diverse sources: websites, PDFs, filings, news, databases, LinkedIn, and more.
- Architect robust, resilient scraping systems capable of bypassing sophisticated anti-bot and geo-blocking measures.
- Develop and deploy entity resolution algorithms to link extracted data across sources (e.g., people, firms, deals).
- Leverage OSINT methodologies to uncover βhiddenβ data and extract insights not available via standard APIs or databases.
- Collaborate with LLM/NLP engineers to automate structuring, cleaning, and validation of parsed data at scale.
- Continuously monitor, QA, and improve pipelines for speed, accuracy, and reliability.
Mentor and lead junior team members (if desired), helping set best practices and high engineering standards.
Who You Are
- Proven experience building industrial-grade parsing/scraping infrastructureβhandling millions of records and high data velocity.
- Expert in Python (Scrapy, Playwright, Selenium, Requests, BeautifulSoup, etc.), or similar modern scraping stacks.
- Hands-on with headless browsers, proxies, captcha-solving, geo-rotation, and anti-bot techniques.
- Deep understanding of HTML/XML/JSON structure, regex, and automated data cleaning.
- Experience with data lakes/warehousing (PostgreSQL, ClickHouse, or similar), and orchestrating ETL/ELT pipelines.
- Knowledge of OSINT, data enrichment, and cross-entity resolution a major plus.
- Familiar with LLM/NLP workflows for data extraction/normalization is a strong plus.
Highly autonomous, outcome-oriented, and able to move fast in a lean, globally distributed team.
Bonus Points For
- Prior work on investor, finance, or B2B datasets.
- Contributions to open-source scraping, data extraction, or OSINT tools.
- Strong background in security, privacy, or compliance in data collection.
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Β· 105 views Β· 3 applications Β· 4d
Machine Learning Engineer / Computer Vision
Full Remote Β· Countries of Europe or Ukraine Β· 3 years of experience Β· Upper-IntermediateAIMPROSOFT β Machine Learning Engineer Opportunity! Aimprosoft, a fast-growing outsourcing IT company, is expanding its staff and is looking to hire a Middle Machine Learning Engineer to work on AI SDLC and companyβs projects. About the role: ...
πAIMPROSOFT β Machine Learning Engineer Opportunity!
Aimprosoft, a fast-growing outsourcing IT company, is expanding its staff and is looking to hire a Middle Machine Learning Engineer to work on AI SDLC and companyβs projects.π―About the role:
In this role, you will be responsible for contribute to the companyβs AI SDLC initiatives, integration of AI tools, and delivery of internal knowledge-sharing sessions. The role involves hands-on work with Computer Vision and AI Agent systems (RAG pipelines), as well as participation in client projects on an outsourced basis. The ideal candidate brings a strong product mindset, is proactive in adopting and implementing state-of-the-art AI technologies, and is keen to mentor others and promote a culture of continuous learning and technical excellence.
π₯What We Need From You:- 3+ years as a Machine Learning Engineer, Data Scientist or AI Engineer
- Πxperience training and deploying Computer Vision models
- Proficient in Python
- Extensive experience with PyTorch, OpenCV, PIL/Pillow, and torchvision
- Proven ability to train, fine-tune, and optimize neural networks on large-scale image datasets
- Experience with model optimization techniques (ONNX conversion, quantization, pruning)
- Proactive in sharing knowledge and fostering a culture of learning
English proficiency at B2 (Upper-Intermediate) level or higher.
AI Agents & RAG Systems:
- Practical experience building and deploying Retrieval-Augmented Generation (RAG) systems
- Proficiency with vector databases and embedding techniques (Qdrant, Milvus)
- Strong knowledge of Large Language Models (LLMs) integration and tuning
- Hands-on experience with LangChain, Haystack, LlamaIndex, or similar agent frameworks
Knowledge of prompt engineering and chain-of-thought reasoning techniques
Infrastructure & Deployment:
- Proficiency with Flask/FastAPI for ML model serving and API development
- Hands-on experience with Docker for containerization
Proficient in Git for version control
Nice to Have:
- Experience with Transformers library and Hugging Face ecosystem
- Knowledge of multimodal AI systems combining vision and language models
- Familiarity with advanced RAG techniques (hybrid search, re-ranking, query expansion)
- Experience with agent memory systems and persistent context management
- Familiarity with MLOps tools (MLflow, Weights & Biases)
- Familiarity with model serving frameworks (TorchServe, BentoML, vLLM)
- Experience deploying ML models and applications on AWS infrastructure.
πΌWhat We Offer:
- Opportunity to work with AI
- A competitive salary that appreciates your skills and experience
- Cozy atmosphere and modern approaches. We have neither bureaucracy nor strict management or "working under pressure" conditions
Opportunity to implement your ideas, tools, and approaches. We are open to changes and suggestions aimed at improvement
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Β· 35 views Β· 1 application Β· 5d
ML / Computer Vision Engineer (Human Understanding)
Ukraine Β· Product Β· 3 years of experience Β· IntermediateSamsung R&D Institute Ukraine (SRUKR) is looking for ML / Computer Vision engineer who wants to apply theoretical knowledge and practical skills to participate in solving Human Understanding challenges in rapidly evolving Vision AI domain. The position...Samsung R&D Institute Ukraine (SRUKR) is looking for ML / Computer Vision engineer who wants to apply theoretical knowledge and practical skills to participate in solving Human Understanding challenges in rapidly evolving Vision AI domain. The position will involve different aspects of R&D including β research, analysis, prototyping, development and commercialization support of the innovative technologies. Resulting solutions are targeted on Samsung products and services reaching millions of users worldwide.
Required skills / expertise:
- Bachelor's (or higher) degree in computer science, math, statistics, or related field
- 3+ years of experience in conventional and ML/DL based image processing and computer vision
- Practical experience in custom NN-architecture development, training and evaluation
- Strong theoretical knowledge and practical skills in computer vision algorithms (OpenCV)
- Solid Python programming skills (numpy, pandas, matplotlib)
- Knowledge in linear algebra, probability, optimization, and 3D geometry
- Proficiency in math, algorithms and data structures
- Experience with object-oriented design and development
- Basic C++ knowledge
- Understanding research methodologies and S/W development lifecycle
Would be a plus:
- Experience in 3D face reconstruction and face attributes detection
- Experience with ComfyUI and data generation activities
- Participation in CV/ML/DL-intensive research (papers, competitions, patents, etcβ¦)
- Pet projects portfolio that includes β object detection/recognition/tracking, key-points detection and tracking, semantic/instance segmentation, etc.
- Experience with vision transformer, vision encoder-decoder architectures
- Experience with model optimizations for on-device inference (ONNX-runtime, TFLite, SNPE)
- Experience with CPU/GPU profiling tools
- Cross-cultural experience and working English to feel confident in the international team
Key Responsibilities:
- R&D activities in CV based Human Understanding domain (person/face attributes detection, recognition and tracking, 3D face reconstruction).
- Design NN-based solutions and train required ML/DL models
- Optimize algorithms & ML/DL models / their inference and size
- Transfer models and solutions to the edge devices using appropriate frameworks (ONNX, TFLite, SNPE, etc)
- Participate in design process of system architecture
- Collaborate with other R&D engineers worldwide to improve product quality with the latest industry trends in relevant technologies
- Maintain and support existing solutions and services
- Develop demo applications for various platforms
- Opportunity to participate in publication and patent activities
Working Conditions:
- GIG contract
- remote work is possible as well as work in Kyiv office
Benefits:
- competitive salary, annual salary review, annual bonuses
- paid 28 work days of annual vacations and sick leaves
- opportunity to become an inventor of international patents with paid bonuses
- medical & life insurance for employees and their childrens
- paid lunches
- discounts to Samsung products, services
- regular education and self-development on internal courses and seminars
- hybrid work format, working in office is required for some tasks
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Β· 26 views Β· 3 applications Β· 5d
Data Scientist
Full Remote Β· Ukraine Β· 3 years of experience Β· IntermediateΠ‘ΡΠ°Π½ΡΡΠ΅ ΡΠ°ΡΡΠΈΠ½ΠΎΡ ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ, ΡΠΎ ΡΡΠ²ΠΎΡΡΡ ΡΠΈΡΡΠΎΠ²Ρ ΡΠ΅Π°Π»ΡΠ½ΡΡΡΡ! MODUS X β ΡΠΊΡΠ°ΡΠ½ΡΡΠΊΠ° ΠΠ’-ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ, ΠΊΠΎΠΌΠ°Π½Π΄Π° 650+ ΡΠ½ΠΆΠ΅Π½Π΅ΡΡΠ², Π°ΡΡ ΡΡΠ΅ΠΊΡΠΎΡΡΠ², ΡΠΏΠ΅ΡΡΠ°Π»ΡΡΡΡΠ² Π· Π±Π΅Π·ΠΏΠ΅ΠΊΠΈ ΡΠ° Π΄Π°ΡΠ°ΡΠ°ΡΠ½ΡΠΈΡΡΡΠ².β ΠΠΈ ΡΠΎΠ·ΠΏΠΎΡΠ°Π»ΠΈ ΡΠ° ΠΏΡΠΎΠ΄ΠΎΠ²ΠΆΡΡΠΌΠΎ ΡΡΠΏΡΠΎΠ²ΡΠ΄ ΡΠΈΡΡΠΎΠ²ΠΎΡ ΡΡΠ°Π½ΡΡΠΎΡΠΌΠ°ΡΡΡ ΠΠ’ΠΠ, ΡΠΊΠ° ΠΏΠ΅ΡΡΠΎΡ Π²...Π‘ΡΠ°Π½ΡΡΠ΅ ΡΠ°ΡΡΠΈΠ½ΠΎΡ ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ, ΡΠΎ ΡΡΠ²ΠΎΡΡΡ ΡΠΈΡΡΠΎΠ²Ρ ΡΠ΅Π°Π»ΡΠ½ΡΡΡΡ!
MODUS X β ΡΠΊΡΠ°ΡΠ½ΡΡΠΊΠ° ΠΠ’-ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ, ΠΊΠΎΠΌΠ°Π½Π΄Π° 650+ ΡΠ½ΠΆΠ΅Π½Π΅ΡΡΠ², Π°ΡΡ ΡΡΠ΅ΠΊΡΠΎΡΡΠ², ΡΠΏΠ΅ΡΡΠ°Π»ΡΡΡΡΠ² Π· Π±Π΅Π·ΠΏΠ΅ΠΊΠΈ ΡΠ° Π΄Π°ΡΠ°ΡΠ°ΡΠ½ΡΠΈΡΡΡΠ².β ΠΠΈ ΡΠΎΠ·ΠΏΠΎΡΠ°Π»ΠΈ ΡΠ° ΠΏΡΠΎΠ΄ΠΎΠ²ΠΆΡΡΠΌΠΎ ΡΡΠΏΡΠΎΠ²ΡΠ΄ ΡΠΈΡΡΠΎΠ²ΠΎΡ ΡΡΠ°Π½ΡΡΠΎΡΠΌΠ°ΡΡΡ ΠΠ’ΠΠ, ΡΠΊΠ° ΠΏΠ΅ΡΡΠΎΡ Π² Π΅Π½Π΅ΡΠ³Π΅ΡΠΈΡΡ Π£ΠΊΡΠ°ΡΠ½ΠΈ ΡΡΠ°Π»Π° Π½Π° ΡΠ»ΡΡ ΠΌΠ°ΡΡΡΠ°Π±Π½ΠΎΠ³ΠΎ Π΄ΡΠ΄ΠΆΠΈΡΠ°Π»-ΠΏΠ΅ΡΠ΅ΡΠ²ΠΎΡΠ΅Π½Π½Ρ. ΠΠΈΠ½Ρ Π²ΠΈΠ΄ΡΠ»ΠΈΠ»ΠΈΡΡ Π² ΠΎΠΊΡΠ΅ΠΌΡ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ, ΡΠΎΠ±ΠΈ Π΄ΡΠ»ΠΈΡΠΈΡΡ ΡΠ²ΠΎΡΠΌ Π΄ΠΎΡΠ²ΡΠ΄ΠΎΠΌ ΡΠ° Π΅ΠΊΡΠΏΠ΅ΡΡΠΈΠ·ΠΎΡ Π½Π°Π·ΠΎΠ²Π½Ρ, Π·Π°Π»ΠΈΡΠ°ΡΡΠΈΡΡ ΠΠ’-ΠΎΠΏΠΎΡΠΎΡ Π΄Π»Ρ ΡΠΈΡ , Ρ ΡΠΎ Π½Π΅ΡΠ΅ ΡΠ²ΡΡΠ»ΠΎ ΡΠ° ΡΠΏΡΠΈΡΡ Π²ΡΠ΄Π½ΠΎΠ²Π»Π΅Π½Π½Ρ ΠΊΡΠ°ΡΠ½ΠΈ.
Π¨ΡΠΊΠ°ΡΠΌΠΎ Middle Data ScientistΡst, Π΄Π»Ρ ΠΏΡΠ΄ΡΠΈΠ»Π΅Π½Π½Ρ Data Science-ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ. Π―ΠΊΠΈΠΉ Π±ΡΠ΄Π΅ Π΄ΠΎΠ»ΡΡΠ΅Π½ΠΈΠΉ Π΄ΠΎ Π²ΠΈΠΊΠΎΠ½Π°Π½Π½Ρ ΠΏΠΎΠ²Π½ΠΎΠ³ΠΎ ΡΠΈΠΊΠ»Ρ ΠΏΡΠΎΠ΅ΠΊΡΡΠ² β Π²ΡΠ΄ Π°Π½Π°Π»ΡΠ·Ρ Π΄Π°Π½ΠΈΡ Π΄ΠΎ Π·Π°ΠΏΡΡΠΊΡ ΠΌΠΎΠ΄Π΅Π»Ρ Ρ ΠΏΡΠΎΠ΄Π°ΠΊΡΠ½, ΠΏΡΠ°ΡΡΡΡΠΈ Ρ ΠΊΡΠΎΡ-ΡΡΠ½ΠΊΡΡΠΎΠ½Π°Π»ΡΠ½ΡΠΉ ΠΊΠΎΠΌΠ°Π½Π΄Ρ.
Π€ΡΠ½ΠΊΡΡΡ ΠΏΠΎΡΠ°Π΄ΠΈ:
- ΠΠ½Π°Π»ΡΠ· ΠΏΡΠΎΠ±Π»Π΅ΠΌΠΈ ΡΠ° ΠΏΠΎΡΡΠ°Π½ΠΎΠ²ΠΊΠ° Π·Π°Π΄Π°ΡΡ
- ΠΠ±ΡΡ, ΠΎΡΠΈΡΠ΅Π½Π½Ρ ΡΠ° ΠΏΡΠ΄Π³ΠΎΡΠΎΠ²ΠΊΠ° Π΄Π°Π½ΠΈΡ
- ΠΠΎΠ΄Π΅Π»ΡΠ²Π°Π½Π½Ρ ΡΠ° Π΅ΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠΈ
- ΠΠ°Π»ΡΠ΄Π°ΡΡΡ ΡΠ° ΠΏΠΎΡΡΠ½Π΅Π½Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡΠ²
- Π‘ΠΏΡΠ²ΠΏΡΠ°ΡΡ ΡΠ° ΠΊΠΎΠΌΡΠ½ΡΠΊΠ°ΡΡΡ
- ΠΠΎΠ»ΡΠΏΡΠ΅Π½Π½Ρ ΠΏΡΠΎΡΠ΅ΡΡΠ² ΡΠ° ΠΌΠ΅Π½ΡΠΎΡΡΡΠ²ΠΎ
- R&D ΡΠ° ΡΠΎΠ·Π²ΠΈΡΠΎΠΊ Π΅ΠΊΡΠΏΠ΅ΡΡΠΈΠ·ΠΈ
ΠΡΠΎΡΠ΅ΡΡΠΉΠ½Ρ ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΡΡ:
- ΠΠΌΡΠ½Π½Ρ ΡΡΡΠΊΠΎ ΡΠΎΡΠΌΡΠ»ΡΠ²Π°ΡΠΈ Π·Π°Π΄Π°ΡΡ ΡΠ° ΡΡΠ°Π²ΠΈΡΠΈ ΠΏΠΈΡΠ°Π½Π½Ρ
- ΠΠΌΡΠ½Π½Ρ ΠΏΠΎΠ΄ΠΈΠ²ΠΈΡΠΈΡΡ Π½Π° ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΠΏΡΠ΄ ΡΠ½ΡΠΈΠΌ ΠΊΡΡΠΎΠΌ Π·ΠΎΡΡ
- Python (pandas, NumPy, scikit-learn), SQL; Π²ΠΏΠ΅Π²Π½Π΅Π½Π° ΡΠΎΠ±ΠΎΡΠ° Π· Git.
- ΠΠ»Π°ΡΠΈΡΠ½Ρ ML-Π°Π»Π³ΠΎΡΠΈΡΠΌΠΈ (Gradient Boosting β XGBoost/LightGBM/CatBoost, Random Forest, Logistic/Linear Regression, k-NN); Π·Π½Π°Π½Π½Ρ ΠΌΠ΅ΡΠΎΠ΄ΡΠ² ΡΠ΅Π³ΡΠ»ΡΡΠΈΠ·Π°ΡΡΡ, ΠΊΡΠΎΡ-Π²Π°Π»ΡΠ΄Π°ΡΡΡ ΡΠ° ΠΏΡΠ΄Π±ΠΎΡΡ Π³ΡΠΏΠ΅ΡΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΡΠ².
- ΠΠΌΠΎΠ²ΡΡΠ½ΡΡΠ½Ρ ΡΠΎΠ·ΠΏΠΎΠ΄ΡΠ»ΠΈ, ΠΏΠ΅ΡΠ΅Π²ΡΡΠΊΠ° Π³ΡΠΏΠΎΡΠ΅Π·, A/B-ΡΠ΅ΡΡΠΈ, ΡΠ½ΡΠ΅ΠΏΡΠ΅ΡΠΎΠ²Π°Π½ΡΡΡΡ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ.
- PyTorch Π°Π±ΠΎ TensorFlow/Keras Π΄Π»Ρ Π·Π°Π΄Π°Ρ CV ΡΠΈ NLP; ΡΠΌΡΠ½Π½Ρ Π±ΡΠ΄ΡΠ²Π°ΡΠΈ ΡΠ° ΡΡΠ΅Π½ΡΠ²Π°ΡΠΈ ΠΌΠΎΠ΄Π΅Π»Ρ Π· TensorBoard-Π»ΠΎΠ³ΡΠ²Π°Π½Π½ΡΠΌ.
- MLflow / Weights & Biases, Docker; Π±Π°Π·ΠΎΠ²Π΅ ΡΠΎΠ·ΡΠΌΡΠ½Π½Ρ CI/CD Π΄Π»Ρ ML-ΠΏΠ°ΠΉΠΏΠ»Π°ΠΉΠ½ΡΠ².
- ΠΡΠ°ΠΊΡΠΈΡΠ½ΠΈΠΉ Π΄ΠΎΡΠ²ΡΠ΄ Ρ Ρ ΠΎΡΠ° Π± ΠΎΠ΄Π½ΡΠΉ ΡΠ· ΠΏΠ»Π°ΡΡΠΎΡΠΌ (AWS, GCP, Azure) Π΄Π»Ρ ΡΠΎΠ·Π³ΠΎΡΡΠ°Π½Π½Ρ Π°Π±ΠΎ ΡΡΠ΅Π½ΡΠ²Π°Π½Π½Ρ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ.
- ΠΠ°ΡΠ²Π½ΡΡΡΡ ΡΠ΅ΡΡΠΈΡΡΠΊΠ°ΡΡΡ ΠΏΠΎ Data&AI
- ΠΠΌΡΠ½Π½Ρ Π½Π΅Π·Π°Π»Π΅ΠΆΠ½ΠΎ ΠΏΠ΅ΡΠ΅Π²ΡΡΡΡΠΈ Π²Ρ ΡΠ΄Π½Ρ Π΄Π°Π½Ρ ΡΠ° ΡΠ΅Π·ΡΠ»ΡΡΠ°Ρ
- ΠΠ²ΡΠΎΠ½ΠΎΠΌΠ½ΡΡΡΡ
- ΠΠΎΠΌΡΠ½ΡΠΊΠ°Π±Π΅Π»ΡΠ½ΡΡΡΡ
ΠΠΈ ΠΏΡΠΎΠΏΠΎΠ½ΡΡΠΌΠΎ:
- ΠΡΡΡΡΠΉΠ½Π΅ ΠΏΡΠ°ΡΠ΅Π²Π»Π°ΡΡΡΠ²Π°Π½Π½Ρ
- KΠΎΠ½ΠΊΡΡΠ΅Π½ΡΠ½ΠΈΠΉ ΡΡΠ²Π΅Π½Ρ Π·Π°ΡΠΎΠ±ΡΡΠ½ΠΎΡ ΠΏΠ»Π°ΡΠΈ ΡΠ° ΡΠΎΡΡΠ°Π»ΡΠ½Ρ Π³Π°ΡΠ°Π½ΡΡΡ
- ΠΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½Π° ΠΏΡΠΎΠ³ΡΠ°ΠΌΠ° ΠΌΠ΅Π΄ΠΈΡΠ½ΠΎΠ³ΠΎ ΡΡΡΠ°Ρ ΡΠ²Π°Π½Π½Ρ ΡΠ° ΠΏΡΠΎΠ³ΡΠ°ΠΌΠ° ΠΏΡΠΈΡ ΠΎΠ»ΠΎΠ³ΡΡΠ½ΠΎΡ ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΠΈ ΡΠΏΡΠ²ΡΠΎΠ±ΡΡΠ½ΠΈΠΊΡΠ²
- Π ΠΎΠ±ΠΎΡΡ Π² ΡΠ½Π½ΠΎΠ²Π°ΡΡΠΉΠ½ΠΎΠΌΡ ΠΏΠ°ΡΠΊΡ Unit City
- ΠΠΎΠΆΠ»ΠΈΠ²ΠΎΡΡΡ Π½Π°Π²ΡΠ°Π½Π½Ρ ΡΠ° ΠΏΡΠΎΡΠ΅ΡΡΠΉΠ½ΠΈΠΉ ΡΠΎΠ·Π²ΠΈΡΠΎΠΊ (ΠΎΠ½Π»Π°ΠΉΠ½ ΠΊΡΡΡΠΈ, Π°ΡΠ΄ΠΈΡΠΎΡΠ½Ρ ΡΡΠ΅Π½ΡΠ½Π³ΠΈ, ΠΌΠ°ΠΉΡΡΠ΅Ρ-ΠΊΠ»Π°ΡΠΈ, ΠΏΡΠΎΡΠ΅ΡΡΠΉΠ½Ρ ΡΠΏΡΠ»ΡΠ½ΠΎΡΠΈ)
ΠΠΈ ΡΡΠ½ΡΡΠΌΠΎ Π²Π°Ρ ΡΠ½ΡΠ΅ΡΠ΅Ρ Π΄ΠΎ MODUS X ΡΠ° Π³ΠΎΡΠΎΠ²Π½ΡΡΡΡ ΠΏΡΠΈΠΉΠΌΠ°ΡΠΈ Π²ΠΈΠΊΠ»ΠΈΠΊΠΈ. Π’ΡΡ ΠΊΠΎΠΆΠ΅Π½ ΠΌΠΎΠΆΠ΅ ΡΠΎΠ·ΠΊΡΠΈΡΠΈ ΡΠ²ΠΎΡ ΡΠ°Π»Π°Π½ΡΠΈ ΠΉ Π·ΡΠΎΠ±ΠΈΡΠΈ Π²Π½Π΅ΡΠΎΠΊ Ρ ΡΠΏΡΠ»ΡΠ½ΠΈΠΉ ΡΡΠΏΡΡ . ΠΠΈ ΡΠ½Π²Π΅ΡΡΡΡΠΌΠΎ Π² ΡΠΎΠ·Π²ΠΈΡΠΎΠΊ, Π΄ΠΎΠΏΠΎΠΌΠ°Π³Π°ΡΠΌΠΎ ΠΎΡΡΠΈΠΌΡΠ²Π°ΡΠΈ Π½ΠΎΠ²Ρ Π·Π½Π°Π½Π½Ρ ΡΠ° Π΄ΠΎΡΡΠ³Π°ΡΠΈ ΠΏΡΠΎΡΠ΅ΡΡΠΉΠ½ΠΈΡ ΡΡΠ»Π΅ΠΉ.
ΠΠ°ΡΠ° ΠΊΠΎΠΌΠ°Π½Π΄Π° ΡΠ²Π°ΠΆΠ½ΠΎ ΡΠΎΠ·Π³Π»ΡΠ΄Π°Ρ Π²ΡΡ Π·Π°ΡΠ²ΠΊΠΈ, Ρ ΡΠΊΡΠΎ Π²Π°ΡΠ° ΠΊΠ°Π½Π΄ΠΈΠ΄Π°ΡΡΡΠ° Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π°Ρ Π²ΠΈΠΌΠΎΠ³Π°ΠΌ Π²Π°ΠΊΠ°Π½ΡΡΡ, ΡΠ΅ΠΊΡΡΡΠ΅Ρ ΠΎΠ±ΠΎΠ²βΡΠ·ΠΊΠΎΠ²ΠΎ Π·Π²βΡΠΆΠ΅ΡΡΡΡ Π· Π²Π°ΠΌΠΈ Π²ΠΏΡΠΎΠ΄ΠΎΠ²ΠΆ 2 ΡΠΈΠΆΠ½ΡΠ².
ΠΡΠ»ΡΡΠ΅ ΡΠ½ΡΠΎΡΠΌΠ°ΡΡΡ ΠΏΡΠΎ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ ΡΠ° Π½Π°Ρ Π΄ΠΎΡΠ²ΡΠ΄ Π½Π° ΠΎΡΡΡΡΠΉΠ½ΡΠΉ ΡΡΠΎΡΡΠ½ΡΡ MODUS X Π² LinkedIn.
ΠΠ°ΠΏΡΠ°Π²Π»ΡΡΡΠΈ ΡΠ΅Π·ΡΠΌΠ΅ Π½Π° ΡΡ Π²Π°ΠΊΠ°Π½ΡΡΡ, ΠΠΈ Π½Π°Π΄Π°ΡΡΠ΅ Π·Π³ΠΎΠ΄Ρ Π’ΠΠ Β«ΠΠΠΠ£Π‘ ΠΠΠ‘Β» Π½Π° ΠΎΠ±ΡΠΎΠ±ΠΊΡ Π½Π°Π΄Π°Π½ΠΈΡ ΠΠ°ΠΌΠΈ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΡΠ½ΠΈΡ Π΄Π°Π½ΠΈΡ Π·Π³ΡΠ΄Π½ΠΎ ΠΠ°ΠΊΠΎΠ½Ρ Π£ΠΊΡΠ°ΡΠ½ΠΈ Β«ΠΡΠΎ Π·Π°Ρ ΠΈΡΡ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΡΠ½ΠΈΡ Π΄Π°Π½ΠΈΡ Β». ΠΠ³ΠΎΠ΄Π° Π½Π°Π΄Π°ΡΡΡΡΡ Π² ΡΠΎΠΌΡ ΡΠΈΡΠ»Ρ Π΄Π»Ρ ΡΡ ΠΎΠ±ΡΠΎΠ±ΠΊΠΈ Π² Π·ΠΎΠ²Π½ΡΡΠ½ΡΡ ΡΠΈΡΡΠ΅ΠΌΠ°Ρ , Π· ΠΌΠ΅ΡΠΎΡ ΡΡΠΏΡΠΎΠ²ΠΎΠ΄ΠΆΠ΅Π½Π½Ρ ΠΏΡΠΎΡΠ΅ΡΡ Π½Π°ΠΉΠΌΡ.
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Β· 45 views Β· 10 applications Β· 30d
Data Science (Π ΠΈΠ·ΠΈΠΊ-ΠΌΠΎΠ΄Π΅Π»Ρ, Π°Π½ΡΠΈΡΡΠΎΠ΄)
Full Remote Β· Countries of Europe or Ukraine Β· Product Β· 3 years of experience Β· Beginner/ElementaryΠΠΎΠ²Π° ΡΠ° ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΡ Python Π°Π±ΠΎ R Scikit-learn XGBoost LightGBM CatBoost TensorFlow SQL Π’Π²ΠΎΡ ΠΎΠ±ΠΎΠ²βΡΠ·ΠΊΠΈ Π‘ΡΠ²ΠΎΡΠ΅Π½Π½Ρ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΡΠΊΠΎΡΠΈΠ½Π³Ρ Π΄Π»Ρ ΠΎΡΡΠ½ΠΊΠΈ ΠΊΡΠ΅Π΄ΠΈΡΠ½ΠΎΠ³ΠΎ ΡΠΈΠ·ΠΈΠΊΡ (Π°ΠΏΠ»ΡΠΊΠ°ΡΡΠΉΠ½ΠΈΠΉ, ΠΏΠΎΠ²Π΅Π΄ΡΠ½ΠΊΠΎΠ²ΠΈΠΉ) Π ΠΎΠ·ΡΠΎΠ±ΠΊΠ° Π°Π½ΡΠΈΡΡΠΎΠ΄-ΡΠΈΡΡΠ΅ΠΌ Π΄Π»Ρ Π²ΠΈΡΠ²Π»Π΅Π½Π½Ρ ΠΏΡΠ΄ΠΎΠ·ΡΡΠ»ΠΈΡ ...ΠΠΎΠ²Π° ΡΠ° ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΡ
Python Π°Π±ΠΎ R Scikit-learn XGBoost LightGBM CatBoost TensorFlow SQL
Π’Π²ΠΎΡ ΠΎΠ±ΠΎΠ²βΡΠ·ΠΊΠΈ
Π‘ΡΠ²ΠΎΡΠ΅Π½Π½Ρ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΡΠΊΠΎΡΠΈΠ½Π³Ρ Π΄Π»Ρ ΠΎΡΡΠ½ΠΊΠΈ ΠΊΡΠ΅Π΄ΠΈΡΠ½ΠΎΠ³ΠΎ ΡΠΈΠ·ΠΈΠΊΡ (Π°ΠΏΠ»ΡΠΊΠ°ΡΡΠΉΠ½ΠΈΠΉ, ΠΏΠΎΠ²Π΅Π΄ΡΠ½ΠΊΠΎΠ²ΠΈΠΉ)
Π ΠΎΠ·ΡΠΎΠ±ΠΊΠ° Π°Π½ΡΠΈΡΡΠΎΠ΄-ΡΠΈΡΡΠ΅ΠΌ Π΄Π»Ρ Π²ΠΈΡΠ²Π»Π΅Π½Π½Ρ ΠΏΡΠ΄ΠΎΠ·ΡΡΠ»ΠΈΡ ΡΡΠ°Π½Π·Π°ΠΊΡΡΠΉ ΡΠ° ΡΠ°Ρ ΡΠ°ΠΉΡΡΠ²Π°
ΠΠΏΡΠΎΠ²Π°Π΄ΠΆΠ΅Π½Π½Ρ Π°Π»Π³ΠΎΡΠΈΡΠΌΡΠ² ΡΠ°Π½Π½ΡΠΎΠ³ΠΎ ΠΏΠΎΠΏΠ΅ΡΠ΅Π΄ΠΆΠ΅Π½Π½Ρ ΡΠΈΠ·ΠΈΠΊΡΠ² Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ Π΄Π°Π½ΠΈΡ ΡΡΠ°Π½Π·Π°ΠΊΡΡΠΉ, ΠΏΠΎΠ²Π΅Π΄ΡΠ½ΠΊΠΎΠ²ΠΈΡ ΡΠ° Π΄Π΅ΠΌΠΎΠ³ΡΠ°ΡΡΡΠ½ΠΈΡ Π΄Π°Π½ΠΈΡ
ΠΡΠ΄Π³ΠΎΡΠΎΠ²ΠΊΠ° Π²Π΅Π»ΠΈΠΊΠΈΡ ΠΎΠ±ΡΡΠ³ΡΠ² Π΄Π°Π½ΠΈΡ Π΄Π»Ρ ΡΡΠ΅Π½ΡΠ²Π°Π½Π½Ρ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ (ΠΏΡΠ΅ΠΏΡΠΎΡΠ΅ΡΠΈΠ½Π³, ΡΡΠ°Π½ΡΡΠΎΡΠΌΠ°ΡΡΡ, ΠΎΡΠΈΡΠ΅Π½Π½Ρ)
ΠΠ½Π°Π»ΡΠ· ΡΡΠ°Π½Π·Π°ΠΊΡΡΠΉΠ½ΠΈΡ Π΄Π°Π½ΠΈΡ , ΠΏΠΎΠ²Π΅Π΄ΡΠ½ΠΊΠΎΠ²ΠΈΡ ΠΏΠ°ΡΠ΅ΡΠ½ΡΠ² ΠΊΠ»ΡΡΠ½ΡΡΠ² ΡΠ° ΡΡΠ²ΠΎΡΠ΅Π½Π½Ρ Π½ΠΎΠ²ΠΈΡ ΡΡΡ Π΄Π»Ρ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ
ΠΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½Ρ ΡΡΡΠΎΡΠΈΡΠ½ΠΈΡ Π΄Π°Π½ΠΈΡ Π΄Π»Ρ Π²ΠΈΠ·Π½Π°ΡΠ΅Π½Π½Ρ ΡΡΠ΅Π½Π΄ΡΠ² ΡΠ°Ρ ΡΠ°ΠΉΡΡΠ²Π° Π°Π±ΠΎ ΡΡΠ½Π°Π½ΡΠΎΠ²ΠΈΡ ΡΠΈΠ·ΠΈΠΊΡΠ²
ΠΡΠΎΠ²Π΅Π΄Π΅Π½Π½Ρ ΡΠ΅ΡΡΡΠ²Π°Π½Π½Ρ, ΠΎΡΡΠ½ΠΊΠΈ ΡΠ° ΠΎΠΏΡΠΈΠΌΡΠ·Π°ΡΡΡ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ Π·Π° Π΄ΠΎΠΏΠΎΠΌΠΎΠ³ΠΎΡ ΠΌΠ΅ΡΡΠΈΠΊ ΡΠΎΡΠ½ΠΎΡΡΡ, recall, precision, AUC-ROC, F1
Π Π΅Π°Π»ΡΠ·Π°ΡΡΡ ΡΠ° ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΠ° ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ Ρ ΡΠ΅Π°Π»ΡΠ½ΠΎΠΌΡ ΡΠ°ΡΡ Π΄Π»Ρ Π·Π°Π±Π΅Π·ΠΏΠ΅ΡΠ΅Π½Π½Ρ Π²ΠΈΡΠΎΠΊΠΎΡ ΡΠ²ΠΈΠ΄ΠΊΠΎΡΡΡ ΠΎΠ±ΡΠΎΠ±ΠΊΠΈ ΡΠ° ΡΠΎΡΠ½ΠΎΡΡΡ
ΠΠ½ΡΠ΅Π³ΡΠ°ΡΡΡ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ Ρ ΠΏΡΠΎΠ΄Π°ΠΊΡΠ½ ΡΠ΅ΡΠ΅Π΄ΠΎΠ²ΠΈΡΠ΅
ΠΠΎΠ½ΡΡΠΎΡΠΈΠ½Π³ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ Π½Π° ΠΏΡΠΎΠ΄Π°ΠΊΡΠ΅Π½Ρ
ΠΠ°ΡΡΡΠ°Π±ΡΠ²Π°Π½Π½Ρ ΡΡΡΠ΅Π½Ρ Π΄Π»Ρ ΡΠΎΠ±ΠΎΡΠΈ Π· Π²Π΅Π»ΠΈΠΊΠΈΠΌ ΠΎΠ±ΡΡΠ³ΠΎΠΌ ΡΡΠ°Π½Π·Π°ΠΊΡΡΠΉ Ρ ΡΠ΅Π°Π»ΡΠ½ΠΎΠΌΡ ΡΠ°ΡΡ
Π ΠΎΠ±ΠΎΡΠ° Π· Π°Π½Π°Π»ΡΡΠΈΠΊΠ°ΠΌΠΈ Π΄Π°Π½ΠΈΡ , ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ²ΠΈΠΌΠΈ ΠΊΠΎΠΌΠ°Π½Π΄Π°ΠΌΠΈ ΡΠ° Π΄Π΅ΠΏΠ°ΡΡΠ°ΠΌΠ΅Π½ΡΠΎΠΌ ΡΠΈΠ·ΠΈΠΊΡΠ² Π΄Π»Ρ Π²ΠΈΠ·Π½Π°ΡΠ΅Π½Π½Ρ Π±ΡΠ·Π½Π΅Ρ-Π²ΠΈΠΌΠΎΠ³
ΠΠ·Π°ΡΠΌΠΎΠ΄ΡΡ Π· ΡΠ½ΠΆΠ΅Π½Π΅ΡΠ°ΠΌΠΈ Π΄Π»Ρ ΡΠ½ΡΠ΅Π³ΡΠ°ΡΡΡ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ Ρ ΡΡΠ½ΡΡΡΡ ΠΏΠ»Π°ΡΡΠΎΡΠΌΠΈ
ΠΠΈΠΌΠΎΠ³ΠΈ
ΠΠΈΡΠ° ΡΠ΅Ρ Π½ΡΡΠ½Π° ΠΎΡΠ²ΡΡΠ° (ΠΊΠΎΠΌΠΏβΡΡΠ΅ΡΠ½Ρ Π½Π°ΡΠΊΠΈ, ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΠΊΠ°, ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠ°, Π΅ΠΊΠΎΠ½ΠΎΠΌΡΠΊΠ°, Π°Π±ΠΎ ΡΡΠ½Π°Π½ΡΠΈ)
ΠΡΠ½ΡΠΌΡΠΌ 3 ΡΠΎΠΊΠΈ Π΄ΠΎΡΠ²ΡΠ΄Ρ ΡΠΎΠ±ΠΎΡΠΈ Ρ ΡΡΠ΅ΡΡ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ Π½Π°Π²ΡΠ°Π½Π½Ρ ΡΠ° ΡΠΈΠ·ΠΈΠΊ-ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ
ΠΡΠ°ΠΊΡΠΈΡΠ½ΠΈΠΉ Π΄ΠΎΡΠ²ΡΠ΄ ΡΠΎΠ·ΡΠΎΠ±ΠΊΠΈ ΡΠΊΠΎΡΠΈΠ½Π³ΠΎΠ²ΠΈΡ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ Π°Π±ΠΎ Π°Π½ΡΠΈΡΡΠΎΠ΄-ΡΠΈΡΡΠ΅ΠΌ
ΠΠΈΡΠΎΠΊΠΈΠΉ ΡΡΠ²Π΅Π½Ρ Π²ΠΎΠ»ΠΎΠ΄ΡΠ½Π½Ρ Python Π°Π±ΠΎ R
ML-ΡΡΠ΅ΠΉΠΌΠ²ΠΎΡΠΊΠΈ: Scikit-learn, XGBoost, LightGBM, CatBoost
ΠΠ»ΠΈΠ±ΠΎΠΊΠ΅ Π½Π°Π²ΡΠ°Π½Π½Ρ: TensorFlow, PyTorch (Π΄ΠΎΠ΄Π°ΡΠΊΠΎΠ²ΠΎ)
ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· SQL Π΄Π»Ρ ΠΎΡΡΠΈΠΌΠ°Π½Π½Ρ Π΄Π°Π½ΠΈΡ ΡΠ· Π±Π°Π·
Π ΠΎΠ±ΠΎΡΠ° Π· Π²Π΅Π»ΠΈΠΊΠΈΠΌΠΈ Π΄Π°Π½ΠΈΠΌΠΈ: Spark (Π±ΡΠ΄Π΅ ΠΏΠ»ΡΡΠΎΠΌ)
Π Π΅Π³ΡΠ΅ΡΡΡ, Π΄Π΅ΡΠ΅Π²Π° ΡΡΡΠ΅Π½Ρ, Π°Π½ΡΠ°ΠΌΠ±Π»Π΅Π²Ρ ΠΌΠ΅ΡΠΎΠ΄ΠΈ, ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΡΡ, Π°Π½ΠΎΠΌΠ°Π»ΡΡ Π΄Π΅ΡΠ΅ΠΊΡΠ½ (anomaly detection)
ROC-AUC, Precision/Recall, Gini, KS
ΠΠΎΠ±ΡΠ΄ΠΎΠ²Π° Π°Π½ΡΠΈΡΡΠΎΠ΄-ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ ΠΏΠΎΠ²Π΅Π΄ΡΠ½ΠΊΠΎΠ²ΠΈΡ Π΄Π°Π½ΠΈΡ (Π½Π°ΠΏΡ. ΠΌΠΎΠ΄Π΅Π»Ρ Π²ΠΈΡΠ²Π»Π΅Π½Π½Ρ Π°Π½ΠΎΠΌΠ°Π»ΡΠΉ)
ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· ΡΠ°ΡΠΎ-ΡΡΠ΄Π½ΠΈΠΌΠΈ Π΄Π°Π½ΠΈΠΌΠΈ ΡΠ° ΡΡΡΠ°ΠΌΠΈ Π΄Π»Ρ Π°Π½Π°Π»ΡΠ·Ρ ΡΡΠ°Π½Π·Π°ΠΊΡΡΠΉ
ΠΠ½Π°Π½Π½Ρ ΡΠΈΡΡΠ΅ΠΌ ΡΠΊΠΎΡΠΈΠ½Π³Ρ
ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· API Π΄Π»Ρ ΡΠ΅Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠ°ΡΡ
ΠΠ½Π°Π½Π½Ρ Ρ ΠΌΠ°ΡΠ½ΠΈΡ ΡΠ΅ΡΠ²ΡΡΡΠ² (AWS, Azure)
Π‘ΠΈΡΡΠ΅ΠΌΠΈ ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ Π²Π΅ΡΡΡΠΉ: Git
ΠΠΎΠ±ΡΠ΅ ΠΌΠ°ΡΠΈ
ΠΠΎΡΠ²ΡΠ΄ Ρ ΡΡΠ²ΠΎΡΠ΅Π½Π½Ρ rule-based ΡΠ° ML-Π±Π°Π·ΠΎΠ²Π°Π½ΠΈΡ Π°Π½ΡΠΈΡΡΠΎΠ΄-ΡΠΈΡΡΠ΅ΠΌ
Π ΠΎΠ·ΡΠΌΡΠ½Π½Ρ Π±ΡΠ·Π½Π΅Ρ-Π»ΠΎΠ³ΡΠΊΠΈ ΡΡΠ½Π°Π½ΡΠΎΠ²ΠΈΡ ΠΏΡΠΎΡΠ΅ΡΡΠ², ΠΊΡΠ΅Π΄ΠΈΡΡΠ²Π°Π½Π½Ρ ΡΠ° ΠΏΠ»Π°ΡΠ΅ΠΆΡΠ²
ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· graph-based ΡΠΈΡΡΠ΅ΠΌΠ°ΠΌΠΈ Π΄Π»Ρ Π΄Π΅ΡΠ΅ΠΊΡΡΡ ΡΠ°Ρ ΡΠ°ΠΉΡΡΠ²Π°
ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· ΡΠΈΡΡΠ΅ΠΌΠ°ΠΌΠΈ ΠΏΠΎΡΠΎΠΊΠΎΠ²ΠΎΡ ΠΎΠ±ΡΠΎΠ±ΠΊΠΈ Π΄Π°Π½ΠΈΡ
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Β· 48 views Β· 3 applications Β· 25d
Data Scientist (NLP + Recommender Systems)
Ukraine Β· Product Β· 3 years of experience Ukrainian Product πΊπ¦ΠΠΎΠΌΠ°Π½Π΄Π° MEGOGO ΡΡΠΊΠ°Ρ Data Scientist, ΡΠΊΠΈΠΉ Π΄ΠΎΠΏΠΎΠΌΠΎΠΆΠ΅ Π²Π΄ΠΎΡΠΊΠΎΠ½Π°Π»ΠΈΡΠΈ Π½Π°ΡΡ ΡΠΈΡΡΠ΅ΠΌΠΈ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΡΠ·Π°ΡΡΡ, ΠΏΠΎΡΡΠΊΡ ΡΠ° ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΡΠΉ. Π―ΠΊΡΠΎ ΡΠΎΠ±Ρ ΡΡΠΊΠ°Π²ΠΎ ΠΏΡΠ°ΡΡΠ²Π°ΡΠΈ Π· ΡΠ΅Π°Π»ΡΠ½ΠΈΠΌΠΈ Π΄Π°Π½ΠΈΠΌΠΈ ΠΌΡΠ»ΡΠΉΠΎΠ½ΡΠ² ΠΊΠΎΡΠΈΡΡΡΠ²Π°ΡΡΠ², Π·Π°ΡΡΠΎΡΠΎΠ²ΡΠ²Π°ΡΠΈ NLP-ΠΌΠΎΠ΄Π΅Π»Ρ Π² ΠΏΡΠΎΠ΄Π°ΠΊΡΠ΅Π½Ρ ΡΠ° ΡΠΎΠ·Π²ΠΈΠ²Π°ΡΠΈ ΡΡΡΠ°ΡΠ½Ρ...ΠΠΎΠΌΠ°Π½Π΄Π° MEGOGO ΡΡΠΊΠ°Ρ Data Scientist, ΡΠΊΠΈΠΉ Π΄ΠΎΠΏΠΎΠΌΠΎΠΆΠ΅ Π²Π΄ΠΎΡΠΊΠΎΠ½Π°Π»ΠΈΡΠΈ Π½Π°ΡΡ ΡΠΈΡΡΠ΅ΠΌΠΈ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΡΠ·Π°ΡΡΡ, ΠΏΠΎΡΡΠΊΡ ΡΠ° ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΡΠΉ. Π―ΠΊΡΠΎ ΡΠΎΠ±Ρ ΡΡΠΊΠ°Π²ΠΎ ΠΏΡΠ°ΡΡΠ²Π°ΡΠΈ Π· ΡΠ΅Π°Π»ΡΠ½ΠΈΠΌΠΈ Π΄Π°Π½ΠΈΠΌΠΈ ΠΌΡΠ»ΡΠΉΠΎΠ½ΡΠ² ΠΊΠΎΡΠΈΡΡΡΠ²Π°ΡΡΠ², Π·Π°ΡΡΠΎΡΠΎΠ²ΡΠ²Π°ΡΠΈ NLP-ΠΌΠΎΠ΄Π΅Π»Ρ Π² ΠΏΡΠΎΠ΄Π°ΠΊΡΠ΅Π½Ρ ΡΠ° ΡΠΎΠ·Π²ΠΈΠ²Π°ΡΠΈ ΡΡΡΠ°ΡΠ½Ρ Recommender Systems β ΠΏΡΠΈΡΠ΄Π½ΡΠΉΡΡ.
Π©ΠΎ Π½Π° ΡΠ΅Π±Π΅ ΠΎΡΡΠΊΡΡ:
- Π ΠΎΠ·ΡΠΎΠ±ΠΊΠ° ΡΠ° ΡΠΎΠ·Π²ΠΈΡΠΎΠΊ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΡΠΉ Π΄Π»Ρ ΡΡΠ·Π½ΠΎΠ³ΠΎ ΡΠΈΠΏΡ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡ;
- ΠΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½Ρ NLP-ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ Π΄Π»Ρ ΠΏΠΎΠΊΡΠ°ΡΠ΅Π½Π½Ρ ΠΏΠΎΡΡΠΊΡ ΡΠ° ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΡΠ·Π°ΡΡΡ;
- Π£ΡΠ°ΡΡΡ Ρ ΡΡΠ²ΠΎΡΠ΅Π½Π½Ρ ΠΏΡΠΎΠ΄ΡΠΊΡΡΠ², ΠΏΠΎΠ²'ΡΠ·Π°Π½ΠΈΡ Π· Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½ΡΠΌ NLP Π½Π°ΠΏΡΡΠΌΠΊΡ(Π°Π½Π°Π»ΡΠ·, ΠΏΠΎΡΡΠΊ, ΡΡΠ²ΠΎΡΠ΅Π½Π½Ρ Π²Π»Π°ΡΠ½ΠΈΡ ΡΡΡ Π· ΡΠ΅ΠΊΡΡΠΎΠ²ΠΎΡ ΡΠΊΠ»Π°Π΄ΠΎΠ²ΠΎΡ, ΡΠΎΡΠΎ);
- Π ΠΎΠ±ΠΎΡΠ° Π· Π²Π΅Π»ΠΈΠΊΠΈΠΌΠΈ ΠΌΠ°ΡΠΈΠ²Π°ΠΌΠΈ ΠΊΠΎΡΠΈΡΡΡΠ²Π°ΡΡΠΊΠΈΡ Π΄Π°Π½ΠΈΡ Π΄Π»Ρ ΠΏΠΎΠ±ΡΠ΄ΠΎΠ²ΠΈ ΡΠ½ΡΠΊΠ°Π»ΡΠ½ΠΈΡ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΡΠ·Π°ΡΡΡ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡ;
- Π£ΡΠ°ΡΡΡ Ρ ΠΏΠΎΠ±ΡΠ΄ΠΎΠ²Ρ ΡΠ° Π²Π΄ΠΎΡΠΊΠΎΠ½Π°Π»Π΅Π½Π½Ρ ΠΏΠ°ΠΉΠΏΠ»Π°ΠΉΠ½ΡΠ² Π΄Π»Ρ ΡΡΠ΅Π½ΡΠ²Π°Π½Π½Ρ ΡΠ° Π΄Π΅ΠΏΠ»ΠΎΡ ML-ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ;
- Π’Π΅ΡΡΡΠ²Π°Π½Π½Ρ Π³ΡΠΏΠΎΡΠ΅Π·, Π·Π°ΠΏΡΡΠΊ A/B Π΅ΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΡΠ² ΡΠ° ΡΠ½ΡΠ΅ΡΠΏΡΠ΅ΡΠ°ΡΡΡ ΡΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡΠ²;
- Π‘ΠΏΡΠ²ΠΏΡΠ°ΡΡ Ρ ΠΎΠ±ΠΌΡΠ½ Π΄ΠΎΡΠ²ΡΠ΄ΠΎΠΌ Π· ΡΠ½ΡΠΈΠΌΠΈ DS-ΡΠ°Ρ ΡΠ²ΡΡΠΌΠΈ Π΄Π»Ρ Π²ΠΈΡΡΡΠ΅Π½Π½Ρ Π·Π°Π΄Π°Ρ Π² ΡΡΠΌΡΠΆΠ½ΠΈΡ Π½Π°ΠΏΡΡΠΌΠΊΠ°Ρ .
ΠΠ΅ΠΎΠ±Ρ ΡΠ΄Π½ΠΈΠΉ Π΄ΠΎΡΠ²ΡΠ΄:
- 3+ ΡΠΎΠΊΠΈ Π΄ΠΎΡΠ²ΡΠ΄Ρ ΡΠΎΠ±ΠΎΡΠΈ Π² ΡΠΎΠ»Ρ Data Scientist Π°Π±ΠΎ ML Engineer;
- ΠΠΏΠ΅Π²Π½Π΅Π½Π΅ Π·Π½Π°Π½Π½Ρ Python ΡΠ° Π±ΡΠ±Π»ΡΠΎΡΠ΅ΠΊ Π΄Π»Ρ Π°Π½Π°Π»ΡΠ·Ρ Π΄Π°Π½ΠΈΡ ΡΠ° ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ Π½Π°Π²ΡΠ°Π½Π½Ρ (Pandas, Scikit-learn, PyTorch Π°Π±ΠΎ TensorFlow);
- Π ΠΎΠ·ΡΠΌΡΠ½Π½Ρ Π°Π»Π³ΠΎΡΠΈΡΠΌΡΠ² ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΡΠΉΠ½ΠΈΡ ΡΠΈΡΡΠ΅ΠΌ: collaborative filtering, matrix factorization, content-based methods, hybrid models, RL;
- ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· NLP-ΠΏΡΠ΄Ρ ΠΎΠ΄Π°ΠΌΠΈ: embedding models, text classification, entity recognition, transformers;
- ΠΠΎΡΠ²ΡΠ΄ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Π½Ρ ΡΠΊΠ»Π°Π΄Π½ΠΈΡ EDA Π½Π° ΡΠ΅Π°Π»ΡΠ½ΠΈΡ Π΄Π°Π½ΠΈΡ ;
- ΠΠ°Π·ΠΎΠ²ΠΈΠΉ Π΄ΠΎΡΠ²ΡΠ΄ Π· MLOps-ΠΏΡΠ΄Ρ ΠΎΠ΄Π°ΠΌΠΈ;
- Π ΠΎΠ·ΡΠΌΡΠ½Π½Ρ A/B ΡΠ΅ΡΡΡΠ²Π°Π½Π½Ρ, ΠΏΠ΅ΡΠ΅Π²ΡΡΠΊΠΈ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ½ΠΈΡ Π³ΡΠΏΠΎΡΠ΅Π·;
- ΠΠΎΠ±ΡΠ΅ ΡΠΎΠ·ΡΠΌΡΠ½Π½Ρ SQL ΡΠ° Π°Π½Π°Π»ΡΠ·Π° Π΄Π°Π½ΠΈΡ : ΡΠΎΠ±ΠΎΡΠ° Π· Π±ΡΠ΄Ρ-ΡΠΊΠΈΠΌΠΈ Π΄ΠΆΠ΅ΡΠ΅Π»Π°ΠΌΠΈ Π΄Π°Π½ΠΈΡ (SQL, noSQL, Π²Π΅ΠΊΡΠΎΡΠ½Ρ Π±Π°Π·ΠΈ Π΄Π°Π½ΠΈΡ , column-oriented Π±Π°Π·ΠΈ Π΄Π°Π½ΠΈΡ , ΡΠΎΡΠΎ).
Π¨ΡΠΊΠ°ΡΠΌΠΎ Π»ΡΠ΄ΠΈΠ½Ρ, ΡΠΊΠ°:
- ΠΠ°Ρ Π±Π°ΠΆΠ°Π½Π½Ρ ΡΡΠ°Π²Π°ΡΠΈ ΡΠΈΠ»ΡΠ½ΡΡΠ΅ ΡΠ°Π·ΠΎΠΌ Π· ΠΊΠΎΠΌΠ°Π½Π΄ΠΎΡ;
- ΠΠ°Ρ RnD mindset: ΡΠΎΠ·ΡΠΌΡΠ½Π½Ρ ΠΊΠΎΠ»ΠΈ ΡΡΠ΅Π±Π° Π·ΡΠΎΠ±ΠΈΡΠΈ ΡΠ²ΠΈΠ΄ΠΊΠΎ Π΄Π»Ρ ΠΏΠ΅ΡΠ΅Π²ΡΡΠΊΠΈ Π³ΡΠΏΠΎΡΠ΅Π·ΠΈ Ρ ΠΊΠΎΠ»ΠΈ Π΄ΡΠΆΠ΅ ΡΠΊΡΡΠ½ΠΎ, Π±ΠΎ Π²ΡΠ΄ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ Π·Π°Π»Π΅ΠΆΠΈΡΡ ΠΊΠΎΡΠΈΡΡΡΠ²Π°ΡΡΠΊΠΈΠΉ Π΄ΠΎΡΠ²ΡΠ΄ ΡΠΈΡΡΡ Π»ΡΠ΄Π΅ΠΉ;
- ΠΠΎΡΠΎΠ²Π° Π²ΡΠ΄ΠΊΡΠΈΡΠΎ Π²ΠΈΡΠ»ΠΎΠ²Π»ΡΠ²Π°ΡΠΈ Π±ΡΠ΄Ρ-ΡΠΊΡ ΡΠ²ΠΎΡ Π΄ΡΠΌΠΊΠΈ.
ΠΡΠ΄Π΅ ΠΏΠ΅ΡΠ΅Π²Π°Π³ΠΎΡ:
- ΠΠΎΡΠ²ΡΠ΄ Π· Elasticsearch Π°Π±ΠΎ ΡΠ½ΡΠΈΠΌΠΈ ΠΏΠΎΡΡΠΊΠΎΠ²ΠΈΠΌΠΈ ΡΠΈΡΡΠ΅ΠΌΠ°ΠΌΠΈ;
- ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· Π°Π»Π³ΠΎΡΠΈΡΠΌΠ°ΠΌΠΈ Π³Π»ΠΈΠ±ΠΎΠΊΠΎΠ³ΠΎ Π½Π°Π²ΡΠ°Π½Π½Ρ: transformers, reinforcement learning, autoencoders, ΡΠΎΡΠΎ.
Π©ΠΎ ΠΌΠΈ ΠΏΡΠΎΠΏΠΎΠ½ΡΡΠΌΠΎ:
- Π ΠΎΠ±ΠΎΡΡ Π² ΡΡΠ°Π±ΡΠ»ΡΠ½ΡΠΉ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ β Π°Π΄ΠΆΠ΅ ΠΌΠΈ ΠΏΠΎΠ½Π°Π΄ 10 ΡΠΎΠΊΡΠ² Π½Π° ΡΠΈΠ½ΠΊΡ;
- ΠΡΠΉΡΠ½ΠΎ ΡΡΠΊΠ°Π²Ρ Π·Π°Π²Π΄Π°Π½Π½Ρ: Π±Π΅ΡΠΈ ΡΡΠ°ΡΡΡ Ρ ΡΡΠ²ΠΎΡΠ΅Π½Π½Ρ ΠΌΠ΅Π΄ΡΠ°ΡΠ΅ΡΠ²ΡΡΡ ΠΌΠ°ΠΉΠ±ΡΡΠ½ΡΠΎΠ³ΠΎ;
- ΠΡΠ΄Π½ΠΎΡΠΈΠ½ΠΈ, ΠΏΠΎΠ±ΡΠ΄ΠΎΠ²Π°Π½Ρ Π½Π° Π΄ΠΎΠ²ΡΡΡ;
- ΠΠ°Π³Π°ΡΠΎ ΠΌΠΎΠΆΠ»ΠΈΠ²ΠΎΡΡΠ΅ΠΉ Π΄Π»Ρ ΡΠΎΠ·Π²ΠΈΡΠΊΡ;
- ΠΠ΅ΠΉΠΌΠΎΠ²ΡΡΠ½ΠΎ ΠΊΡΡΡΡ ΠΊΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²ΠΈ;
- ΠΠ΅Π·ΠΊΠΎΡΡΠΎΠ²Π½Ρ ΡΡΠΎΠΊΠΈ Π°Π½Π³Π»ΡΠΉΡΡΠΊΠΎΡ ΠΌΠΎΠ²ΠΈ;
- ΠΠ°Π½ΡΡΡΡ Π· ΠΏΠ»Π°Π²Π°Π½Π½Ρ, Π° ΡΠ°ΠΊΠΎΠΆ ΡΡΠΎΠΊΠΈ Π½Π°ΡΡΠΎΠ»ΡΠ½ΠΎΠ³ΠΎ ΡΠ΅Π½ΡΡΡ;
- ΠΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΠΏΡΠΈΡ ΠΎΠ»ΠΎΠ³Π°;
ΠΠ»Ρ ΡΠΏΡΠ²ΡΠΎΠ±ΡΡΠ½ΠΈΠΊΡΠ² ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ Π·Π½ΠΈΠΆΠΊΠΈ Π²ΡΠ΄ Π±ΡΠ΅Π½Π΄ΡΠ² ΠΏΠ°ΡΡΠ½Π΅ΡΡΠ².
ΠΠΈ ΠΏΡΠ°Π³Π½Π΅ΠΌΠΎ Π±ΡΡΠΈ ΡΠΎΠ±ΠΎΡΠΎΠ΄Π°Π²ΡΠ΅ΠΌ, ΡΠΊΠΎΠ³ΠΎ ΠΎΠ±ΠΈΡΠ°ΡΡΡ.
ΠΡΠ΄Π΅ΠΌΠΎ Π²Π΄ΡΡΠ½Ρ, ΡΠΊΡΠΎ Π·Π°ΠΏΠΎΠ²Π½ΠΈΡ ΠΊΠΎΡΠΎΡΠΊΠ΅ ΠΎΠΏΠΈΡΡΠ²Π°Π½Π½Ρ ΠΏΡΠΎ ΡΠ΅, ΡΠΎ Π΄Π»Ρ ΡΠ΅Π±Π΅ Π²Π°ΠΆΠ»ΠΈΠ²ΠΎ. Π¦Π΅ Π΄ΠΎΠΏΠΎΠΌΠΎΠΆΠ΅ Π½Π°ΠΌ ΠΊΡΠ°ΡΠ΅ ΡΠΎΠ·ΡΠΌΡΡΠΈ ΠΎΡΡΠΊΡΠ²Π°Π½Π½Ρ ΠΊΠ°Π½Π΄ΠΈΠ΄Π°ΡΡΠ² Ρ ΡΡΠ²ΠΎΡΡΠ²Π°ΡΠΈ ΡΠ΅ Π±ΡΠ»ΡΡ ΠΊΠΎΠΌΡΠΎΡΡΠ½Π΅ ΡΠ΅ΡΠ΅Π΄ΠΎΠ²ΠΈΡΠ΅ Π² MEGOGO.
ΠΠΎΡΠΈΠ»Π°Π½Π½Ρ ΡΡΡ - https://bit.ly/43YaxBH
ΠΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π°ΡΡΠΈ Π½Π° Π²Π°ΠΊΠ°Π½ΡΡΡ Ρ Π½Π°Π΄ΡΡΠ»Π°Π²ΡΠΈ ΡΠ²ΠΎΡ ΡΠ΅Π·ΡΠΌΠ΅ Π² ΠΠΎΠΌΠΏΠ°Π½ΡΡ (Π’ΠΠ Β«ΠΠΠΠΠΠΒ»), Π·Π°ΡΠ΅ΡΡΡΡΠΎΠ²Π°Π½Ρ ΠΉ Π΄ΡΡΡΡ Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π½ΠΎ Π΄ΠΎ Π·Π°ΠΊΠΎΠ½ΠΎΠ΄Π°Π²ΡΡΠ²Π° Π£ΠΊΡΠ°ΡΠ½ΠΈ, ΡΠ΅ΡΡΡΡΠ°ΡΡΠΉΠ½ΠΈΠΉ Π½ΠΎΠΌΠ΅Ρ 38347009, Π°Π΄ΡΠ΅ΡΠ°: Π£ΠΊΡΠ°ΡΠ½Π°, 01011, ΠΌΡΡΡΠΎ ΠΠΈΡΠ², Π²ΡΠ».Π ΠΈΠ±Π°Π»ΡΡΡΠΊΠ°, Π±ΡΠ΄ΠΈΠ½ΠΎΠΊ 22 (Π΄Π°Π»Ρ Β«ΠΠΎΠΌΠΏΠ°Π½ΡΡΒ»), Π²ΠΈ ΠΏΡΠ΄ΡΠ²Π΅ΡΠ΄ΠΆΡΡΡΠ΅ ΡΠ° ΠΏΠΎΠ³ΠΎΠ΄ΠΆΡΡΡΠ΅ΡΡ Π· ΡΠΈΠΌ, ΡΠΎ ΠΠΎΠΌΠΏΠ°Π½ΡΡ ΠΎΠ±ΡΠΎΠ±Π»ΡΡ Π²Π°ΡΡ ΠΎΡΠΎΠ±ΠΈΡΡΡ Π΄Π°Π½Ρ, ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ Ρ Π²Π°ΡΠΎΠΌΡ ΡΠ΅Π·ΡΠΌΠ΅, Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π½ΠΎ Π΄ΠΎ ΠΠ°ΠΊΠΎΠ½Ρ Π£ΠΊΡΠ°ΡΠ½ΠΈ Β«ΠΡΠΎ Π·Π°Ρ ΠΈΡΡ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΡΠ½ΠΈΡ Π΄Π°Π½ΠΈΡ Β» ΡΠ° ΠΏΡΠ°Π²ΠΈΠ» GDPR.
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Data Scientist
Office Work Β· Ukraine (Kyiv) Β· Product Β· 3 years of experienceΠΡΡΠ°ΡΠΌΠΎ Π² King Group ΠΌΡΡΡΡ, Π΄Π΅ Π·ΡΡΡΡΡΡΠ°ΡΡΡΡΡ Π½Π°ΠΉΠΊΡΠ°ΡΡ Π»ΡΠ΄ΠΈ Π· IT- ΡΠ° Π³Π΅ΠΌΠ±Π»ΡΠ½Π³-ΡΠ½Π΄ΡΡΡΡΡΡ, ΡΠΎΠ± ΡΠ°Π·ΠΎΠΌ ΡΠΎΠ±ΠΈΡΠΈ Π΄ΠΈΠ²ΠΎΠ²ΠΈΠΆΠ½Ρ ΡΠ΅ΡΡ. ΠΠΈ ΠΎΠΏΠ΅ΡΡΡΠΌΠΎ ΡΠΈΡΠ»Π΅Π½Π½ΠΈΠΌΠΈ ΠΏΡΠΎΡΠΊΡΠ°ΠΌΠΈ Ρ ΡΡΠ΅ΡΡ iGaming Π½Π° ΡΠΈΠ½ΠΊΠ°Ρ Π£ΠΊΡΠ°ΡΠ½ΠΈ, ΠΠ²ΡΠΎΠΏΠΈ ΡΠ° Π‘Π¨Π, ΡΠ½Π²Π΅ΡΡΡΡΠΌΠΎ Ρ Π²Π΅Π½ΡΡΡΠ½Ρ ΡΡΠ°ΡΡΠ°ΠΏΠΈ, ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Π½Ρ ΡΠ΄Π΅Ρ...ΠΡΡΠ°ΡΠΌΠΎ Π² King Group γΌ ΠΌΡΡΡΡ, Π΄Π΅ Π·ΡΡΡΡΡΡΠ°ΡΡΡΡΡ Π½Π°ΠΉΠΊΡΠ°ΡΡ Π»ΡΠ΄ΠΈ Π· IT- ΡΠ° Π³Π΅ΠΌΠ±Π»ΡΠ½Π³-ΡΠ½Π΄ΡΡΡΡΡΡ, ΡΠΎΠ± ΡΠ°Π·ΠΎΠΌ ΡΠΎΠ±ΠΈΡΠΈ Π΄ΠΈΠ²ΠΎΠ²ΠΈΠΆΠ½Ρ ΡΠ΅ΡΡ. ΠΠΈ ΠΎΠΏΠ΅ΡΡΡΠΌΠΎ ΡΠΈΡΠ»Π΅Π½Π½ΠΈΠΌΠΈ ΠΏΡΠΎΡΠΊΡΠ°ΠΌΠΈ Ρ ΡΡΠ΅ΡΡ iGaming Π½Π° ΡΠΈΠ½ΠΊΠ°Ρ Π£ΠΊΡΠ°ΡΠ½ΠΈ, ΠΠ²ΡΠΎΠΏΠΈ ΡΠ° Π‘Π¨Π, ΡΠ½Π²Π΅ΡΡΡΡΠΌΠΎ Ρ Π²Π΅Π½ΡΡΡΠ½Ρ ΡΡΠ°ΡΡΠ°ΠΏΠΈ, ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Π½Ρ ΡΠ΄Π΅Ρ ΡΠ° Π»ΡΠ΄Π΅ΠΉ.
ΠΠΈ Π°ΠΊΡΠΈΠ²Π½ΠΎ Π·ΡΠΎΡΡΠ°ΡΠΌΠΎ ΡΠ° ΡΠΎΠ·ΡΠΈΡΡΡΠΌΠΎΡΡ, ΡΡΠΏΡΡΠ½ΠΎ Π·Π°ΠΏΡΡΡΠΈΠ²ΡΠΈ ΡΠ° ΡΠΎΠ·ΡΠΈΡΠΈΠ²ΡΠΈ Π½ΠΈΠ·ΠΊΡ Π½ΠΎΠ²ΠΈΡ ΠΏΡΠΎΠ΄ΡΠΊΡΡΠ² ΠΏΡΠΎΡΡΠ³ΠΎΠΌ ΠΎΡΡΠ°Π½Π½ΡΠΎΠ³ΠΎ ΡΠΎΠΊΡ.
ΠΠ°ΡΠ°Π·Ρ ΠΌΠΈ Ρ ΠΏΠΎΡΡΠΊΠ°Ρ Data Scientist, ΡΠΎ Π΄ΠΎΡΠ΄Π½Π°ΡΡΡΡΡ ΡΠ° ΠΏΡΠ΄ΡΠΈΠ»ΠΈΡΡ Π½Π°ΡΡ Analytics & Insights ΠΊΠΎΠΌΠ°Π½Π΄Ρ.
ΠΡΠ½ΠΎΠ²Π½Ρ Π²ΠΈΠΌΠΎΠ³ΠΈ:
β Π‘ΡΡΠΏΡΠ½Ρ Π±Π°ΠΊΠ°Π»Π°Π²ΡΠ°/ΠΌΠ°Π³ΡΡΡΡΠ° Π°Π±ΠΎ Π΅ΠΊΠ²ΡΠ²Π°Π»Π΅Π½ΡΠ½ΠΈΠΉ Π΄ΠΎΡΠ²ΡΠ΄ Ρ Π³Π°Π»ΡΠ·Ρ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΠΊΠΈ, ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠΈ, ΡΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΊΠΈ ΡΠΈ ΡΡΠΌΡΠΆΠ½ΠΈΡ Π³Π°Π»ΡΠ·Π΅ΠΉ;
β ΠΠ»ΠΈΠ±ΠΎΠΊΡ Π·Π½Π°Π½Π½Ρ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΠΊΠΈ, ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠΈ ΡΠ° ΡΠ΅ΠΎΡΡΡ ΠΉΠΌΠΎΠ²ΡΡΠ½ΠΎΡΡΠ΅ΠΉ;
β ΠΠ°Π²ΠΈΡΠΊΠΈ ΠΏΡΠΎΠ³ΡΠ°ΠΌΡΠ²Π°Π½Π½Ρ Π½Π° Python;
β ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· Π±ΡΠ±Π»ΡΠΎΡΠ΅ΠΊΠ°ΠΌΠΈ: Pandas, Numpy, Scipy, Scikit-learn, Ρ ΡΠ΄.;
β ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ ΡΠ· SQL;
β ΠΠ»ΠΈΠ±ΠΎΠΊΠ΅ ΡΠΎΠ·ΡΠΌΡΠ½Π½Ρ ΠΊΠ»Π°ΡΠΈΡΠ½ΠΈΡ ML Π°Π»Π³ΠΎΡΠΈΡΠΌΡΠ²: Clustering, Logistic Regression, Decision Trees, Random Forest, Boostings.ΠΠ΄Π½ΠΎΠ·Π½Π°ΡΠ½ΠΎ Π±ΡΠ΄Π΅ Π²Π΅Π»ΠΈΠΊΠΎΡ ΠΏΠ΅ΡΠ΅Π²Π°Π³ΠΎΡ:
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β ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· Time Series modeling;
β ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ DL framework: TensorFlow, PyTorch (+ CUDA);
β ΠΠΎΡΠ²ΡΠ΄ Π· GCP cloud: BigQuery, Cloud Functions.
Π’ΠΎΠ±Ρ ΡΠΎΡΠ½ΠΎ Π΄ΠΎ Π½Π°Ρ, ΡΠΊΡΠΎ ΡΠΈ:
β ΠΠΎΠ»ΠΎΠ΄ΡΡΡ Π²ΡΠ΄ΠΌΡΠ½Π½ΠΈΠΌΠΈ Π°Π½Π°Π»ΡΡΠΈΡΠ½ΠΈΠΌΠΈ Π½Π°Π²ΠΈΡΠΊΠ°ΠΌΠΈ ΡΠ° ΠΊΡΠΈΡΠΈΡΠ½ΠΈΠΌ ΠΌΠΈΡΠ»Π΅Π½Π½ΡΠΌ;
β ΠΠ°ΡΡ ΡΠΈΠ»ΡΠ½Ρ ΠΎΡΠ³Π°Π½ΡΠ·Π°ΡΠΎΡΡΡΠΊΡ Π·Π΄ΡΠ±Π½ΠΎΡΡΡ;
β Π£Π²Π°ΠΆΠ½ΠΈΠΉ Π΄ΠΎ Π΄Π΅ΡΠ°Π»Π΅ΠΉ, Π° ΡΠ°ΠΊΠΎΠΆ ΠΌΠ°ΡΡ Π½Π°Π²ΠΈΡΠΊΠΈ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠ³ΠΎ Π²ΠΈΡΡΡΠ΅Π½Π½Ρ ΠΏΡΠΎΠ±Π»Π΅ΠΌ, ΡΠΏΡΠ°Π²Π»ΡΠ½Π½Ρ ΡΠ°ΡΠΎΠΌ Ρ Π»ΠΎΠ³ΡΠΊΠΈ;
β ΠΠ°ΡΡ ΠΆΠ°Π³Ρ Π΄ΠΎ ΡΡΠ·Π½ΠΎΠ³ΠΎ ΡΠΎΠ΄Ρ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Ρ ΡΠ° ΡΠ°ΠΌΠΎΠ²Π΄ΠΎΡΠΊΠΎΠ½Π°Π»Π΅Π½Π½Ρ;
β ΠΠΎΠ»ΠΎΠ΄ΡΡΡ Π½Π°Π²ΠΈΡΠΊΠ°ΠΌΠΈ Π΅ΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡ ΠΊΠΎΠΌΡΠ½ΡΠΊΠ°ΡΡΡ, Π·Π΄Π°ΡΠ½ΠΈΠΉ ΡΡΡΠΊΠΎ ΡΠ° Π»Π°ΠΊΠΎΠ½ΡΡΠ½ΠΎ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΡΠΈ Π΄Π°Π½Ρ ΠΊΠΎΠΌΠ°Π½Π΄Π°ΠΌ ΡΠ° Π·Π°ΡΡΠΊΠ°Π²Π»Π΅Π½ΠΈΠΌ ΡΡΠΎΡΠΎΠ½Π°ΠΌ.
ΠΠΈ ΠΏΡΠΎΠΏΠΎΠ½ΡΡΠΌΠΎ:
β ΠΡΠ΄ΡΡΡΠ½ΡΡΡΡ Π±ΡΡΠΎΠΊΡΠ°ΡΡΡ Π² ΠΏΡΠΎΡΠ΅ΡΠ°Ρ ΠΏΡΠΈΠΉΠ½ΡΡΡΡ ΡΡΡΠ΅Π½Ρ Ρ ΠΌΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ Π±Π΅Π·ΠΏΠΎΡΠ΅ΡΠ΅Π΄Π½ΡΠΎ Π²ΠΏΠ»ΠΈΠ²Π°ΡΠΈ Π½Π° ΠΏΡΠΎΠ΄ΡΠΊΡ/ΠΏΡΠΎΡΠΊΡ;
β ΠΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ Π½Π°Π²ΡΠ°ΡΠΈΡΡ β Π°Π±ΠΎ Π½Π°Π²ΡΠ°ΡΠΈ (ΠΌΠ°ΡΠΌΠΎ ΠΏΡΠΎΡΠΊΡΠΈ Π· ΡΠ½ΡΠ΅ΡΠ½Π°ΡΡΡΠΈ ΡΠ° ΠΌΠ΅Π½ΡΠΎΡΡΡΠ²Π°);
β Π Π΅Π°Π»ΡΠ·Π°ΡΡΡ ΡΠ΄Π΅ΠΉ ΡΠ΅ΡΠ΅Π· Π²Π»Π°ΡΠ½Ρ ΠΏΡΠΎΡΠΊΡΠΈ;
β ΠΠ΅ Π±ΡΠΉΡΠ΅ΡΡ Π΅ΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΡΠ²Π°ΡΠΈ! ΠΡΠΎΠΏΠΎΠ½ΡΠΉΡΠ΅ ΡΠ° ΠΎΠ²Π½Π΅ΡΡΡΡ ΠΏΡΠΎΡΠ΅Ρ ΡΠ΅Π°Π»ΡΠ·Π°ΡΡΡ;
β ΠΡΠ΄ΡΡΠΈΠΌΡΡΡΠ΅ ΡΠ΅ΡΠ΅Π΄ΠΎΠ²ΠΈΡΠ΅ ΡΠ° ΠΊΠΎΠΌΠ°Π½Π΄Π°, ΡΠ· ΡΠΊΠΎΡ ΠΌΠΎΠΆΠ½Π° ΡΠΎΠ±ΠΈΡΠΈ Π΄ΡΠΉΡΠ½ΠΎ ΠΊΡΡΡΡ ΠΏΡΠΎΠ΄ΡΠΊΡΠΈ, ΡΠΎ Π·ΠΌΡΠ½ΡΡΡΡ ΡΠΈΠ½ΠΎΠΊ;
β ΠΠ°ΡΠΏΠ»Π°ΡΡ ΡΡΠ²Π½Ρ IT-/iGaming-ΡΠΈΠ½ΠΊΡ ΡΠ° ΠΏΠΎΠ²Π½ΠΈΠΉ ΡΠΎΡΠΏΠ°ΠΊΠ΅Ρ (ΠΌΠ΅Π΄ΠΈΡΠ½Π° ΡΡΡΠ°Ρ ΠΎΠ²ΠΊΠ°, ΠΊΠΎΠ½ΡΡΠ»ΡΡΠ°ΡΡΡ ΡΠ΅ΡΠ°ΠΏΠ΅Π²ΡΠ° Π² ΠΎΡΡΡΡ, ΠΊΠΎΠΌΠΏΠ΅Π½ΡΠ°ΡΡΡ ΡΠΏΠΎΡΡΠ·Π°Π»Ρ, ΠΊΠΎΠΌΠΏΠ΅Π½ΡΠ°ΡΡΡ Π²Π°ΡΡΠΎΡΡΡ Π»Π°Π½ΡΡΠ² Π· Π΄ΠΎΡΡΠ°Π²ΠΊΠΎΡ ΡΠΎΡΠΎ);
β ΠΡΡΡΠ½ΠΈΠΉ ΠΎΡΡΡ Ρ ΡΠ΅Π½ΡΡΡ ΠΠΈΡΠ²Π° (ΠΏΡΡΠΊΠΈ Π·Ρ ΠΠ²ΡΡΠΈΠ½Π΅ΡΡΠΊΠΎΡ/ΠΠΈΠ±ΡΠ΄ΡΡΠΊΠΎΡ) ΡΠ· Π·Π΅Π»Π΅Π½ΠΎΡ ΠΏΠ°Π½ΠΎΡΠ°ΠΌΠ½ΠΎΡ ΡΠ΅ΡΠ°ΡΠΎΡ. ΠΡΠΎΠ±Π»Π΅ΠΌΠ° Π±Π»Π΅ΠΊΠ°ΡΡΡΠ² Π²ΠΈΡΡΡΠ΅Π½Π° Π½Π° 100%;
β ΠΡΠ΄ΠΏΡΡΡΠΊΠ° - Ρ ΡΠ΅Π±Π΅ Π±ΡΠ΄Π΅ ΠΎΠΏΠ»Π°ΡΡΠ²Π°Π½Π° Π²ΡΠ΄ΠΏΡΡΡΠΊΠΈ ΡΠ° Π΅ΠΊΡΡΡΠ°Π²ΠΈΡ ΡΠ΄Π½Ρ - Π² Π΅ΠΊΡΡΡΠ°Π΄Π½Ρ Π½Π°Π΄Π°ΡΠΌΠΎ Π΅ΠΊΡΡΡΠ°Π²ΠΈΡ ΡΠ΄Π½Ρ Π½Π°: ΠΎΠ΄ΡΡΠΆΠ΅Π½Π½Ρ, Π½Π°ΡΠΎΠ΄ΠΆΠ΅Π½Π½Ρ Π΄ΠΈΡΠΈΠ½ΠΈ, Π½Π΅ΠΏΠ΅ΡΠ΅Π΄Π±Π°ΡΡΠ²Π°Π½Ρ ΠΏΠΎΠ΄ΡΡ ΡΠ° ΡΠ½ΡΠ΅;
β ΠΠΎΠ½ΡΡ Π·Π° ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΡΡ - ΠΠΈ Π·Π°Π²ΠΆΠ΄ΠΈ ΡΠ°Π΄ΡΡΠΌΠΎ ΡΠ° ΡΡΠ½ΡΡΠΌΠΎ ΡΠ΅, ΡΠΎ ΡΡΠΌΠΌΠ΅ΠΉΡΠΈ ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄ΡΡΡΡ ΡΠ²ΠΎΡΡ Π΄ΡΡΠ·ΡΠ², ΡΠΎΠΌΡ Π΄ΠΎ ΠΏΠ»ΡΡΡΠ² ΡΠΎΠ±ΠΎΡΠΈ Π· ΠΏΠ΅ΡΠ΅Π²ΡΡΠ΅Π½ΠΎΡ ΡΠ° Π½Π°Π΄ΡΠΉΠ½ΠΎΡ Π»ΡΠ΄ΠΈΠ½ΠΎΡ ΠΌΠΈ Π΄ΠΎΠ΄Π°ΡΠΌΠΎ Π±ΠΎΠ½ΡΡ;
β Π Π΅Π»ΠΎΠΊΠ΅ΠΉΡ - Π·ΠΌΡΠ½Π° ΠΌΡΡΡΠ° ΠΏΡΠΎΠΆΠΈΠ²Π°Π½Π½Ρ Π·Π°Π²ΠΆΠ΄ΠΈ ΡΠΏΠΎΠ½ΡΠΊΠ°Ρ Π΄ΠΎ Π΄ΠΎΠ΄Π°ΡΠΊΠΎΠ²ΠΈΡ Π²ΠΈΡΡΠ°Ρ, Π° Π½Π°Ρ Π±ΠΎΠ½ΡΡ Π΄ΠΎΠΏΠΎΠΌΠ°Π³Π°Ρ ΠΏΡΠΎΠΉΡΠΈ ΡΠ΅ΠΉ ΠΏΠ΅ΡΡΠΎΠ΄ Π±Π΅Π· Π·Π°ΠΉΠ²ΠΈΡ ΡΡΡΠ΅ΡΡΠ².
Π―ΠΊΡΠΎ ΡΠΈ ΡΡΠΊΠ°ΡΡ Π΄Π»Ρ ΡΠ΅Π±Π΅ ΡΡΠ°Π±ΡΠ»ΡΠ½Ρ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ Π· ΠΊΠ»Π°ΡΠ½ΠΈΠΌΠΈ Π»ΡΠ΄ΡΠΌΠΈ ΡΠ° ΠΌΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ ΡΠΎΡΡΡ - ΡΠΎΠ±Ρ Π΄ΠΎ Π½Π°Ρ! ΠΡΠ΄ΠΏΡΠ°Π²Π»ΡΠΉ ΡΠ΅Π·ΡΠΌΠ΅! -
Β· 12 views Β· 0 applications Β· 1d
Game Mathematician to $3000
Hybrid Remote Β· Poland Β· Product Β· 3 years of experience Β· IntermediateΠ£ NeverEnding ΠΌΠΈ ΡΡΠ²ΠΎΡΡΡΠΌΠΎ ΡΠ³ΡΠΈ, ΡΠΊΡ Π΄ΡΠΉΡΠ½ΠΎ βΠ·Π°ΡΡΠ³ΡΡΡΡβ β Ρ Π·Π°ΡΠ°Π· ΡΡΠΊΠ°ΡΠΌΠΎ Game Mathematicianβa, ΡΠΊΠΈΠΉ Π΄ΠΎΠΏΠΎΠΌΠΎΠΆΠ΅ Π½Π°ΠΌ ΡΠΎΠ·ΡΠΎΠ±ΠΈΡΠΈ ΠΌΠ΅Ρ Π°Π½ΡΠΊΠΈ, ΡΠΎ ΠΏΡΠΈΠ½ΠΎΡΡΡΡ Π·Π°Π΄ΠΎΠ²ΠΎΠ»Π΅Π½Π½Ρ Π³ΡΠ°Π²ΡΡ ΠΉ Π΄ΠΎΡ ΡΠ΄ Π±ΡΠ·Π½Π΅ΡΡ. Π¦Π΅ ΡΠΎΠ»Ρ Π΄Π»Ρ ΡΠΈΡ , Ρ ΡΠΎ Ρ ΠΎΡΠ΅ Π±ΡΠ΄ΡΠ²Π°ΡΠΈ Π½ΠΎΠ²Ρ ΡΠ³ΡΠΈ Π· Π½ΡΠ»Ρ, ΠΏΡΠ°ΡΡΠ²Π°ΡΠΈ Π·...Π£ NeverEnding ΠΌΠΈ ΡΡΠ²ΠΎΡΡΡΠΌΠΎ ΡΠ³ΡΠΈ, ΡΠΊΡ Π΄ΡΠΉΡΠ½ΠΎ βΠ·Π°ΡΡΠ³ΡΡΡΡβ β Ρ Π·Π°ΡΠ°Π· ΡΡΠΊΠ°ΡΠΌΠΎ Game Mathematicianβa, ΡΠΊΠΈΠΉ Π΄ΠΎΠΏΠΎΠΌΠΎΠΆΠ΅ Π½Π°ΠΌ ΡΠΎΠ·ΡΠΎΠ±ΠΈΡΠΈ ΠΌΠ΅Ρ Π°Π½ΡΠΊΠΈ, ΡΠΎ ΠΏΡΠΈΠ½ΠΎΡΡΡΡ Π·Π°Π΄ΠΎΠ²ΠΎΠ»Π΅Π½Π½Ρ Π³ΡΠ°Π²ΡΡ ΠΉ Π΄ΠΎΡ ΡΠ΄ Π±ΡΠ·Π½Π΅ΡΡ.
Π¦Π΅ ΡΠΎΠ»Ρ Π΄Π»Ρ ΡΠΈΡ , Ρ ΡΠΎ Ρ ΠΎΡΠ΅ Π±ΡΠ΄ΡΠ²Π°ΡΠΈ Π½ΠΎΠ²Ρ ΡΠ³ΡΠΈ Π· Π½ΡΠ»Ρ, ΠΏΡΠ°ΡΡΠ²Π°ΡΠΈ Π· ΡΡΠ·Π½ΠΈΠΌΠΈ ΠΆΠ°Π½ΡΠ°ΠΌΠΈ (ΡΠ»ΠΎΡΠΈ, ΡΠ½ΡΡΠ°Π½Ρ, crash) ΡΠ° Π²ΠΏΠ»ΠΈΠ²Π°ΡΠΈ Π½Π° ΠΊΠΎΠΆΠ½Ρ ΡΠΈΡΡΡ, ΡΠΊΠ° Π·βΡΠ²Π»ΡΡΡΡΡΡ Ρ Π³ΡΡ.
Π©ΠΎ ΡΠΎΠ±ΠΈΡΠΈ:
- ΠΡΠΎΡΠΊΡΡΠ²Π°ΡΠΈ ΡΠ³ΡΠΎΠ²Ρ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΠΊΡ: RTP, volatilities, hit frequency, distribution curves.
- Π ΠΎΠ·ΡΠΎΠ±Π»ΡΡΠΈ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ½Ρ ΠΌΠΎΠ΄Π΅Π»Ρ Π΄Π»Ρ ΡΠ»ΠΎΡΡΠ², instant- ΡΠ° crash-ΡΠ³ΠΎΡ.
- ΠΠΈΡΠ°ΡΠΈ ΡΠΈΠΌΡΠ»ΡΡΡΡ ΡΠ° Π°Π½Π°Π»ΡΠ·ΡΠ²Π°ΡΠΈ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΈ Π΄Π»Ρ Π±Π°Π»Π°Π½ΡΡΠ²Π°Π½Π½Ρ.
- ΠΡΠ°ΡΡΠ²Π°ΡΠΈ ΡΠ°Π·ΠΎΠΌ ΡΠ· Π³Π΅ΠΉΠΌ-Π΄ΠΈΠ·Π°ΠΉΠ½Π΅ΡΠ°ΠΌΠΈ, Ρ ΡΠ΄ΠΎΠΆΠ½ΠΈΠΊΠ°ΠΌΠΈ, ΠΏΡΠΎΠ΄Π°ΠΊΡΠ°ΠΌΠΈ ΡΠ° Π΄Π΅Π²Π°ΠΌΠΈ Π΄Π»Ρ ΡΡΠ²ΠΎΡΠ΅Π½Π½Ρ ΡΡΠ»ΡΡΠ½ΠΎΠ³ΠΎ ΡΠ³ΡΠΎΠ²ΠΎΠ³ΠΎ Π΄ΠΎΡΠ²ΡΠ΄Ρ.
- ΠΠΈΠ·Π½Π°ΡΠ°ΡΠΈ ΡΠ΅Ρ Π½ΡΡΠ½Ρ ΠΎΠ±ΠΌΠ΅ΠΆΠ΅Π½Π½Ρ, ΡΠΈΠ·ΠΈΠΊΠΈ, ΡΠ° Π·Π½Π°Ρ ΠΎΠ΄ΠΈΡΠΈ ΡΡΡΠ΅Π½Π½Ρ Π½Π° Π΅ΡΠ°ΠΏΡ ΠΊΠΎΠ½ΡΠ΅ΠΏΡΡ.
ΠΠΎΠ³ΠΎ ΠΌΠΈ ΡΡΠΊΠ°ΡΠΌΠΎ:
- 3+ ΡΠΎΠΊΠΈ Π΄ΠΎΡΠ²ΡΠ΄Ρ Π² ΡΠ³ΡΠΎΠ²ΡΠΉ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΡ Π°Π±ΠΎ Π³Π΅ΠΉΠΌ-Π΄ΠΈΠ·Π°ΠΉΠ½Ρ Π· ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ½ΠΈΠΌ ΡΡ ΠΈΠ»ΠΎΠΌ.
- ΠΠ»ΠΈΠ±ΠΎΠΊΠ΅ ΡΠΎΠ·ΡΠΌΡΠ½Π½Ρ ΡΠ»ΠΎΡ-ΠΌΠ΅Ρ Π°Π½ΡΠΊ, RTP, volatilities, bonus systems.
- ΠΠΏΠ΅Π²Π½Π΅Π½Π΅ Π²ΠΎΠ»ΠΎΠ΄ΡΠ½Π½Ρ Excel / Google Sheets, Python Π°Π±ΠΎ ΡΠ½ΡΠΈΠΌ ΡΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠΎΠΌ Π΄Π»Ρ ΡΠΈΠΌΡΠ»ΡΡΡΠΉ.
- ΠΠΎΡΠ²ΡΠ΄ ΡΠ· Π±Π°Π»Π°Π½ΡΡΠ²Π°Π½Π½ΡΠΌ Payout Tables Ρ Free Spins - Π²Π΅Π»ΠΈΠΊΠΈΠΉ ΠΏΠ»ΡΡ.
- ΠΠ½Π°Π»ΡΡΠΈΡΠ½Π΅ ΠΌΠΈΡΠ»Π΅Π½Π½Ρ, ΡΠ²Π°ΠΆΠ½ΡΡΡΡ Π΄ΠΎ Π΄Π΅ΡΠ°Π»Π΅ΠΉ, Π·Π΄Π°ΡΠ½ΡΡΡΡ ΠΎΠΏΡΠΈΠΌΡΠ·ΡΠ²Π°ΡΠΈ ΡΠΊΠ»Π°Π΄Π½Π΅.
ΠΠΈ ΡΡΠΊΠ°ΡΠΌΠΎ Π»ΡΠ΄ΠΈΠ½Ρ, ΡΠΊΠ°:
- ΠΡΠΌΠ°Ρ ΡΠΊ Π³ΡΠ°Π²Π΅ΡΡ Ρ ΠΌΠΈΡΠ»ΠΈΡΡ ΡΠΊ Π°Π½Π°Π»ΡΡΠΈΠΊ.
- Π£ΠΌΡΡ Π±ΡΠ΄ΡΠ²Π°ΡΠΈ ΠΌΠΎΠ΄Π΅Π»Ρ, ΡΠΎ Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π°ΡΡΡ ΡΠΊ Π³Π΅ΠΉΠΌΠΏΠ»Π΅ΠΉΠ½ΠΈΠΌ, ΡΠ°ΠΊ Ρ Π±ΡΠ·Π½Π΅Ρ-ΡΡΠ»ΡΠΌ.
- Π₯ΠΎΡΠ΅ ΠΏΡΠ°ΡΡΠ²Π°ΡΠΈ Π· Π½ΠΎΠ²ΠΈΠΌΠΈ ΡΠ΄Π΅ΡΠΌΠΈ, Π° Π½Π΅ ΡΡΠ°ΠΌΠΏΡΠ²Π°ΡΠΈ ΡΠΈΠΏΠΎΠ²Ρ ΡΡΡΡ.
- ΠΡΠ±ΠΈΡΡ ΡΠΈΡΡΠΈ, Π΅ΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠΈ Ρ ΡΠ΅Π»Π΅Π½Π΄ΠΆΡ.
Π§ΠΎΠΌΡ Π²Π°ΡΡΠΎ ΠΏΡΠΈΡΠ΄Π½Π°ΡΠΈΡΡ Π΄ΠΎ NeverEnding?
- Π ΠΎΠ±ΠΎΡΠ° Π· Π½ΡΠ»Ρ Π½Π°Π΄ ΠΏΠ΅ΡΡΠΈΠΌΠΈ ΡΠ³ΡΠ°ΠΌΠΈ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ.
- ΠΠΏΠ»ΠΈΠ² Π½Π° ΡΠΎΡΠΌΡΠ²Π°Π½Π½Ρ ΡΠ³ΡΠΎΠ²ΠΎΡ Π»ΡΠ½ΡΠΉΠΊΠΈ ΠΉ math-Π½Π°ΠΏΡΡΠΌΡ Π² ΠΊΠΎΠΌΠ°Π½Π΄Ρ.
- ΠΡΡΠΌΠ° ΡΠΏΡΠ²ΠΏΡΠ°ΡΡ Π· ΡΠ°ΡΠ½Π΄Π΅ΡΠ°ΠΌΠΈ ΡΠ° ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ²ΠΎΡ ΠΊΠΎΠΌΠ°Π½Π΄ΠΎΡ.
- ΠΠ½ΡΡΠΊΡΡΡΡ, ΡΠ²ΠΈΠ΄ΠΊΡΡΡΡ Ρ Π²ΡΠ΄ΠΊΡΠΈΡΠ΅ ΠΏΠΎΠ»Π΅ Π΄Π»Ρ ΡΠ²ΠΎΡΡΠΎΡΡΡ.
- Π‘ΡΠ°ΡΡΠ°ΠΏ ΡΠ· Π°ΠΌΠ±ΡΡΡΡΡ ΡΡΠ°ΡΠΈ ΠΏΡΠΎΠ²Π°ΠΉΠ΄Π΅ΡΠΎΠΌ Π½ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΠΊΠΎΠ»ΡΠ½Π½Ρ.
π Π€ΠΎΡΠΌΠ°Ρ: Remote / Hybrid β Π½Π° ΡΠ²ΡΠΉ Π²ΠΈΠ±ΡΡ
π Π€ΠΎΡΠΌΠ°Ρ: ΠΠΎΠ²Π½Π° Π·Π°ΠΉΠ½ΡΡΡΡΡΡ | 40 Π³ΠΎΠ΄/ΡΠΈΠΆΠ΄Π΅Π½Ρ | 5/2
π° ΠΠ°ΡΠΏΠ»Π°ΡΠ°: ΠΠΎΠ½ΠΊΡΡΠ΅Π½ΡΠ½Π° β ΠΎΠ±Π³ΠΎΠ²ΠΎΡΡΡΡΡΡΡ
Π―ΠΊΡΠΎ ΡΠΈ Ρ ΠΎΡΠ΅Ρ ΡΡΠ²ΠΎΡΡΠ²Π°ΡΠΈ Π½ΠΎΠ²Ρ ΡΠ²ΡΡΠΈ ΡΠ΅ΡΠ΅Π· ΡΠΎΡΠΌΡΠ»ΠΈ ΡΠ° ΡΠΈΡΡΠΈ β ΠΏΡΠΈΡΠ΄Π½ΡΠΉΡΡ.
NeverEnding ΡΡΠ»ΡΠΊΠΈ ΠΏΠΎΡΠΈΠ½Π°ΡΡΡΡΡ.
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