Jobs
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Β· 24 views Β· 5 applications Β· 7d
Azure Data/ Machine Learning Expert (Microsoft Certified DP-100 / DP-203)
Full Remote Β· Countries of Europe or Ukraine Β· 3 years of experience Β· IntermediateWeβre building a team of certified data professionals to work on high-impact projects in the areas of data engineering and machine learning using Microsoft Azure. If you are certified in one or more of the following areas, letβs connect! Required...Weβre building a team of certified data professionals to work on high-impact projects in the areas of data engineering and machine learning using Microsoft Azure. If you are certified in one or more of the following areas, letβs connect!
Required Certifications:
- DP-100 β Microsoft Certified: Azure Data Scientist Associate
- DP-203 β Microsoft Certified: Azure Data Engineer Associate
What you'll be doing:
- Designing and developing data pipelines and ML models on Azure
- Working with large datasets and building scalable solutions
- Collaborating with engineering and business teams to implement end-to-end data solutions
- Supporting proof of concept and prototype development
What we expect:
- Valid certification(s) in DP-100 or DP-203
- Practical experience with Azure Machine Learning, Synapse, Data Factory, or related tools
- Strong analytical skills and understanding of data modeling and ML workflows
- English β Intermediate level or higher
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Β· 23 views Β· 4 applications Β· 6d
Senior Data Scientist
Full Remote Β· EU Β· 5 years of experience Β· Upper-IntermediateUVIK Software is looking for an experienced and proactive Senior Data Scientist to join our team. You will take ownership of end-to-end machine learning solutions β from exploration to deployment β and turn complex business challenges into actionable...UVIK Software is looking for an experienced and proactive Senior Data Scientist to join our team. You will take ownership of end-to-end machine learning solutions β from exploration to deployment β and turn complex business challenges into actionable insights. This role is perfect for someone who thrives in a fast-paced, data-driven environment and wants to make real impact through intelligent systems.
What Youβll Do:
- Design, develop, and deploy machine learning models using structured and unstructured data
- Own the full ML lifecycle: data exploration, feature engineering, model training, evaluation, deployment, and monitoring
- Use Apache Spark (PySpark) to efficiently process large-scale datasets in distributed environments
- Collaborate with data engineers, product managers, and stakeholders to align ML models with business goals
- Perform model validation and performance tuning, ensuring reproducibility with robust pipelines
- Work in Python with libraries such as pandas, scikit-learn, NumPy, MLflow
- Use Git for version control and collaborate via code reviews and structured workflows
- Present insights, recommendations, and results clearly to both technical and non-technical audiences
- Stay up to date with AI/ML trends and assess new tools and techniques for implementation
- Document models, experiments, and workflows for clarity and future use
What Weβre Looking For:
- 5+ years of experience in machine learning, AI, and statistical modeling
- Deep expertise in supervised, unsupervised, and ensemble learning methods
- Strong proficiency in Python for data science and machine learning
- Hands-on experience with Apache Spark / PySpark for distributed data processing
- Solid knowledge of Git and collaborative development practices
- Experience building scalable, production-grade ML pipelines and working with large datasets
- Strong problem-solving skills and solid foundations in math and statistics
- Excellent communication skills β ability to explain technical results to non-technical audiences
- Based in an EU country
Nice to Have
- Experience with cloud-based ML platforms like AWS SageMaker, GCP, or Azure ML
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Β· 16 views Β· 0 applications Β· 6d
Senior Data Science/ AI Engineer (with Databricks)
Poland Β· 4 years of experience Β· Upper-IntermediateThe Company (Location: Langenfeld, Germany): Since 2002, the client has been a market leader in automotive claims management, processing over 18 million vehicle claims annually with a global team of more than 2,000 employees. Operating in over 30...The Company (Location: Langenfeld, Germany):
Since 2002, the client has been a market leader in automotive claims management, processing over 18 million vehicle claims annually with a global team of more than 2,000 employees. Operating in over 30 countries, the company specializes in digital solutions that optimize vehicle damage processing for insurance companies, car dealerships, repair shops, leasing firms, and automotive manufacturers. By leveraging automation, advanced technologies, and industry expertise, the client continuously enhances efficiency and accuracy in claims handling. An in-house research and development team drives innovation, tailoring solutions to local market needs while advancing digital transformation in the industry. At the core of this evolution is a strong development team, building scalable, high-performance software solutions that integrate data-driven processes with human expertise to reshape automotive claims management.Short Role Description: We are seeking a Senior Data Scientist / AI Engineer with expertise in Databricks to enhance our capabilities in developing advanced AI solutions for automotive claims management. You will leverage your analytical and machine learning skills to innovate and improve data-driven processes, working closely with data engineers and solution architects in a collaborative, international environment.
Key Responsibilities:
- Develop and implement advanced machine learning and AI models using Databricks.
- Collaborate with data engineering and solution architecture teams to define and refine data processing pipelines.
- Analyze complex datasets (structured, PDFs, images) to extract meaningful insights and predictive capabilities.
- Build and maintain vector databases, leveraging embeddings and AI model integrations.
- Actively participate in AI governance, ensuring best practices in model development, validation, and deployment.
- Document methodologies, experiments, and model architectures for reproducibility and knowledge sharing.
- Continuously evaluate emerging technologies and methodologies in AI to enhance capabilities.
- Present findings clearly and persuasively to technical and non-technical stakeholders.
What We Expect from You (Requirements):
- 4+ years of proven experience in Data Science or AI Engineering.
- Strong expertise in developing and deploying machine learning models using Databricks.
- Proficiency in Python, including common machine learning libraries (TensorFlow, PyTorch, scikit-learn).
- Familiarity with data handling in cloud environments (Azure or AWS).
- Solid experience with big data processing frameworks, especially Apache Spark.
- Experience in managing structured and unstructured data (PDFs, images, hierarchical data).
- Ability to build, validate, and deploy AI models efficiently and effectively.
- Strong communication skills, able to present complex data insights clearly.
- Fluent in English at B2 level or higher.
- Collaborative, adaptable, and eager to learn within international teams.
Nice to have:
- Familiarity with Elasticsearch or other vector-based storage solutions.
- Experience with NLP and computer vision techniques.
- Proficiency with container technologies (Docker, Kubernetes).
- Knowledge of data governance practices related to AI models.
- Availability for international travel twice a year for workshops and alignment meetings.
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Β· 17 views Β· 0 applications Β· 6d
Senior Data Science Engineer
Poland Β· 4 years of experience Β· Upper-IntermediateWe are looking for a Data Scientist with a strong analytical mindset and a passion for solving real-world supply chain problems. You will join a cross-functional team focused on optimizing the flow of units from warehouses to stores. This is a high-impact...We are looking for a Data Scientist with a strong analytical mindset and a passion for solving real-world supply chain problems. You will join a cross-functional team focused on optimizing the flow of units from warehouses to stores. This is a high-impact role where your insights and models will directly influence key business operations.
Responsibilities:
- Prepare and clean datasets to enable reliable experimentation
- Develop and fine-tune machine learning models and algorithms
- Test models, analyze outcomes, and generate actionable insights
- Communicate findings to stakeholders through reports and visualizations
- Propose data-driven solutions and optimization strategies
Requirements:
- Proficiency in Python and/or R
- Experience with SQL
- Data Platforms & Tools
- Hands-on experience with Azure Databricks or Snowflake
- Familiarity with building and deploying machine learning pipelines is a strong advantage
- Java experience is a plus
- Education - Bachelor's or Master's degree in Computer Science, Mathematics, Engineering, or a related field
Mathematics & Statistics:
- Solid understanding of multivariable calculus and linear algebra
- Applied knowledge of statistical distributions and hypothesis testing
- Machine Learning
- Practical knowledge of ML techniques: kNN, decision forests, regressions, MLE, time series
- Understanding of model evaluation metrics and performance tuning
- Data Handling & Visualization
- Skilled in managing missing or inconsistent data
- Experience with tools like matplotlib, seaborn, or ggplot2
Nice to Have:
- Experience with deploying ML models into production environments
- Previous work in supply chain or logistics domains
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Β· 78 views Β· 3 applications Β· 6d
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)
More
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· 18 views Β· 0 applications Β· 5d
Senior Data Science with Snowflake
Full Remote Β· Ukraine Β· 5 years of experience Β· Upper-IntermediateWe are looking for Experienced Senior Data Analyst with Snowflake for one of our projects. About Project: The company is a multinational pharmaceutical and diagnostics corporation. It is renowned for its contributions to healthcare through the...We are looking for Experienced Senior Data Analyst with Snowflake for one of our projects.
About Project:
The company is a multinational pharmaceutical and diagnostics corporation. It is renowned for its contributions to healthcare through the development of innovative drugs and diagnostic solutions. With a focus on advancing medical science, the company has established itself as a leader in the biotechnology and healthcare industries. The company commitment to scientific research and development underscores its role in shaping the future of healthcare worldwide.
Responsibilities:
- You will collaborate with cross-functional teams to analyze requirements, develop technical specifications and implement solutions;
- Analyze large and complex datasets to uncover insights, trends, and patterns that support strategic and operational decision-making
- Design and maintain dashboards and reports using BI tools (e.g., Power BI, Tableau, or similar) to communicate findings effectively to both technical and non-technical users
- Act as a bridge between business and technical teams β translating business questions into analytical tasks and ensuring data solutions align with business objectives
- Design and develop data models and data warehouses in Snowflake;
- Develop and maintain ETL processes to move data from source systems to Snowflake;
Create and maintain views, stored procedures, and other database objects.
Requirements:
- Proven experience as Data Analyst with Snowflake in a senior capacity;
- In-depth understanding of Snowflake architecture and best practices;
- Hands-on experience with AWS;
- Strong proficiency in SQL and experience working with large datasets;
- Hands-on experience with data modeling, ETL processes, and data warehousing;
- Solid understanding of data visualization tools.
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Β· 54 views Β· 2 applications Β· 4d
Computer Vision Engineer
Ukraine Β· Product Β· 4 years of experience MilTech πͺWe are looking for a Computer Vision Engineer with a background in classical computer vision techniques and hands-on implementation of low-level CV algorithms. The ideal candidate will have experience with SLAM, Visual-Inertial Odometry (VIO), and sensor...We are looking for a Computer Vision Engineer with a background in classical computer vision techniques and hands-on implementation of low-level CV algorithms.
The ideal candidate will have experience with SLAM, Visual-Inertial Odometry (VIO), and sensor fusion.
We consider engineers at Middle/Senior levels β tasks and responsibilities will be adjusted accordingly.
Required Qualifications:
- 3+ years of hands-on experience with classical computer vision
- Knowledge of popular computer vision networks and components
- Understanding of geometrical computer vision principles
- Hands-on experience in implementing low-level CV algorithms
- Practical experience with SLAM and/or Visual-Inertial Odometry (VIO)
- Proficiency in C++
- Experience with Linux
- Ability to quickly navigate through recent research and trends in computer vision.
Nice to Have:
- Experience with Python
- Familiarity with neural networks and common CV frameworks/libraries (OpenCV, NumPy, PyTorch, ONNX, Eigen, etc.)
- Experience with sensor fusion.
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Β· 45 views Β· 14 applications Β· 4d
Principal/Senior Marketing Analyst to $6000
Full Remote Β· Worldwide Β· Product Β· 3 years of experience Β· Upper-IntermediateAtom Apps β a fast-growing, AI-powered mobile app company with 15M+ U.S. downloads. They specialize in end-to-end mobile app development, monetization, and distribution across various verticals. Now, Atom Apps is hiring a Marketing Analyst. Key...Atom Apps β a fast-growing, AI-powered mobile app company with 15M+ U.S. downloads. They specialize in end-to-end mobile app development, monetization, and distribution across various verticals.
Now, Atom Apps is hiring a Marketing Analyst.
Key responsibilities:
- Evaluate paid campaign effectiveness and work closely with marketers to achieve better profitability.
- Work closely with the marketing team to align on goals and optimize lead-to-revenue performance.
- Use data to identify high-value segments, guide personalization strategies, and improve conversion rates across the funnel.
- Perform cohort analyses, funnel evaluations, and churn investigations to uncover growth opportunities and user friction points on different acquisition channels.
- Build scalable SQL queries, data models, and BI dashboards (e.g., in Tableau, Looker, Power BI) to support self-serve analytics and empower team autonomy.
- Build and update LTV prediction models.
- Collaborate with data engineering to ensure clean, scalable, and real-time data pipelines.
- Be a thought partner to CMO, Head of Product, and CEO, while driving company bets, not just reporting metrics.
- Try out different mathematical modeling methods to attribute organic traffic by channel.
Must-have technology & skill requirements:
- Advanced SQL: ability to write complex queries for large, distributed datasets.
- Proficiency in data visualization tools such as Tableau, Looker, Power BI, or similar.
- Strong skills in Python or R for statistical analysis, modeling, and automation.
- Familiarity with data warehousing and analytics engineering tools, and creating ETL Models (e.g., dbt).
Nice-to-have technology & skill requirements:
- Experience in marketing analytics, business intelligence, or data analysis, ideally in tech.
- Hands-on experience with multichannel campaign analysis (especially paid search, paid social, email, and web).
Why Join Atom Apps?
- π Fully Remote β Work from anywhere in the world
- π° Competitive Pay & Benefits
- π± Growth Opportunities β International exposure & cross-functional collaboration
- βοΈ Modern Tech Stack β Access to the latest AI models and marketing tools
- π€ Global Team β Work with top-tier talent across continents
π§ Real Ownership β Make a visible impact on our product and business
Join Us in Building the Future of AI-Powered Consumer Apps
If youβre passionate about scaling mobile apps through performance marketing and want to work with a dynamic, international team, we want to hear from you!
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Β· 18 views Β· 0 applications Β· 10h
Senior Data Scientist to $8000
Full Remote Β· Bulgaria, Poland, Portugal, Ukraine Β· Product Β· 5 years of experience Β· Upper-IntermediateWho we are: Adaptiq is a technology hub specializing in building, scaling, and supporting R&D teams for high-end, fast-growing product companies in a wide range of industries. About the Product: Our client is a leading SaaS company offering pricing...Who we are:
Adaptiq is a technology hub specializing in building, scaling, and supporting R&D teams for high-end, fast-growing product companies in a wide range of industries.
About the Product:
Our client is a leading SaaS company offering pricing optimization solutions for e-commerce businesses. Its advanced technology utilizes big data, machine learning, and AI to assist customers in optimizing their pricing strategies and maximizing their profits.
About the Role:
As a Data Scientist youβll play a critical role in shaping and enhancing our AI-driven pricing platform.
Key Responsibilities:
- Develop and Optimize Advanced ML Models: Build, improve, and deploy machine learning and statistical models for forecasting demand, analyzing price elasticities, and recommending optimal pricing strategies.
- Lead End-to-End Data Science Projects: Own your projects fully, from conceptualization and experimentation through production deployment, monitoring, and iterative improvement.
- Innovate with Generative and Predictive AI Solutions: Leverage state-of-the-art generative and predictive modeling techniques to automate complex pricing scenarios and adapt to rapidly changing market dynamics.
Required Competence and Skills:
- A Masterβs or PhD in Computer Science, Physics, Applied Mathematics or a related field, demonstrating a strong foundation in analytical thinking.
- At least 5 years of professional experience in end-to-end machine learning lifecycle (design, development, deployment, and monitoring).
- At least 5 years of professional experience with Python development, including OOP, writing production-grade code, testing, and optimization.
- At least 5 years of experience with data mining, statistical analysis, and effective data visualization techniques.
- Deep familiarity with modern ML/DL methods and frameworks (e.g., PyTorch, XGBoost, scikit-learn, statsmodels).
- Strong analytical skills combined with practical experience interpreting model outputs to drive business decisions.
Nice-to-Have:
- Practical knowledge of SQL and experience with large-scale data systems like Hadoop or Spark.
- Familiarity with MLOps tools and practices (CI/CD, model monitoring, data version control).
- Experience in reinforcement learning and Monte-Carlo methods.
- A solid grasp of microeconomic principles, including supply and demand dynamics, price elasticity, as well as econometrics.
- Experience with cloud services and platforms, preferably AWS.
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Β· 18 views Β· 0 applications Β· 10h
Machine Learning Engineer
Full Remote Β· Ukraine Β· Product Β· 2 years of experienceBig product software company is looking for a Machine Learning Engineer. Remote work, high salary + financial bonuses (up to 100% of the salary), regular salary review, interesting projects, good working conditions. REQUIREMENTS: - Machine Learning...Big product software company is looking for a Machine Learning Engineer. Remote work, high salary + financial bonuses (up to 100% of the salary), regular salary review, interesting projects, good working conditions.
REQUIREMENTS:
- Machine Learning experience from 2 years;
- Practical skills with Python;
- Higher education;
- Technical English (higher level is advantage).
COMPANY OFFERS:
- Employment under gig-contract, all taxes are paid;
- Flexible working hours;
- 28 days of paid vacation + 15 days at your own expense;
- Paid sick leave;
- Medical insurance (with dentistry and optics), including the children;
- Opportunity to become an inventor of international patents with paid bonuses;
- Career and professional growth;
- Own base of courses and trainings;
- Office in the Kyiv city centre / remotely;
- Provision of necessary up-to-date equipment;
- Regular salary review and financial bonuses (up to 100% of the salary);
- Bonuses for wedding, birth of children and other significant events;
- Paid maternity leave;
- Paid lunches, tea, coffee, water, snacks;
- Discounts to company's products, services.
More -
Β· 13 views Β· 0 applications Β· 3h
Fractional Mathematician
Part-time Β· Full Remote Β· Worldwide Β· 3 years of experience Β· Upper-IntermediateSwipe Games is seeking a Fractional Mathematician with deep iGaming expertise to design, analyze, and optimize game mechanics and payout structures. This is a hands-on, high-impact consulting role for an expert who understands both the creative and...Swipe Games is seeking a Fractional Mathematician with deep iGaming expertise to design, analyze, and optimize game mechanics and payout structures. This is a hands-on, high-impact consulting role for an expert who understands both the creative and regulatory demands of modern game math. You will collaborate closely with our core team to ensure our products deliver engaging, fair, and profitable experiences for players and partners.
Key Requirements
- Proven experience designing and analyzing game math for successful, high-load iGaming products
- Deep understanding of probability theory, statistics, and stochastic modeling as applied to games of chance and skill
- Familiarity with regulatory and compliance requirements for game mathematics in key iGaming markets
- Experience with provably fair algorithms and cryptography in gaming contexts, RNG certification
- Strong business orientation: ability to balance player engagement, fairness, and monetization
- High level of ownership and initiative in delivering mathematical solutions
Responsibilities
- Design and validate mathematical models and payout structures for new and existing games
- Collaborate with product, engineering, and compliance teams to ensure math models meet regulatory and business requirements
- Analyze game performance, volatility, and player behavior to optimize engagement and profitability
- Develop and review provably fair algorithms and cryptographic solutions for game outcomes
- Provide ad-hoc mathematical support for partner integrations, risk management, and game audits
- Document and present mathematical concepts and models to both technical and non-technical stakeholders
What We Offer
- Opportunity to shape the core mechanics of breakthrough iGaming products
- Flexible, high-ownership engagement with a next-gen product team
- Collaboration with industry experts in a dynamic, innovation-driven environment
- Competitive compensation for fractional consulting
If you are a mathematician with a passion for iGaming innovation and a proven record of delivering robust, compliant game math, we want to hear from you.
More -
Β· 28 views Β· 0 applications Β· 3d
Senior Data Scientist (AI)
Ukraine Β· Product Β· 5 years of experience Ukrainian Product πΊπ¦Π ΠΊΠΎΠΌΠ°Π½Π΄Ρ DataDiscovery ΡΡΠΊΠ°ΡΠΌΠΎ Π½Π° ΡΠΎΠ·ΡΠΈΡΠ΅Π½Π½Ρ Senior Data Scientist. ΠΠ°Ρ ΡΠ΄Π΅Π°Π»ΡΠ½ΠΈΠΉ ΠΊΠ°Π½Π΄ΠΈΠ΄Π°Ρ ΠΌΠ°Ρ: - 5+ ΡΠΎΠΊΠΈ ΠΊΠΎΠΌΠ΅ΡΡΡΠΉΠ½ΠΎΠ³ΠΎ Π΄ΠΎΡΠ²ΡΠ΄Ρ ΡΠΎΠ·ΡΠΎΠ±ΠΊΠΈ. ΠΠ°Π²ΠΈΡΠΊΠΈ: - Python ΡΠ° Π±ΡΠ±Π»ΡΠΎΡΠ΅ΠΊΠΈ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ Π½Π°Π²ΡΠ°Π½Π½Ρ: TensorFlow, PyTorch; - Π’Π΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΡ Big Data: Kafka, Amazon...Π ΠΊΠΎΠΌΠ°Π½Π΄Ρ DataDiscovery ΡΡΠΊΠ°ΡΠΌΠΎ Π½Π° ΡΠΎΠ·ΡΠΈΡΠ΅Π½Π½Ρ Senior Data Scientist.
ΠΠ°Ρ ΡΠ΄Π΅Π°Π»ΡΠ½ΠΈΠΉ ΠΊΠ°Π½Π΄ΠΈΠ΄Π°Ρ ΠΌΠ°Ρ:
- 5+ ΡΠΎΠΊΠΈ ΠΊΠΎΠΌΠ΅ΡΡΡΠΉΠ½ΠΎΠ³ΠΎ Π΄ΠΎΡΠ²ΡΠ΄Ρ ΡΠΎΠ·ΡΠΎΠ±ΠΊΠΈ.
ΠΠ°Π²ΠΈΡΠΊΠΈ:
- Python ΡΠ° Π±ΡΠ±Π»ΡΠΎΡΠ΅ΠΊΠΈ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ Π½Π°Π²ΡΠ°Π½Π½Ρ: TensorFlow, PyTorch;
- Π’Π΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΡ Big Data: Kafka, Amazon S3, Spark;
- SQL ΡΠ° Π°Π½Π°Π»ΡΠ· 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₯ΠΌΠ°ΡΠ½Ρ ΠΏΠ»Π°ΡΡΠΎΡΠΌΠΈ: AWS, GCP;
ΠΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ½Π° ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠ°: ΡΠ΅Π³ΡΠ΅ΡΡΡ, ΡΠΎΠ·ΠΏΠΎΠ΄ΡΠ» ΠΉΠΌΠΎΠ²ΡΡΠ½ΠΎΡΡΠ΅ΠΉ, - ΠΏΠ΅ΡΠ΅Π²ΡΡΠΊΠ° ΡΡΠ°ΡΠΈΡΡΠΈΡΠ½ΠΈΡ Π³ΡΠΏΠΎΡΠ΅Π· ΡΠΎΡΠΎ;
- ΠΡΠ΄Ρ ΠΎΠ΄ΠΈ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ Π½Π°Π²ΡΠ°Π½Π½Ρ: ΡΠ΅Π³ΡΠ΅ΡΡΡ, ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΡΡ, Π΄Π΅ΡΠ΅Π²Π° ΡΡΡΠ΅Π½Ρ ΡΠ° ΡΠ½ΡΡ;
- ΠΠ»Π³ΠΎΡΠΈΡΠΌΠΈ Π³Π»ΠΈΠ±ΠΎΠΊΠΎΠ³ΠΎ Π½Π°Π²ΡΠ°Π½Π½Ρ: transformers, reinforcement learning, autoencoders, diffusion models, ΡΠΎΡΠΎ;
- ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ·ΡΠΎΠ±ΠΊΠΈ ΠΏΡΠΎΠ΄ΡΠΊΡΡΠ² AI: NLP, CV, Recsys, Generative AI;
- MLOps.
Π©ΠΎ ΠΏΠΎΡΡΡΠ±Π½ΠΎ ΡΠΎΠ±ΠΈΡΠΈ:
- ΠΠΈΡΡΡΡΠ²Π°ΡΠΈ ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ²Ρ ΡΠ° Π΄ΠΎΡΠ»ΡΠ΄Π½ΠΈΡΡΠΊΡ Π²ΠΈΠΊΠ»ΠΈΠΊΠΈ ΠΊΡΡΡΠΎΠ³ΠΎ ΡΠΊΡΠ°ΡΠ½ΡΡΠΊΠΎΠ³ΠΎ ΠΏΡΠΎΠ΄ΡΠΊΡΡ;
- ΠΡΠ°ΡΡΠ²Π°ΡΠΈ Π· ΡΠ΅Π°Π»ΡΠ½ΠΈΠΌΠΈ Π΄Π°Π½ΠΈΠΌΠΈ ΡΠ΅Π°Π»ΡΠ½ΠΈΡ ΠΊΠΎΡΠΈΡΡΡΠ²Π°ΡΡΠ²;
- ΠΠΈΠ²ΡΠ°ΡΠΈ ΡΠ° Π²ΠΏΡΠΎΠ²Π°Π΄ΠΆΡΠ²Π°ΡΠΈ ΡΠΊΠ»Π°Π΄Π½Ρ state-of-the-art Π°Π»Π³ΠΎΡΠΈΡΠΌΠΈ Π² ΠΎΠ±Π»Π°ΡΡΡ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ Π½Π°Π²ΡΠ°Π½Π½Ρ Π΄Π»Ρ Π²ΠΈΡΡΡΠ΅Π½Π½Ρ ΠΏΡΠ°ΠΊΡΠΈΡΠ½ΠΈΡ Π·Π°Π΄Π°Ρ;
- ΠΡΡΠ½ΡΠ²Π°ΡΠΈ ΡΠ΅Ρ Π½ΡΡΠ½Ρ ΠΊΠΎΠΌΠΏΡΠΎΠΌΡΡΠΈ ΠΏΠΎ ΠΊΠΎΠΆΠ½ΠΎΠΌΡ ΡΡΡΠ΅Π½Π½Ρ;
- ΠΡΠ°ΡΡΠ²Π°ΡΠΈ Π² ΡΡΡΠ½ΠΎΠΌΡ ΡΠΏΡΠ²ΡΠΎΠ±ΡΡΠ½ΠΈΡΡΠ²Ρ Π· ΡΠ½ΡΠΈΠΌΠΈ ΠΊΠΎΠΌΠ°Π½Π΄Π°ΠΌΠΈ Π΄Π»Ρ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½Ρ Π½ΠΎΠ²ΠΈΡ ΠΌΠΎΠΆΠ»ΠΈΠ²ΠΎΡΡΠ΅ΠΉ Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½Ρ ΡΠ½ΡΡΡΡΠΌΠ΅Π½ΡΡΠ² AI.
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.
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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Β· 138 views Β· 1 application Β· 26d
Data Scientist
Ukraine Β· Product Β· 1 year of experience Β· Pre-IntermediateΠ ΠΊΠΎΠΌΠ°Π½Π΄Ρ 10 Π΄Π°ΡΠ° ΡΠ°ΡΠ½ΡΠΈΡΡΡΠ². ΠΡΠ°ΡΡΡΡΡ Π· ΠΌΠΎΠ΄Π΅Π»ΡΠΌΠΈ ΠΊΡΠ΅Π΄ΠΈΡΠ½ΠΎΠ³ΠΎ ΡΠΊΠΎΡΠΈΠ½Π³Ρ, ΠΎΡΡΠ½ΠΊΠΈ ΡΠΈΠ·ΠΈΠΊΡ Π»ΡΠΊΠ²ΡΠ΄Π½ΠΎΡΡΡ, ΠΊΠΎΠ»Π΅ΠΊΡΠ½Ρ, Π°Π½ΡΠΈΡΡΠΎΠ΄Ρ, ΠΌΠΎΠ½ΡΡΠΎΡΠΈΠ½Π³Ρ. ΠΡΠ½ΠΎΠ²Π½Ρ Π²ΠΈΠΌΠΎΠ³ΠΈ ΠΠΠΠ'Π―ΠΠΠΠΠ 1+ ΡΡΠΊ ΠΊΠΎΠΌΠ΅ΡΡΡΠΉΠ½ΠΎΠ³ΠΎ Π΄ΠΎΡΠ²ΡΠ΄Ρ Π½Π° Π°Π½Π°Π»ΠΎΠ³ΡΡΠ½ΡΠΉ ΠΏΠΎΡΠ°Π΄Ρ ΠΠ°ΠΊΡΠ½ΡΠ΅Π½Π° Π²ΠΈΡΠ° ΠΎΡΠ²ΡΡΠ° (ΡΡΠ·ΠΈΠΊΠΎ-ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ½Π°,...Π ΠΊΠΎΠΌΠ°Π½Π΄Ρ 10 Π΄Π°ΡΠ° ΡΠ°ΡΠ½ΡΠΈΡΡΡΠ². ΠΡΠ°ΡΡΡΡΡ Π· ΠΌΠΎΠ΄Π΅Π»ΡΠΌΠΈ ΠΊΡΠ΅Π΄ΠΈΡΠ½ΠΎΠ³ΠΎ ΡΠΊΠΎΡΠΈΠ½Π³Ρ, ΠΎΡΡΠ½ΠΊΠΈ ΡΠΈΠ·ΠΈΠΊΡ Π»ΡΠΊΠ²ΡΠ΄Π½ΠΎΡΡΡ, ΠΊΠΎΠ»Π΅ΠΊΡΠ½Ρ, Π°Π½ΡΠΈΡΡΠΎΠ΄Ρ, ΠΌΠΎΠ½ΡΡΠΎΡΠΈΠ½Π³Ρ.
ΠΡΠ½ΠΎΠ²Π½Ρ Π²ΠΈΠΌΠΎΠ³ΠΈ
- ΠΠΠΠ'Π―ΠΠΠΠΠ 1+ ΡΡΠΊ ΠΊΠΎΠΌΠ΅ΡΡΡΠΉΠ½ΠΎΠ³ΠΎ Π΄ΠΎΡΠ²ΡΠ΄Ρ Π½Π° Π°Π½Π°Π»ΠΎΠ³ΡΡΠ½ΡΠΉ ΠΏΠΎΡΠ°Π΄Ρ
- ΠΠ°ΠΊΡΠ½ΡΠ΅Π½Π° Π²ΠΈΡΠ° ΠΎΡΠ²ΡΡΠ° (ΡΡΠ·ΠΈΠΊΠΎ-ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ½Π°, ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠ°, ΠΊΠΎΠΌΠΏ'ΡΡΠ΅ΡΠ½Ρ Π½Π°ΡΠΊΠΈ)
- Π‘ΠΊΡΡΠΏΡΠ»ΡΠΎΠ·Π½ΡΡΡΡ, ΡΠ²Π°ΠΆΠ½ΡΡΡΡ Ρ Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π°Π»ΡΠ½ΡΡΡΡ
- ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ·ΡΠΎΠ±ΠΊΠΈ, Π²ΠΏΡΠΎΠ²Π°Π΄ΠΆΠ΅Π½Π½Ρ ΡΠ° ΡΡΠΏΡΠΎΠ²ΠΎΠ΄ΠΆΠ΅Π½Π½Ρ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ
- ΠΠΎΡΠ²ΡΠ΄ Π½Π°ΠΏΠΈΡΠ°Π½Π½Ρ ΠΊΠ»Π°ΡΡΠ², ΠΏΠ°ΠΉΠΏΠ»Π°ΠΉΠ½ΡΠ²
- ΠΠ½Π°Π½Π½Ρ Python
- ΠΠ°ΡΠ½Π΅ Π·Π½Π°Π½Π½Ρ SQL (Π°Π½Π°Π»ΡΠ·ΡΡΠΌΠΎ Π½Π°ΠΉΠ±ΡΠ»ΡΡΡ Π· ΡΡΡΡ Π±Π°Π½ΠΊΡΠ² Π±Π°Π·ΠΈ Π΄Π°Π½ΠΈΡ )
- ΠΠ½Π°Π½Π½Ρ ΠΎΡΠ½ΠΎΠ²Π½ΠΈΡ Π°Π»Π³ΠΎΡΠΈΡΠΌΡΠ² ML (ΠΊΠ»Π°ΡΠΈΡΠ½ΠΈΠΉ ML, ΡΠ΅Π³ΡΠ΅ΡΡΡ, ΠΊΠ»Π°ΡΠΈΡΡΠΊΠ°ΡΡΡ, ΠΏΡΠΎΠ³Π½ΠΎΠ· ΡΠ°ΡΠΎΠ²ΠΈΡ ΡΡΠ΄ΡΠ²)
ΠΡΠ΄Π΅ ΠΏΠ»ΡΡΠΎΠΌ
- ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Ρ ΡΡΠ½ΡΠ΅Ρ Π΄ΠΎΠΌΠ΅Π½Ρ
- ΠΠΎΡΠ²ΡΠ΄ Π· Amazon Sagemaker, Amazon S3
- ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· Git
ΠΡΠ½ΠΎΠ²Π½Ρ ΠΎΠ±ΠΎΠ²βΡΠ·ΠΊΠΈ
- ΠΠ½Π°Π»ΡΠ· ΡΠ° Π²Π°Π»ΡΠ΄Π°ΡΡΡ Π΄Π°Π½ΠΈΡ
- Π ΠΎΠ·ΡΠΎΠ±ΠΊΠ° ΡΠ° Π²ΠΏΡΠΎΠ²Π°Π΄ΠΆΠ΅Π½Π½Ρ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΠΎΡΡΠ½ΠΊΠΈ ΡΠΈΠ·ΠΈΠΊΡΠ², ΡΠΊΡ Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π°ΡΡΡ ΠΊΡΠ°ΡΠΈΠΌ ΡΠ²ΡΡΠΎΠ²ΠΈΠΌ ΠΏΡΠ°ΠΊΡΠΈΠΊΠ°ΠΌ
- ΠΠΎΠ½ΡΡΠΎΡΠΈΠ½Π³ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ, Π·Π°Π±Π΅Π·ΠΏΠ΅ΡΠ΅Π½Π½Ρ Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π½ΠΎΡ ΡΠΊΠΎΡΡΡ
Π‘Π²ΠΎΡΠΌ ΡΠΏΡΠ²ΡΠΎΠ±ΡΡΠ½ΠΈΠΊΠ°ΠΌ ΠΌΠΈ ΠΏΡΠΎΠΏΠΎΠ½ΡΡΠΌΠΎ
- Π ΠΎΠ±ΠΎΡΡ Π² Π½Π°ΠΉΠ±ΡΠ»ΡΡΠΎΠΌΡ ΡΠ° ΡΠ½Π½ΠΎΠ²Π°ΡΡΠΉΠ½ΠΎΠΌΡ Π±Π°Π½ΠΊΡ Π£ΠΊΡΠ°ΡΠ½ΠΈ
- ΠΡΡΡΡΠΉΠ½Π΅ ΠΏΡΠ°ΡΠ΅Π²Π»Π°ΡΡΡΠ²Π°Π½Π½Ρ ΡΠ° 24 ΠΊΠ°Π»Π΅Π½Π΄Π°ΡΠ½ΠΈΡ Π΄Π½Ρ Π²ΡΠ΄ΠΏΡΡΡΠΊΠΈ
- ΠΠΎΠΌΠΏΠ΅Π½ΡΠ°ΡΡΡ Π»ΡΠΊΠ°ΡΠ½ΡΠ½ΠΈΡ
- ΠΠΎΠ½ΠΊΡΡΠ΅Π½ΡΠ½Ρ Π·Π°ΡΠΎΠ±ΡΡΠ½Ρ ΠΏΠ»Π°ΡΡ
- ΠΠΎΠ½ΡΡΠΈ, ΠΏΡΠ΅ΠΌΡΡ Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π½ΠΎ Π΄ΠΎ ΠΏΠΎΠ»ΡΡΠΈΠΊΠΈ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ
- ΠΠ΅Π΄ΠΈΡΠ½Π΅ ΡΡΡΠ°Ρ ΡΠ²Π°Π½Π½Ρ
- ΠΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½Π΅ Π½Π°Π²ΡΠ°Π½Π½Ρ
- ΠΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ Π²ΡΠ΄Π΄Π°Π»Π΅Π½ΠΎΠ³ΠΎ ΡΠΎΡΠΌΠ°ΡΡ ΡΠΎΠ±ΠΎΡΠΈ
- ΠΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½Ρ ΡΡΠ½Π°Π½ΡΠΎΠ²Ρ Π΄ΠΎΠΏΠΎΠΌΠΎΠ³Ρ Ρ ΠΊΡΠΈΡΠΈΡΠ½ΠΈΡ ΡΠΈΡΡΠ°ΡΡΡΡ
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Β· 145 views Β· 9 applications Β· 29d
Junior / Middle Data Scientist
Ukraine Β· Product Β· 1 year of experience Ukrainian Product πΊπ¦SKELAR β ΡΠΊΡΠ°ΡΠ½ΡΡΠΊΠΈΠΉ venture builder, ΡΠΊΠΈΠΉ Π±ΡΠ΄ΡΡ ΠΌΡΠΆΠ½Π°ΡΠΎΠ΄Π½Ρ tech-Π±ΡΠ·Π½Π΅ΡΠΈ. Π Π°Π·ΠΎΠΌ ΡΠ· ΠΊΠΎ-ΡΠ°ΡΠ½Π΄Π΅ΡΠ°ΠΌΠΈ Π·Π±ΠΈΡΠ°ΡΠΌΠΎ ΡΠΈΠ»ΡΠ½Ρ ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ, ΡΠΎΠ± ΠΏΠ΅ΡΠ΅ΠΌΠ°Π³Π°ΡΠΈ Π½Π° Π³Π»ΠΎΠ±Π°Π»ΡΠ½ΠΈΡ ΡΠΈΠ½ΠΊΠ°Ρ . Π‘ΡΠΎΠ³ΠΎΠ΄Π½Ρ Π² SKELAR β Π΄Π΅ΡΡΡΠΎΠΊ Π±ΡΠ·Π½Π΅ΡΡΠ² Ρ ΡΡΠ·Π½ΠΈΡ Π½ΡΡΠ°Ρ Π²ΡΠ΄ EdTech Π΄ΠΎ SaaS. Π¦Π΅ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ, ΡΠΎ ΠΌΠ°ΡΡΡ...SKELAR β ΡΠΊΡΠ°ΡΠ½ΡΡΠΊΠΈΠΉ venture builder, ΡΠΊΠΈΠΉ Π±ΡΠ΄ΡΡ ΠΌΡΠΆΠ½Π°ΡΠΎΠ΄Π½Ρ tech-Π±ΡΠ·Π½Π΅ΡΠΈ. Π Π°Π·ΠΎΠΌ ΡΠ· ΠΊΠΎ-ΡΠ°ΡΠ½Π΄Π΅ΡΠ°ΠΌΠΈ Π·Π±ΠΈΡΠ°ΡΠΌΠΎ ΡΠΈΠ»ΡΠ½Ρ ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ, ΡΠΎΠ± ΠΏΠ΅ΡΠ΅ΠΌΠ°Π³Π°ΡΠΈ Π½Π° Π³Π»ΠΎΠ±Π°Π»ΡΠ½ΠΈΡ ΡΠΈΠ½ΠΊΠ°Ρ .
Π‘ΡΠΎΠ³ΠΎΠ΄Π½Ρ Π² SKELAR β Π΄Π΅ΡΡΡΠΎΠΊ Π±ΡΠ·Π½Π΅ΡΡΠ² Ρ ΡΡΠ·Π½ΠΈΡ Π½ΡΡΠ°Ρ Π²ΡΠ΄ EdTech Π΄ΠΎ SaaS. Π¦Π΅ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ, ΡΠΎ ΠΌΠ°ΡΡΡ Π²ΡΠ΄Π·Π½Π°ΠΊΠΈ Π²ΡΠ΄ Product Hunt, ΠΏΠΎΡΡΠ°ΠΏΠ»ΡΡΡΡ Ρ ΡΠ΅ΠΉΡΠΈΠ½Π³ΠΈ Π’ΠΠ-ΡΡΠ°ΡΡΠ°ΠΏΡΠ² ΡΠ° ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ²ΠΈΡ ΠΊΠΎΠΌΠΏΠ°Π½ΡΠΉ Π£ΠΊΡΠ°ΡΠ½ΠΈ, Π·Π°ΠΉΠΌΠ°ΡΡΡ Π½Π°ΠΉΠ²ΠΈΡΡ ΡΠ°Π±Π»Ρ Π² AppStore ΡΠ° ΡΠΎΠ·ΡΠΎΠ±Π»ΡΡΡΡ ΠΏΠ»Π°ΡΡΠΎΡΠΌΠΈ, ΡΠΊΠΈΠΌΠΈ ΠΊΠΎΡΠΈΡΡΡΡΡΡΡΡ ΠΌΡΠ»ΡΠΉΠΎΠ½ΠΈ Π»ΡΠ΄Π΅ΠΉ. Π ΡΠ΅ ΠΏΡΠΎ Π±ΡΠ·Π½Π΅ΡΠΈ SKELAR ΠΏΠΈΡΡΡΡ TechCrunch, Wired ΡΠ° ΡΠ½ΡΡ ΡΠ²ΡΡΠΎΠ²Ρ ΠΌΠ΅Π΄ΡΠ°.
ΠΠΈΡΠ°ΡΠΌΠΎΡΡ ΡΠΈΠ»ΡΠ½ΠΎΡ ΠΊΠΎΠΌΠ°Π½Π΄ΠΎΡ ΡΠ· 800+ ΡΠ°Ρ ΡΠ²ΡΡΠ², ΡΠΊΡ ΠΌΠ°ΡΡΡ ΠΊΡΡΡΡ Π΅ΠΊΡΠΏΠ΅ΡΡΠΈΠ·Ρ ΠΉ Π°ΠΌΠ±ΡΡΠ½Ρ ΡΡΠ»Ρ. ΠΠ°ΡΡ Π»ΡΠ΄ΠΈ β Π½Π°ΠΉΡΡΠ½Π½ΡΡΠΈΠΉ Π°ΠΊΡΠΈΠ² ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ, ΡΠΎΠΆ ΠΌΠΈ ΠΎΠ±ΠΈΡΠ°ΡΠΌΠΎ Π±ΡΠ΄ΡΠ²Π°ΡΠΈ Π±ΡΠ·Π½Π΅ΡΠΈ ΡΠ°Π·ΠΎΠΌ Π· Π½Π°ΠΉΠΊΡΠ°ΡΠΈΠΌΠΈ ΡΠ°Π»Π°Π½ΡΠ°ΠΌΠΈ Π½Π° ΡΠΈΠ½ΠΊΡ.
ΠΠ°ΡΠ°Π· ΠΌΠΈ Ρ ΠΏΠΎΡΡΠΊΡ Data Scientist Ρ Π½Π°ΡΡ ΠΏΠΎΡΡΡΠ΅Π»ΡΠ½Ρ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ TENTENS Tech.
TENTENS Tech β ΡΠΊΡΠ°ΡΠ½ΡΡΠΊΠ° IT-ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ, ΡΠΎ ΡΠΎΠ·ΡΠΎΠ±Π»ΡΡ ΠΏΠ»Π°ΡΡΠΎΡΠΌΠΈ Ρ ΡΡΠ΅ΡΡ ΡΡΡΡΠΌΡΠ½Π³Ρ ΡΠ° social discovery. ΠΠΎΠΌΠ°Π½Π΄Π° TENTENS Tech ΠΌΠ°Ρ Π΄ΠΎΡΠ²ΡΠ΄ ΡΡΠΏΡΡΠ½ΠΈΡ Π·Π°ΠΏΡΡΠΊΡΠ² Π΄Π΅ΡΡΡΠΊΡΠ² ΠΏΠ»Π°ΡΡΠΎΡΠΌ, ΡΠΊΠΈΠΌΠΈ ΠΊΠΎΡΠΈΡΡΡΡΡΡΡΡ ΠΌΡΠ»ΡΠΉΠΎΠ½ΠΈ Π»ΡΠ΄Π΅ΠΉ Π½Π° Π²ΡΡΡ ΠΊΠΎΠ½ΡΠΈΠ½Π΅Π½ΡΠ°Ρ ΡΠ²ΡΡΡ (ΠΎΠΊΡΡΠΌ ΠΠ½ΡΠ°ΡΠΊΡΠΈΠ΄ΠΈ, ΠΏΠΎΠΊΠΈ ΡΠΎ). Π ΡΠ»ΠΎΠ³Π°Π½ We donβt think limits Π²ΡΠ΄ΠΎΠ±ΡΠ°ΠΆΠ°Ρ ΡΠΊ ΡΡΡΠ°ΡΠ΅Π³ΡΡΠ½Π΅ ΠΌΠΈΡΠ»Π΅Π½Π½Ρ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ, ΡΠ°ΠΊ Ρ ΠΌΠΎΡΠΈΠ²Π°ΡΡΡ ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ Π·Π°Π²ΠΆΠ΄ΠΈ ΠΌΡΠ°ΡΠΈ Π΄Π°Π»Π΅ΠΊΠΎ Π·Π° Π³ΠΎΡΠΈΠ·ΠΎΠ½Ρ. ΠΠ°ΡΠΌΠΎ ΠΏΠΎΡΡΠΆΠ½Ρ ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ Π°Π½Π°Π»ΡΡΠΈΠΊΠΈ Π· 20+ ΡΠΏΡΠ²ΡΠΎΠ±ΡΡΠ½ΠΈΠΊΡΠ².
Π¨ΡΠΊΠ°ΡΠΌΠΎ: ΡΠ°Π»Π°Π½ΠΎΠ²ΠΈΡΠΎΠ³ΠΎ data scientist-Π° Π· Π΄ΠΎΡΠ²ΡΠ΄ΠΎΠΌ ΡΠΎΠ±ΠΎΡΠΈ ΡΡΠΊ+, ΡΠΊΠΈΠΉ full-time Π±ΡΠ΄Π΅ ΡΠ°Π·ΠΎΠΌ Π· ΠΊΠΎΠ»Π΅Π³Π°ΠΌΠΈ ΡΠΎΠ·ΡΠΎΠ±Π»ΡΡΠΈ Π΅ΡΠ΅ΠΊΡΠΈΠ²Π½Ρ ΠΌΠΎΠ΄Π΅Π»Ρ Π΄Π»Ρ ΠΏΠΎΡΡΠ΅Π± Π±ΡΠ·Π½Π΅ΡΡ Ρ ΡΠΎΠ·Π²ΠΈΠ²Π°ΡΠΈ Π²ΠΆΠ΅ Π½Π°ΡΠ²Π½Ρ. Π£ Π²Π°Ρ Π±ΡΠ΄Π΅ ΠΌΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ ΠΏΡΠ°ΡΡΠ²Π°ΡΠΈ ΡΠ· ΡΡΠ·Π½ΠΎΠΌΠ°Π½ΡΡΠ½ΠΈΠΌΠΈ ΡΠΈΠΏΠ°ΠΌΠΈ Π΄Π°Π½ΠΈΡ ΡΠ° ΠΌΠΎΠ΄Π΅Π»ΡΠΌΠΈ, Π·Π°ΡΡΠΎΡΠΎΠ²ΡΠ²Π°ΡΠΈ Π½ΠΎΠ²ΡΡΠ½Ρ ΠΏΡΠ΄Ρ ΠΎΠ΄ΠΈ Π² ΠΎΠ±Π»Π°ΡΡΡ AΠ ΡΠ° ΠΏΡΠΎΠΏΠΎΠ½ΡΠ²Π°ΡΠΈ Π½Π°ΠΉΠΊΡΠ°ΡΡ ΡΡΡΠ΅Π½Π½Ρ Π΄Π»Ρ ΠΏΠΎΡΡΠ΅Π± Π±ΡΠ·Π½Π΅ΡΡ.
Π―ΠΊΡ Π²ΠΈΠΊΠ»ΠΈΠΊΠΈ ΡΠ΅ΠΊΠ°ΡΡΡ Π½Π° ΡΠ΅Π±Π΅ Π² ΡΠΎΠ»Ρ Data Scientist:
β Π ΠΎΠ·Π²ΠΈΡΠΎΠΊ Π²ΠΆΠ΅ Π½Π°ΡΠ²Π½ΠΈΡ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ Ρ ΡΡ ΡΠ΅ΡΠ²ΡΡΡΠ² (Π°Π½ΡΠΈΡΡΠΎΠ΄, ΠΎΡΡΠ½ΠΊΠ° ΠΌΠ°ΡΠΊΠ΅ΡΠΈΠ½Π³Ρ, ΠΌΠΎΠ΄Π΅ΡΠ°ΡΡΡ);
β Π‘ΠΏΡΠ²ΠΏΡΠ°ΡΡ Π· Π°Π½Π°Π»ΡΡΠΈΠΊΠ°ΠΌΠΈ, DE-ΡΠΏΠ΅ΡΡΠ°Π»ΡΡΡΠ°ΠΌΠΈ ΡΠ° ΡΠ½ΡΠΈΠΌΠΈ ΡΠ½ΠΆΠ΅Π½Π΅ΡΠ°ΠΌΠΈ Π΄Π»Ρ ΠΏΠΎΠ±ΡΠ΄ΠΎΠ²ΠΈ ΡΠΊΠ»Π°Π΄Π½ΠΈΡ ML-ΠΏΠ°ΠΉΠΏΠ»Π°ΠΉΠ½ΡΠ²;
β End-to-end ΡΠΎΠ·ΡΠΎΠ±ΠΊΠ° Ρ Π²ΠΏΡΠΎΠ²Π°Π΄ΠΆΠ΅Π½Π½Ρ Π½ΠΎΠ²ΠΈΡ ML-ΡΠ΅ΡΠ²ΡΡΡΠ²;
β ΠΠ°Π±Π΅Π·ΠΏΠ΅ΡΠ΅Π½Π½Ρ ΡΠΊΠΎΡΡΡ ΠΏΠ°ΠΉΠΏΠ»Π°ΠΉΠ½ΡΠ² ΡΡΠ΅Π½ΡΠ²Π°Π½Π½Ρ ΡΠ° ΡΠ΅ΡΡΡΠ²Π°Π½Π½Ρ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ;
β Π ΠΎΠ·ΡΠΎΠ±ΠΊΠ° Ρ Π²ΠΏΡΠΎΠ²Π°Π΄ΠΆΠ΅Π½Π½Ρ ΡΠΈΡΡΠ΅ΠΌ ΠΌΠΎΠ½ΡΡΠΎΡΠΈΠ½Π³Ρ ΡΠΎΠ±ΠΎΡΠΈ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ;
Π©ΠΎ Π΄Π»Ρ Π½Π°Ρ Π²Π°ΠΆΠ»ΠΈΠ²ΠΎ:
β ΠΠΏΠ΅Π²Π½Π΅Π½Π΅ Π²ΠΎΠ»ΠΎΠ΄ΡΠ½Π½Ρ SQL ΡΠ° Python;
β ΠΠΏΠ΅Π²Π½Π΅Π½Ρ Π·Π½Π°Π½Π½Ρ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΠΊΠΈ, ΡΠ΅ΠΎΡΡΡ ΠΉΠΌΠΎΠ²ΡΡΠ½ΠΎΡΡΠ΅ΠΉ ΡΠ° ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠΈ;
β ΠΠ½Π°Π½Π½Ρ ΠΊΠ»Π°ΡΠΈΡΠ½ΠΈΡ ML-Π°Π»Π³ΠΎΡΠΈΡΠΌΡΠ²;
β ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· ML-ΡΡΠ΅ΠΉΠΌΠ²ΠΎΡΠΊΠ°ΠΌΠΈ ΡΠ° Π±ΡΠ±Π»ΡΠΎΡΠ΅ΠΊΠ°ΠΌΠΈ Π΄Π»Ρ ΠΎΠ±ΡΠΎΠ±ΠΊΠΈ Π΄Π°Π½ΠΈΡ ;
β ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· GCP/Azure/AWS;
β ΠΠΎΡΠ²ΡΠ΄ ΡΠ΅Π°Π»ΡΠ·Π°ΡΡΡ Ρ ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΠΈ ΠΏΠ°ΠΉΠΏΠ»Π°ΠΉΠ½ΡΠ² ΡΡΠ΅Π½ΡΠ²Π°Π½Π½Ρ/ΡΠ΅ΡΡΡΠ²Π°Π½Π½Ρ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ;
β ΠΠ½Π°Π½Π½Ρ Docker, ΡΠΎΠ·ΡΠΌΡΠ½Π½Ρ IaS;
β ΠΠΊΡΡΠ°ΡΠ½ΡΡΡΡ, ΡΠ²Π°Π³Π° Π΄ΠΎ Π΄Π΅ΡΠ°Π»Π΅ΠΉ, ΠΊΡΠΈΡΠΈΡΠ½Π΅ ΠΌΠΈΡΠ»Π΅Π½Π½Ρ;
β ΠΠΌΡΠ½Π½Ρ ΠΏΡΠ°ΡΡΠ²Π°ΡΠΈ Π² ΠΊΠΎΠΌΠ°Π½Π΄Ρ.
SKELAR foundation β Π±Π»Π°Π³ΠΎΠ΄ΡΠΉΠ½ΠΈΠΉ ΡΠΎΠ½Π΄, ΡΡΠ²ΠΎΡΠ΅Π½ΠΈΠΉ ΡΠΏΡΠ²ΡΠΎΠ±ΡΡΠ½ΠΈΠΊΠ°ΠΌΠΈ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ. Π ΠΌΠ΅ΠΆΠ°Ρ ΡΠ½ΡΡΡΠ°ΡΠΈΠ²ΠΈ ΡΡΠ²ΠΎΡΡΡΠΌΠΎ ΡΠ° ΡΡΠ½Π°Π½ΡΡΡΠΌΠΎ ΠΏΡΠΎΡΠΊΡΠΈ, ΡΠΎ ΡΠΏΡΠΈΡΡΡΡ ΠΏΠΎΠ΄ΠΎΠ»Π°Π½Π½Ρ Π½Π°ΡΠ»ΡΠ΄ΠΊΡΠ² Π²ΡΠΉΠ½ΠΈ ΡΠ° Π²ΡΠ΄Π½ΠΎΠ²Π»Π΅Π½Π½Ρ Π£ΠΊΡΠ°ΡΠ½ΠΈ.
SKELAR β ΡΠ΅ΡΠ΅Π΄ΠΎΠ²ΠΈΡΠ΅ Π΄Π»Ρ ΡΠ°ΠΌΠΎΡΠ΅Π°Π»ΡΠ·Π°ΡΡΡ Π»ΡΠ΄Π΅ΠΉ, ΡΠΊΡ Π·Π΄Π°ΡΠ½Ρ ΡΡΠ²ΠΎΡΠΈΡΠΈ ΡΡΠΏΡΡΠ½Ρ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ. ΠΠΈ ΡΠ°ΠΊΡ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ Π½Π°Π·ΠΈΠ²Π°ΡΠΌΠΎ the next big everything. ΠΡΡΠΈΠΌΠΎ Π² ΡΡ ΠΏΠΎΡΡΠΆΠ½ΡΡΡΡ ΡΠ° ΠΌΠ°ΡΡΡΠ°Π±.ΠΠΈ ΠΏΠ»Π°Π½ΡΡΠΌΠΎ ΠΉ Π½Π°Π΄Π°Π»Ρ ΡΠΎΠ·Π²ΠΈΠ²Π°ΡΠΈ tech-Π±ΡΠ·Π½Π΅ΡΠΈ, ΠΏΡΠ΄ΠΊΠΎΡΡΠ²Π°ΡΠΈ Π³Π»ΠΎΠ±Π°Π»ΡΠ½Ρ ΡΠΈΠ½ΠΊΠΈ ΡΠ° ΠΏΡΠ°ΡΡΠ²Π°ΡΠΈ Π·Π°Π΄Π»Ρ ΠΏΠ΅ΡΠ΅ΠΌΠΎΠ³ΠΈ Π£ΠΊΡΠ°ΡΠ½ΠΈ πΊπ¦
ΠΠ»Ρ ΡΡΠΎΠ³ΠΎ ΡΡΠ²ΠΎΡΠΈΠ»ΠΈ Π²ΡΡ ΠΌΠΎΠΆΠ»ΠΈΠ²ΠΎΡΡΡ Π²ΡΠ΅ΡΠ΅Π΄ΠΈΠ½Ρ Π½Π°ΡΠΎΠ³ΠΎ Π²Π΅Π½ΡΡΡ Π±ΡΠ»Π΄Π΅ΡΠ°:
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β 8 ΡΠ½ΡΡΠ°ΡΡΡΡΠΊΡΡΡΠ½ΠΈΡ ΠΊΠΎΠΌΠ°Π½Π΄, ΡΠΊΡ Π΄ΠΎΠΏΠΎΠΌΠ°Π³Π°ΡΡΡ Π±ΡΠ·Π½Π΅ΡΠ°ΠΌ Π·Π°ΠΊΡΠΈΠ²Π°ΡΠΈ Π±ΡΠ΄Ρ-ΡΠΊΡ ΠΏΠΈΡΠ°Π½Π½Ρ: Π²ΡΠ΄ ΡΠ΅ΠΊΡΡΡΠΈΠ½Π³Ρ Ρ ΠΊΠΎΠΌΡΠ½ΡΠΊΠ°ΡΡΠΉ Π΄ΠΎ ΡΡΠ½Π°Π½ΡΡΠ² ΡΠ° ΡΡΠΈΠ΄ΠΈΡΠ½ΠΈΡ ΠΏΠΈΡΠ°Π½Ρ;
β Π‘ΠΏΡΠ»ΡΠ½ΠΎΡΠ° ΡΠ°ΡΠ½Π΄Π΅ΡΡΠ², ΡΠΊΡ Π²ΠΆΠ΅ Π·Π°ΠΏΡΡΡΠΈΠ»ΠΈ Π½Π΅ ΠΎΠ΄ΠΈΠ½ Π±ΡΠ·Π½Π΅Ρ ΠΉ ΠΌΠΎΠΆΡΡΡ Π΄ΡΠ»ΠΈΡΠΈΡΡ ΠΏΡΠ°ΠΊΡΠΈΡΠ½ΠΈΠΌ Π΄ΠΎΡΠ²ΡΠ΄ΠΎΠΌ;
β ΠΠ½ΡΡΡΡΡΠ½Ρ ΠΊΠ»ΡΠ±ΠΈ Π·Π° ΠΏΡΠΎΡΠ΅ΡΡΠΉΠ½ΠΈΠΌΠΈ Π½Π°ΠΏΡΡΠΌΠΊΠ°ΠΌΠΈ: ΠΌΠ°ΡΠΊΠ΅ΡΠΈΠ½Π³, ΡΠΎΠ·ΡΠΎΠ±ΠΊΠ°, ΡΡΠ½Π°Π½ΡΠΈ, ΡΠ΅ΠΊΡΡΡΠΈΠ½Π³;
β Π’ΡΠ΅Π½ΡΠ½Π³ΠΈ, ΠΊΡΡΡΠΈ, Π²ΡΠ΄Π²ΡΠ΄ΡΠ²Π°Π½Π½Ρ ΠΊΠΎΠ½ΡΠ΅ΡΠ΅Π½ΡΡΠΉ;
β ΠΠ΅Π΄ΠΈΡΠ½Π΅ ΡΡΡΠ°Ρ ΡΠ²Π°Π½Π½Ρ, ΠΊΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½ΠΈΠΉ Π»ΡΠΊΠ°Ρ.
ΠΠ°Π²Π°ΠΉ ΡΠ°Π·ΠΎΠΌ Π±ΡΠ΄ΡΠ²Π°ΡΠΈ the next big everything! -
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Full Remote Β· Ukraine Β· 1 year of experience Β· Upper-IntermediateΠΠΈ ΡΡΠΊΠ°ΡΠΌΠΎ ΡΠ°Π»Π°Π½ΠΎΠ²ΠΈΡΠΎΠ³ΠΎ Intern Data Scientist, ΡΠΊΠΈΠΉ Π·Π°Ρ ΠΎΠΏΠ»ΡΡΡΡΡΡ Π°Π½Π°Π»ΡΠ·ΠΎΠΌ Π΄Π°Π½ΠΈΡ , ΠΏΡΠ°Π³Π½Π΅ ΡΠΎΠ·Π²ΠΈΠ²Π°ΡΠΈ ΠΏΡΠ°ΠΊΡΠΈΡΠ½Ρ Π½Π°Π²ΠΈΡΠΊΠΈ Π² ΠΏΠΎΠ±ΡΠ΄ΠΎΠ²Ρ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ, ΡΠΎΠ±ΠΎΡΡ Π· Π²Π΅Π»ΠΈΠΊΠΈΠΌΠΈ ΠΌΠ°ΡΠΈΠ²Π°ΠΌΠΈ ΡΠ½ΡΠΎΡΠΌΠ°ΡΡΡ ΡΠ° ΠΌΠ°Ρ Π±Π°ΠΆΠ°Π½Π½Ρ Π½Π°Π²ΡΠ°ΡΠΈΡΡ ΠΉ Π·ΡΠΎΡΡΠ°ΡΠΈ ΡΠ°Π·ΠΎΠΌ ΡΠ· ΠΊΠΎΠΌΠ°Π½Π΄ΠΎΡ. Π¦Π΅ ΡΡΠ΄ΠΎΠ²Π° ΠΌΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ Π΄Π»Ρ...ΠΠΈ ΡΡΠΊΠ°ΡΠΌΠΎ ΡΠ°Π»Π°Π½ΠΎΠ²ΠΈΡΠΎΠ³ΠΎ Intern Data Scientist, ΡΠΊΠΈΠΉ Π·Π°Ρ ΠΎΠΏΠ»ΡΡΡΡΡΡ Π°Π½Π°Π»ΡΠ·ΠΎΠΌ Π΄Π°Π½ΠΈΡ , ΠΏΡΠ°Π³Π½Π΅ ΡΠΎΠ·Π²ΠΈΠ²Π°ΡΠΈ ΠΏΡΠ°ΠΊΡΠΈΡΠ½Ρ Π½Π°Π²ΠΈΡΠΊΠΈ Π² ΠΏΠΎΠ±ΡΠ΄ΠΎΠ²Ρ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ, ΡΠΎΠ±ΠΎΡΡ Π· Π²Π΅Π»ΠΈΠΊΠΈΠΌΠΈ ΠΌΠ°ΡΠΈΠ²Π°ΠΌΠΈ ΡΠ½ΡΠΎΡΠΌΠ°ΡΡΡ ΡΠ° ΠΌΠ°Ρ Π±Π°ΠΆΠ°Π½Π½Ρ Π½Π°Π²ΡΠ°ΡΠΈΡΡ ΠΉ Π·ΡΠΎΡΡΠ°ΡΠΈ ΡΠ°Π·ΠΎΠΌ ΡΠ· ΠΊΠΎΠΌΠ°Π½Π΄ΠΎΡ. Π¦Π΅ ΡΡΠ΄ΠΎΠ²Π° ΠΌΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ Π΄Π»Ρ ΡΡΠ°ΡΡΡ ΠΊΠ°ΡβΡΡΠΈ Ρ ΡΡΠ΅ΡΡ Data Science!
ΠΡΠ½ΠΎΠ²Π½Ρ ΠΎΠ±ΠΎΠ²βΡΠ·ΠΊΠΈ
- ΠΠ±ΡΡ, ΠΊΠΎΠ½ΡΠΎΠ»ΡΠ΄Π°ΡΡΡ ΡΠ° ΠΏΡΠ΄Π³ΠΎΡΠΎΠ²ΠΊΠ° Π΄Π°Π½ΠΈΡ Π· ΡΡΠ·Π½ΠΈΡ Π΄ΠΆΠ΅ΡΠ΅Π».
- Π£ΡΠ°ΡΡΡ Ρ ΡΠΎΠ·ΡΠΎΠ±ΡΡ ΡΠ° ΠΏΠΎΠΊΡΠ°ΡΠ΅Π½Π½Ρ Π°Π»Π³ΠΎΡΠΈΡΠΌΡΠ² Π°Π½Π°Π»ΡΠ·Ρ Π΄Π°Π½ΠΈΡ .
- ΠΠΎΠ±ΡΠ΄ΠΎΠ²Π° Π±Π°Π·ΠΎΠ²ΠΈΡ Π°Π½Π°Π»ΡΡΠΈΡΠ½ΠΈΡ ΡΠ° ΠΏΡΠΎΠ³Π½ΠΎΠ·Π½ΠΈΡ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ.
- Π‘ΡΠ²ΠΎΡΠ΅Π½Π½Ρ Π·Π°ΠΏΠΈΡΡΠ² Π΄ΠΎ Π±Π°Π· Π΄Π°Π½ΠΈΡ Ρ ΠΏΡΠ΄Π³ΠΎΡΠΎΠ²ΠΊΠ° Π·Π²ΡΡΡΠ².
- ΠΡΠ·ΡΠ°Π»ΡΠ·Π°ΡΡΡ Π΄Π°Π½ΠΈΡ Π΄Π»Ρ Π²Π½ΡΡΡΡΡΠ½ΡΡ ΠΊΠΎΠΌΠ°Π½Π΄ ΡΠ° ΠΏΡΠΈΠΉΠ½ΡΡΡΡ ΡΡΡΠ΅Π½Ρ.
- ΠΠΈΠ²ΡΠ΅Π½Π½Ρ ΡΠ° Π·Π°ΡΡΠΎΡΡΠ²Π°Π½Π½Ρ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ½ΠΈΡ ΠΏΡΠ΄Ρ ΠΎΠ΄ΡΠ² Π΄ΠΎ Π°Π½Π°Π»ΡΠ·Ρ.
- Π‘ΠΏΡΠ²ΠΏΡΠ°ΡΡ Π· Π°Π½Π°Π»ΡΡΠΈΠΊΠ°ΠΌΠΈ, ΡΠΎΠ·ΡΠΎΠ±Π½ΠΈΠΊΠ°ΠΌΠΈ ΡΠ° Π±ΡΠ·Π½Π΅Ρ-ΠΊΠΎΠΌΠ°Π½Π΄Π°ΠΌΠΈ.
- ΠΠ²ΡΠΎΠΌΠ°ΡΠΈΠ·Π°ΡΡΡ ΠΏΠΎΠ²ΡΠΎΡΡΠ²Π°Π½ΠΈΡ Π°Π½Π°Π»ΡΡΠΈΡΠ½ΠΈΡ Π·Π°Π΄Π°Ρ.ΠΠΈΠΌΠΎΠ³ΠΈ Π΄ΠΎ ΠΊΠ°Π½Π΄ΠΈΠ΄Π°ΡΠ°
- ΠΠ°ΡΠ²Π½ΡΡΡΡ Π·Π°ΠΊΡΠ½ΡΠ΅Π½ΠΎΡ Π²ΠΈΡΠΎΡ ΠΎΡΠ²ΡΡΠΈ Π·Π° ΡΠΏΠ΅ΡΡΠ°Π»ΡΠ½ΡΡΡΡ
- ΠΠΎΡΠ²ΡΠ΄ Π½Π°Π²ΡΠ°Π½Π½Ρ Π°Π±ΠΎ ΡΡΠ°ΠΆΡΠ²Π°Π½Π½Ρ Ρ ΡΡΠ΅ΡΡ Data Science / Π°Π½Π°Π»ΡΡΠΈΠΊΠΈ Π΄Π°Π½ΠΈΡ .
- ΠΠ°Π·ΠΎΠ²Π΅ Π²ΠΎΠ»ΠΎΠ΄ΡΠ½Π½Ρ Python (pandas, numpy, sklearn ΡΠΎΡΠΎ).
- ΠΠ½Π°Π½Π½Ρ ΠΎΡΠ½ΠΎΠ² SQL ΡΠ° Π²ΠΌΡΠ½Π½Ρ ΠΏΡΠ°ΡΡΠ²Π°ΡΠΈ Π· Π±Π°Π·Π°ΠΌΠΈ Π΄Π°Π½ΠΈΡ .
- Π ΠΎΠ·ΡΠΌΡΠ½Π½Ρ Π±Π°Π·ΠΎΠ²ΠΈΡ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ½ΠΈΡ ΠΌΠ΅ΡΠΎΠ΄ΡΠ² ΡΠ° ΠΏΡΠΈΠ½ΡΠΈΠΏΡΠ² ΠΌΠΎΠ΄Π΅Π»ΡΠ²Π°Π½Π½Ρ.
- ΠΠ°Π²ΠΈΡΠΊΠΈ Π²ΡΠ·ΡΠ°Π»ΡΠ·Π°ΡΡΡ Π΄Π°Π½ΠΈΡ (Power BI, Tableau Π°Π±ΠΎ matplotlib/seaborn).
- ΠΠ°ΠΆΠ°Π½Π½Ρ Π½Π°Π²ΡΠ°ΡΠΈΡΡ ΠΉ ΡΠΎΠ·Π²ΠΈΠ²Π°ΡΠΈ Π½Π°Π²ΠΈΡΠΊΠΈ ΡΠΎΠ±ΠΎΡΠΈ Π· Big Data ΡΠ° Ρ ΠΌΠ°ΡΠ½ΠΈΠΌΠΈ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΡΠΌΠΈ (Azure β Π±ΡΠ΄Π΅ ΠΏΠ΅ΡΠ΅Π²Π°Π³ΠΎΡ).
- ΠΠ½Π°Π»ΡΡΠΈΡΠ½Π΅ ΠΌΠΈΡΠ»Π΅Π½Π½Ρ, ΡΠ²Π°ΠΆΠ½ΡΡΡΡ Π΄ΠΎ Π΄Π΅ΡΠ°Π»Π΅ΠΉ, ΡΠ½ΡΡΡΠ°ΡΠΈΠ²Π½ΡΡΡΡ.ΠΠΈ ΠΏΡΠΎΠΏΠΎΠ½ΡΡΠΌΠΎ
- ΠΠ½ΡΡΠΊΠΈΠΉ Π³ΡΠ°ΡΡΠΊ ΡΠ° Π²ΡΠ΄Π΄Π°Π»Π΅Π½ΠΈΠΉ ΡΠΎΡΠΌΠ°Ρ ΡΠΎΠ±ΠΎΡΠΈ.
- Π ΠΎΠ±ΠΎΡΡ Π½Π°Π΄ ΡΠ΅Π°Π»ΡΠ½ΠΈΠΌΠΈ ΠΊΠ΅ΠΉΡΠ°ΠΌΠΈ ΠΉ Π°Π½Π°Π»ΡΡΠΈΡΠ½ΠΈΠΌΠΈ Π·Π°Π΄Π°ΡΠ°ΠΌΠΈ.
- ΠΠ΅Π½ΡΠΎΡΡΡΠ²ΠΎ Π· Π±ΠΎΠΊΡ Π΄ΠΎΡΠ²ΡΠ΄ΡΠ΅Π½ΠΈΡ Data Scientist-ΡΠ².
- ΠΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ ΠΏΠΎΠ΄Π°Π»ΡΡΠΎΠ³ΠΎ ΠΏΡΠ°ΡΠ΅Π²Π»Π°ΡΡΡΠ²Π°Π½Π½Ρ Π·Π° ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌΠΈ ΡΡΠ°ΠΆΡΠ²Π°Π½Π½Ρ.
- ΠΡΡΠΆΠ½Ρ Π°ΡΠΌΠΎΡΡΠ΅ΡΡ ΡΠ° ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΡ ΡΠΎΠ·Π²ΠΈΡΠΊΡ.Π―ΠΊΡΠΎ ΡΠΈ Ρ ΠΎΡΠ΅Ρ ΡΠΎΠ·ΠΏΠΎΡΠ°ΡΠΈ ΠΊΠ°ΡβΡΡΡ Π² Data Science, ΠΏΡΠ°ΡΡΠ²Π°ΡΠΈ Π· Π΄Π°Π½ΠΈΠΌΠΈ, ΡΠΎ ΠΌΠ°ΡΡΡ Π·Π½Π°ΡΠ΅Π½Π½Ρ, Ρ Π·ΡΠΎΡΡΠ°ΡΠΈ ΠΏΡΠΎΡΠ΅ΡΡΠΉΠ½ΠΎ β Π½Π°Π΄ΡΠΈΠ»Π°ΠΉ ΡΠ²ΠΎΡ ΡΠ΅Π·ΡΠΌΠ΅!