Jobs
14-
Β· 46 views Β· 4 applications Β· 26d
Middle Data Scientist
Full Remote Β· Countries of Europe or Ukraine Β· Product Β· 2 years of experience Β· IntermediateIn Competera, we are building a place where optimal pricing decisions can be made easily. We believe that AI technologies will soon drive all challenging decisions and are capable of helping humans be better. We are now looking for a Middle Data Scientist...In Competera, we are building a place where optimal pricing decisions can be made easily. We believe that AI technologies will soon drive all challenging decisions and are capable of helping humans be better. We are now looking for a Middle Data Scientist to change the way we deliver our solution to customers.
You could be a perfect match for the position if
You want to:
- Validate datasets to ensure data accuracy and consistency.
- Design proof-of-concept (POC) solutions to explore new approaches.
- Develop technical solutions by mapping requirements to existing tools and functionalities.
- Train models and create custom approaches for new domains.
- Troubleshoot data processing and model performance issues.
You have:
- 2+ years of experience in data science or a related field.
- Strong SQL skills for data manipulation and extraction.
- Proficiency in Python, with the ability to write modular and readable code for experiments and prototypes.
- A solid mathematical background, preferably in a Computer Science-related field.
- Expertise in scientific Python libraries, including NumPy, pandas, scikit-learn, and either Keras/TensorFlow or PyTorch.
- Familiarity with Time Series Forecasting methodologies.
- Experience in statistical testing, including A/B testing.
- 1+ years working with tabular and multimodal data (e.g., combining tabular data with text, audio, or images).
- Upper-intermediate or higher English level.
Soft skills:
- Analytical mindset and critical thinking to solve complex problems.
- Agile approach, with the ability to experiment and test hypotheses in a dynamic business environment.
- Business-oriented thinking, capable of translating complex models into clear business insights.
- Curiosity and a drive for continuous learning in the data domain.
- Strong team player, able to collaborate across cross-functional teams.
Youβre gonna love it, and hereβs why:
- Rich innovative software stack, freedom to choose the best suitable technologies.
- Remote-first ideology: freedom to operate from the home office or any suitable coworking.
- Flexible working hours (we start from 8 to 11 am) and no time tracking systems on.
- Regular performance and compensation reviews.
- Recurrent 1-1s and measurable OKRs.
- In-depth onboarding with a clear success track.
- Competera covers 70% of your training/course fee.
- 20 vacation days, 15 days off, and up to one week of paid Christmas holidays.
- 20 business days of sick leave.
- Partial medical insurance coverage.
- We reimburse the cost of coworking.
Drive innovations with us. Be a Competerian.
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Β· 33 views Β· 0 applications Β· 11d
Senior Data Scientist
Worldwide Β· Product Β· 4 years of experience Β· IntermediatePIN-UP Global is an international holding specializing in the development and implementation of advanced technologies, B2B solutions and innovative products for the iGaming industry. Our holding is represented in seven countries (Cyprus, Ukraine, Poland,...PIN-UP Global is an international holding specializing in the development and implementation of advanced technologies, B2B solutions and innovative products for the iGaming industry.
Our holding is represented in seven countries (Cyprus, Ukraine, Poland, Kazakhstan, Armenia, Peru, Malta). The headquarters of the holding is located in Cyprus.
We are looking for a Senior Data Scientist to join our team!
Requirements:
- Proven experience in analysis of large amount of data;
- Experience in solving customer behavior prediction tasks;
- Knowledge of ML algorithms for regression, classification, clustering, ability to apply them to business problems;
- Solid experience in SQL;
- Extensive experience with Python for data processing, modeling, and visualization;
- Deep understanding of statistics, probability theory;
- Passion to drive projects from R&D to business value.
Will be plus:
- Familiarity with AWS infrastructure and toolchain (SageMaker, CloudFormation, CloudWatch, etc.);
- Familiarity with BI tools (Metabase, Tableau);
- Experience with tools for managing ML workflows;
- Knowledge and experience with algorithms and data structures;
- Knowledge of OOP;
- Understanding of containerization technologies (Docker);
- Participation in Kaggle competitions.
Responsibilities:
- Developing and testing machine learning models for customer behavior prediction, deploying to production;
- Working with tabular data collecting, cleaning, and exploring datasets, building ML pipelines;
- Applying statistical methods to analyze and interpret data, finding patterns, insights to improve quality of models;
- Maintaining existing models;
- Communicating and presenting results.
Benefits:
π An exciting and challenging job in a fast-growing product holding, the opportunity to be part of a multicultural team of top professionals in Development, Engineering and Architecture, Management, Operations, Marketing, etc;
π€ Great working atmosphere with passionate IT experts and leaders, sharing a friendly culture and a success-driven mindset is guaranteed;
πBeautiful offices in Limassol, Warsaw, Almaty, Yerevan β work with comfort and enjoy the opportunity to build a network of connections with IT professionals day by day;
π§βπ» Laptop & all necessary equipment for work according to the ecosystem standards;
π Paid vacations, personal events days, days off;
π« Paid sick leave;
π¨ββ Medical insurance;
π΅ Referral program β enjoy cooperation with your colleagues and get a bonus;
π Educational support by our L&D team: internal and external trainings and conferences, courses on Udemy;
π¦ Multiple internal activities: online platform with newsletters, quests, gamification, and presents for collecting bonuses, PIN-UP talks club for movie and book lovers, board games cozy evenings, special office days dedicated to holidays, etc;
π³ Company events, team buildings.
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Β· 49 views Β· 1 application Β· 20d
Computer Vision Engineer
Office Work Β· Ukraine (Kyiv) Β· Product Β· 5 years of experience Β· Intermediate MilTech πͺOverview We are seeking a highly skilled and experienced Senior/Lead Computer Vision Engineer specializing in Navigation to join our innovative R&D team. In this pivotal role, you will drive the development and deployment of state-of-the-art computer...Overview
We are seeking a highly skilled and experienced Senior/Lead Computer Vision Engineer specializing in Navigation to join our innovative R&D team. In this pivotal role, you will drive the development and deployment of state-of-the-art computer vision algorithms for autonomous navigation systems, contributing to our efforts in robotics, autonomous vehicles, drones, or similar fields. You will work cross-functionally with engineering, product, and research teams to deliver robust, real-time solutions that enable safe and intelligent navigation in dynamic environments.
Responsibilities- Lead the design, development, and optimization of computer vision algorithms for localization, mapping, and navigation.
- Develop and implement algorithms for object detection, segmentation, SLAM, 3D scene reconstruction, visual odometry, and sensor fusion (using cameras, LiDAR, IMUs, etc.).
- Guide the integration of computer vision modules with navigation and control systems, ensuring seamless operation in real-world conditions.
- Collaborate with software, hardware, and product teams to define requirements and deliver scalable, robust navigation solutions.
- Stay current with advancements in deep learning, computer vision, and robotics, and introduce relevant state-of-the-art techniques into the product.
- Design and execute experiments to evaluate performance and robustness; analyze results and iterate on solutions.
- Prepare technical documentation, progress reports, and presentations for internal and external stakeholders.
Requirements- 5+ years of experience in computer vision, preferably in navigation, robotics, or autonomous systems.
- Masterβs or PhD in Computer Science, Robotics, Electrical Engineering, or related field.
- Strong proficiency in Python and/or C++.
- Hands-on experience with deep learning frameworks (e.g., PyTorch, TensorFlow) and classical computer vision libraries (e.g., OpenCV, PCL).
- Experience in deploying and optimizing models for single-board computers such as Raspberry Pi, Nvidia Jetson
- Proven track record of developing and deploying real-time vision algorithms for navigation tasks in challenging environments.
- Extensive knowledge of SLAM, visual odometry, sensor fusion, and related algorithms.
- Experience with ROS, embedded systems, and real-time software development is a plus.
- Excellent problem-solving skills, strong analytical mindset, and effective communication abilities.
Preferred Qualifications- Knowledge of SLAM and related models.
- Familiarity with the MAVLink protocol and ArduPilot.
- Familiarity with edge computing or real-time GPU-based inference.
- Publications or contributions to the open-source community in vision or robotics.
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Β· 56 views Β· 6 applications Β· 16d
Senior Data Science
Full Remote Β· Worldwide Β· Product Β· 5 years of experience Β· IntermediateGypsy is looking for a Senior Data Science β a seeker of patterns, a builder of meaning, and a navigator through metrics, models, and signals. What makes you a great match 5+ years of experience working with large datasets and building complex...Gypsy is looking for a Senior Data Science β a seeker of patterns, a builder of meaning, and a navigator through metrics, models, and signals.
π§ What makes you a great match
- 5+ years of experience working with large datasets and building complex models;
- Python experience: 3+ years. Use of libraries for analysis and modeling: pandas, numpy, scikit-learn, statsmodels;
- SQL experience: 3+ years. Working with relational databases: PostgreSQL, BigQuery;
- Tableau/Superset: 2+ years of experience. Visualization optimization for large datasets;
- Git: Confident user. Code and data version control, conflict resolution, pull requests;
Experience in the iGaming environment.
π Your daily adventures
- Creating and validating hypotheses based on data;
- Using statistical methods to identify patterns;
- Building and optimizing predictive models (Machine Learning, regression, clustering);
- Analyzing changes in key metrics and identifying their causes;
- Developing logic for distinguishing VIP and regular players;
- Designing models for analyzing player activity and player retention;
- Continuously improving data collection, processing, and analysis workflows;
- Developing and maintaining Python scripts to automate processes;
- Ensuring data quality and troubleshooting processing errors.
π Preferred Qualifications
- Experience with A/B testing tools;
- Experience with BigQuery or other analytical DWHs.
Benefits:
πΈ Flexible payment options: choose the method that works best for you;
π§Ύ Tax assistance included: we handle part of your taxes and provide guidance on the local setup;
π Financial perks: Bonuses for holidays, B-day, work milestones and more β just to show we care;
π Learn & grow: We cover courses and certifications β and offer real opportunities to grow your career with us;
π₯ Benefit Π‘afeteria: Choose what suits you β sports, language courses, therapy sessions, and more;
π Stay connected: From team-building events to industry conferences β we bring people together online, offline, and on stage;
π» Modern Equipment: We provide new laptops along with essential peripherals like monitors and headphones for a comfortable workflow;
π Your schedule, your rules: Start your day at 9, 10, or even 11 β we care about results, not clock-ins.
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Β· 52 views Β· 19 applications Β· 17d
Data Scientist
Full Remote Β· Countries of Europe or Ukraine Β· Product Β· 4 years of experience Β· IntermediateΠ¨ΡΠΊΠ°ΡΠΌΠΎ Data Scientist, ΡΠΎ ΠΏΡΠ°ΡΡΠ²Π°ΡΠΈΠΌΠ΅ Π· R&D, ΡΠ΅Π³ΡΠ΅ΡΡΡΠΌΠΈ ΡΠ° ΡΠ°Π±Π»ΠΈΡΠ½ΠΈΠΌΠΈ Π΄Π°Π½ΠΈΠΌΠΈ, zero-to-one ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠΌ Π½Π° Π³Π»ΠΎΠ±Π°Π»ΡΠ½ΠΎΠΌΡ ΡΠΈΠ½ΠΊΡ. ΠΠΏΠ»ΠΈΠ² Π½Π° Π°Π»Π³ΠΎΡΠΈΡΠΌΠΈ ΡΠ° Π±ΡΠ·Π½Π΅Ρ, Π΅ΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠΈ Π±Π΅Π· ΠΎΠ±ΠΌΠ΅ΠΆΠ΅Π½Ρ. Π’ΠΎΠΏΠΎΠ²Ρ ΡΠΌΠΎΠ²ΠΈ, ΠΏΠΎΠ²Π½ΠΈΠΉ ΡΠ΅ΠΌΠΎΡΡ. Main Responsibilities: β Be responsible for...Π¨ΡΠΊΠ°ΡΠΌΠΎ Data Scientist, ΡΠΎ ΠΏΡΠ°ΡΡΠ²Π°ΡΠΈΠΌΠ΅ Π· R&D, ΡΠ΅Π³ΡΠ΅ΡΡΡΠΌΠΈ ΡΠ° ΡΠ°Π±Π»ΠΈΡΠ½ΠΈΠΌΠΈ Π΄Π°Π½ΠΈΠΌΠΈ, zero-to-one ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠΌ Π½Π° Π³Π»ΠΎΠ±Π°Π»ΡΠ½ΠΎΠΌΡ ΡΠΈΠ½ΠΊΡ. ΠΠΏΠ»ΠΈΠ² Π½Π° Π°Π»Π³ΠΎΡΠΈΡΠΌΠΈ ΡΠ° Π±ΡΠ·Π½Π΅Ρ, Π΅ΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠΈ Π±Π΅Π· ΠΎΠ±ΠΌΠ΅ΠΆΠ΅Π½Ρ. Π’ΠΎΠΏΠΎΠ²Ρ ΡΠΌΠΎΠ²ΠΈ, ΠΏΠΎΠ²Π½ΠΈΠΉ ΡΠ΅ΠΌΠΎΡΡ.
Main Responsibilities:
β Be responsible for DS-ML-related solutions developed by all Service and Delivery(SnD) squads: maintenance and improvement of existing solutions; design, development, and implementation of new solutions
β Provide technical leadership for other SnD DSs
β Test developed algorithms, work with the SnD team within UAT, adapt, and reach DOD and DOR
β Verify key tasks done by SnD DS, such as model training and AB test design
β Conduct code reviews for DS-ML-related solutions
β Automate DS work in SnD.Develop data requirements for SnD implementation projects
β Cooperate with IM and DE to achieve and maintain the optimal dataset configuration according to DS-ML needs
β Measure solution stability, quality, and effectiveness; transparently communicate the current state of things to other roles/teams, and suggest and implement improvements
β Regularly collaborate (provide prototypes/ideas/detailed feature requests) with RnD Teams on tools for SnD
Mandatory Requirements:
β A minimum of 3 years of experience in data science or a related field
β Proficiency in SQL for data manipulation and extraction
β Strong Python skills, capable of writing modular and readable code for experiments and prototypes
β A solid mathematical background, preferably in a Computer Science-related field
β Proficiency in the scientific Python toolkit, including NumPy, pandas, scikit-learn, and either Keras/TensorFlow or PyTorch
β Familiarity with Time Series Forecasting approaches
β Experience in statistical testing methodologies, including A/B testing
β At least 3 years of experience working with tabular and mixed (multimodal) data (e.g., tabular data combined with text, audio, or images).
β Upper-intermediate or higher level of English (for future communication with clients and user interviews)
Soft skills
β Analytical mindset and strong critical thinking abilities
β Leadership skills to efficiently organize the data team (Data Scientists, Data Engineers, Data Analysts)
β Agile approach, with the ability to experiment and test hypotheses in an unstable business environment
β Business-oriented mindset, able to translate complex models into simple business language
β Excellent presentation skills, capable of communicating complex ideas effectively
β Curiosity and a commitment to continuous learning within the domain
β Strong team player, able to collaborate effectively with cross-functional teams
We offer:
β Rich innovative software stack, freedom to choose the best suitable technologies
β Remote-first ideology: freedom to operate from the home office or any suitable coworking
β Flexible working hours (we start from 8 to 11 am) and no time tracking systems on
β Regular performance and compensation reviews
β Recurrent 1-1s and measurable OKRs
β In-depth onboarding with a clear success track
β Competera covers 70% of your training/course fee
β 20 vacation days, 15 days off, and up to one week of paid Christmas holidays
β 20 business days of sick leave
β Partial medical insurance coverage
β We reimburse the cost of coworking
More -
Β· 30 views Β· 5 applications Β· 24d
Game Mathematician
Full Remote Β· Countries of Europe or Ukraine Β· Product Β· 5 years of experience Β· 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. We are currently looking for a Game...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.
We are currently looking for a Game Mathematician for Spinlab.
Job Type: Full-Time
About the client:
We help slot gaming leaders unlock the potential of their data, enhancing business outcomes and strengthening their competitive edge in the market. We collect and process data using advanced methods and technologies to provide our clients with clear, actionable recommendations based on real metrics. SpinLabβs goal is not just to collect data but to help businesses use it for maximum efficiency and amplify their performance results.
About the Role:
We are looking for a mathematician with a proven record of creating slot games, who will join our team and help to build data-driven projects on game math optimization. You will have an opportunity to learn playersβ behavior and shape the future vision of our products.
The main task:- is to adapt the mathematics of existing games, not develop new ones;
- It is desirable to be interested in development in the direction of analytics and DS;
- Most of the time will be spent working with AB testing.
Responsibilities:- Develop engaging game mathematics and mechanics in collaboration with Product Owners and Game Developers;
- Fine-tune game mathematics and mechanics to create the best possible product;
- Perform simulations of game math;
- Explore the most popular games from other providers;
- Provide competitive game math analysis to reveal valuable insights;
- Be involved in the game creation process from start to finish;
- Collaborate with development teams, provide game math specifications;
- Be a discussion sparring partner inside our team to help build better products
Requirements:- University degree in mathematics / computer science / technology / engineering;
- At least 5 years commercial experience as a game mathematician (or similar position) in online gambling;
- An understanding of how different slot mechanics work;
- A high level of attention to detail: the ability to spot even small mistakes in your math models and troubleshoot them;
- Brilliant at solving abstract and highly complex math problems;
- Data-driven and analytical mindset in decision-making;
- Teamwork experience;
- English B2+
Will be a plus:- Scripting skills in Python;
- Experience with Git (or other VCS);
- Motivated by challenges and stretch goals;
- Strong mathematical intuition, creativity and curiosity;
- Ability to look at games from playersβ point of view;
- Self-organization, ability to prioritize tasks
Benefits:- Interesting products and ability to shape them with your vision.
- Flexible schedule with remote mode.
- 21 working days of vacation.
- 25 sick leaves.
- Paid Ukrainian National Holidays.
- Medical insurance is provided after completing the probation period (3 months from the start date).
- Budget for education.
- Paid individual English classes.
- Technical equipment that meets the working needs.
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Β· 35 views Β· 1 application Β· 6d
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· 95 views Β· 9 applications Β· 2d
Strong Junior Data Scientist
Full Remote Β· Worldwide Β· Product Β· 1 year of experience Β· IntermediateIn Competera, we are building a place where optimal pricing decisions can be made easily. We believe that AI technologies will soon drive all challenging decisions and are capable of helping humans be better. We are now seeking a Junior Data Scientist to...In Competera, we are building a place where optimal pricing decisions can be made easily. We believe that AI technologies will soon drive all challenging decisions and are capable of helping humans be better.
We are now seeking a Junior Data Scientist to play a key role in reshaping the way we deliver our solutions.What you will do
- Conduct Exploratory Data Analysis (EDA) to uncover hidden patterns and formulate hypotheses that shape the modeling strategy.
- Design and analyze A/B tests to measure the impact of your models and ideas.
- Train and evaluate predictive models, (feature engineering/ hyperparameter tuning), for challenges like demand forecasting and price elasticity estimation.
- Map business requirements into well-defined machine learning problems under consultancy.
- Communicate complex model outputs as clear, actionable insights for business stakeholders.
You have:
- SQL basics.
- A strong math background (Computer Science-related education is preferred).
- Scientific python toolkit (NumPy, pandas, scikit-learn, Keras / TensorFlow or PyTorch).
- Deep understanding of ML basics: overfitting, metrics, cross-validation, hyperparameter tuning, classification of ML tasks and models (classification, regression, clustering etc.).
- Good communication English skills (Intermediate+).
- 1+ year of hands-on experience in a data science.
Pleasant extras:
- Proven graduation from ML/AI MOOCs (Coursera, etc.).
- Participation in ML competitions (i.e. Kaggle).
Soft skills:
- Analytical mindset and critical thinking to solve complex problems.
- Agile approach, with the ability to experiment and test hypotheses in a dynamic business environment.
- Business-oriented thinking, capable of translating complex models into clear business insights.
- Curiosity and a drive for continuous learning in the data domain.
- Strong team player, able to collaborate across cross-functional teams.
Youβre gonna love it, and hereβs why:
- Rich innovative software stack, freedom to choose the best suitable technologies.
- Remote-first ideology: freedom to operate from the home office or any suitable coworking.
- Flexible working hours (we start from 8 to 11 am) and no time tracking systems on.
- Regular performance and compensation reviews.
- Recurrent 1-1s and measurable OKRs.
- In-depth onboarding with a clear success track.
- Competera covers 70% of your training/course fee.
- 20 vacation days, 15 days off, and up to one week of paid Christmas holidays.
- 20 business days of sick leave.
- Partial medical insurance coverage.
- We reimburse the cost of coworking.
Drive innovations with us. Be a Competerian.
More -
Β· 97 views Β· 3 applications Β· 12d
Data Scientist
Full Remote Β· Ukraine Β· Product Β· 2 years of experience Β· Intermediate Ukrainian Product πΊπ¦ΠΠ±ΠΎΠ²βΡΠ·ΠΊΠΈ: ΠΠ½Π°Π»ΡΠ· Π΄Π°Π½ΠΈΡ : ΠΠ±ΡΡ, ΠΎΠ±ΡΠΎΠ±ΠΊΠ° ΡΠ° Π°Π½Π°Π»ΡΠ· Π²Π΅Π»ΠΈΠΊΠΈΡ Π½Π°Π±ΠΎΡΡΠ² Π΄Π°Π½ΠΈΡ Π΄Π»Ρ Π²ΠΈΡΠ²Π»Π΅Π½Π½Ρ ΠΏΠ°ΡΠ΅ΡΠ½ΡΠ², ΡΡΠ΅Π½Π΄ΡΠ² ΡΠ° ΠΊΠΎΡΠΈΡΠ½ΠΎΡ ΡΠ½ΡΠΎΡΠΌΠ°ΡΡΡ. Π‘ΡΠ²ΠΎΡΠ΅Π½Π½Ρ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ: Π ΠΎΠ·ΡΠΎΠ±ΠΊΠ°, Π½Π°Π»Π°Π³ΠΎΠ΄ΠΆΠ΅Π½Π½Ρ ΡΠ° Π²ΠΏΡΠΎΠ²Π°Π΄ΠΆΠ΅Π½Π½Ρ ΠΌΠ°ΡΠΈΠ½Π½ΠΈΡ Π½Π°Π²ΡΠ°Π»ΡΠ½ΠΈΡ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ Π΄Π»Ρ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΡΠ²Π°Π½Π½Ρ ΡΠ°...ΠΠ±ΠΎΠ²βΡΠ·ΠΊΠΈ:
- ΠΠ½Π°Π»ΡΠ· Π΄Π°Π½ΠΈΡ : ΠΠ±ΡΡ, ΠΎΠ±ΡΠΎΠ±ΠΊΠ° ΡΠ° Π°Π½Π°Π»ΡΠ· Π²Π΅Π»ΠΈΠΊΠΈΡ Π½Π°Π±ΠΎΡΡΠ² Π΄Π°Π½ΠΈΡ Π΄Π»Ρ Π²ΠΈΡΠ²Π»Π΅Π½Π½Ρ ΠΏΠ°ΡΠ΅ΡΠ½ΡΠ², ΡΡΠ΅Π½Π΄ΡΠ² ΡΠ° ΠΊΠΎΡΠΈΡΠ½ΠΎΡ ΡΠ½ΡΠΎΡΠΌΠ°ΡΡΡ.
- Π‘ΡΠ²ΠΎΡΠ΅Π½Π½Ρ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ: Π ΠΎΠ·ΡΠΎΠ±ΠΊΠ°, Π½Π°Π»Π°Π³ΠΎΠ΄ΠΆΠ΅Π½Π½Ρ ΡΠ° Π²ΠΏΡΠΎΠ²Π°Π΄ΠΆΠ΅Π½Π½Ρ ΠΌΠ°ΡΠΈΠ½Π½ΠΈΡ Π½Π°Π²ΡΠ°Π»ΡΠ½ΠΈΡ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ Π΄Π»Ρ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΡΠ²Π°Π½Π½Ρ ΡΠ° ΠΊΠ»Π°ΡΠΈΡΡΠΊΠ°ΡΡΡ.
- ΠΡΠ·ΡΠ°Π»ΡΠ·Π°ΡΡΡ Π΄Π°Π½ΠΈΡ : Π‘ΡΠ²ΠΎΡΠ΅Π½Π½Ρ Π³ΡΠ°ΡΡΠΊΡΠ² Π΄Π»Ρ Π²ΡΠ·ΡΠ°Π»ΡΠ·Π°ΡΡΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡΠ² Π°Π½Π°Π»ΡΠ·Ρ.
- Π‘ΠΏΡΠ²ΠΏΡΠ°ΡΡ Π· ΠΊΠΎΠΌΠ°Π½Π΄Π°ΠΌΠΈ: Π ΠΎΠ±ΠΎΡΠ° Π· ΠΊΠΎΠΌΠ°Π½Π΄Π°ΠΌΠΈ ΡΠΎΠ·ΡΠΎΠ±Π½ΠΈΠΊΡΠ² ΡΠ° ΠΏΡΠΎΠ΄Π°ΠΊΡΡΠ² Π΄Π»Ρ ΡΠ½ΡΠ΅Π³ΡΠ°ΡΡΡ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ Ρ ΠΏΡΠΎΠ΄ΡΠΊΡΠΈ.
- ΠΠΈΠΊΠΎΠ½Π°Π½Π½Ρ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Ρ: Π£ΡΠ°ΡΡΡ Ρ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½ΡΡ Π΄Π»Ρ Π²Π΄ΠΎΡΠΊΠΎΠ½Π°Π»Π΅Π½Π½Ρ ΡΡΠ½ΡΡΡΠΈΡ ΡΡΡΠ΅Π½Ρ Ρ Π²ΠΏΡΠΎΠ²Π°Π΄ΠΆΠ΅Π½Π½Ρ Π½ΠΎΠ²ΠΈΡ ΠΏΡΠ΄Ρ ΠΎΠ΄ΡΠ².
- ΠΠΎΠΊΡΠΌΠ΅Π½ΡΠ°ΡΡΡ: ΠΡΠΎΡΠΌΠ»Π΅Π½Π½Ρ Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ°ΡΡΡ Π΄Π»Ρ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΡΠ° ΠΏΡΠΎΡΠ΅ΡΡΠ² Π°Π½Π°Π»ΡΠ·Ρ Π΄Π°Π½ΠΈΡ
.
ΠΠΈΠΌΠΎΠ³ΠΈ:
- ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π² Data Science - Π²ΡΠ΄ 2 ΡΠΎΠΊΡΠ²;
- ΠΡΠ²ΡΡΠ°: ΠΠΈΡΠ° ΠΎΡΠ²ΡΡΠ° Ρ Π³Π°Π»ΡΠ·Ρ ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠΈ, ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΠΊΠΈ, ΠΊΠΎΠΌΠΏβΡΡΠ΅ΡΠ½ΠΈΡ Π½Π°ΡΠΊ Π°Π±ΠΎ Π² ΡΡΠΌΡΠΆΠ½ΠΈΡ ΠΎΠ±Π»Π°ΡΡΡΡ .
- ΠΠ½Π°Π½Π½Ρ ΡΠ½ΡΡΡΡΠΌΠ΅Π½ΡΡΠ²: ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ ΡΠ° ΡΡΠ΄ΠΎΠ²Π΅ Π·Π½Π°Π½Π½Ρ Python ΡΠ° Π±ΡΠ±Π»ΡΠΎΡΠ΅ΠΊ Π΄Π»Ρ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ Π½Π°Π²ΡΠ°Π½Π½Ρ Π· ΠΏΡΡΠΎΡΠΈΡΠ΅ΡΠΎΠΌ Π½Π° ΡΠ°Π±Π»ΠΈΡΠ½Ρ Π΄Π°Π½Ρ (Pandas, Numpy, Scikit-learn, Xgboost/LightGBM).
- ΠΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ½Ρ Π½Π°Π²ΠΈΡΠΊΠΈ: Π ΠΎΠ·ΡΠΌΡΠ½Π½Ρ ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠΈ, ΡΠ΅ΠΎΡΡΡ ΠΉΠΌΠΎΠ²ΡΡΠ½ΠΎΡΡΠ΅ΠΉ ΡΠ° Π°Π»Π³ΠΎΡΠΈΡΠΌΡΠ² ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ Π½Π°Π²ΡΠ°Π½Π½Ρ.
- ΠΠ½Π°Π»ΡΡΠΈΡΠ½Ρ Π·Π΄ΡΠ±Π½ΠΎΡΡΡ: ΠΠΌΡΠ½Π½Ρ Π°Π½Π°Π»ΡΠ·ΡΠ²Π°ΡΠΈ Π΄Π°Π½Ρ ΡΠ° ΡΠΎΠ±ΠΈΡΠΈ Π²ΠΈΡΠ½ΠΎΠ²ΠΊΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ ΠΎΡΡΠΈΠΌΠ°Π½ΠΈΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡΠ².
- ΠΠΎΠΌΡΠ½ΡΠΊΠ°ΡΡΠΉΠ½Ρ Π½Π°Π²ΠΈΡΠΊΠΈ: ΠΠΌΡΠ½Π½Ρ ΠΏΠΎΡΡΠ½ΡΠ²Π°ΡΠΈ ΡΠ΅Ρ Π½ΡΡΠ½Ρ ΠΊΠΎΠ½ΡΠ΅ΠΏΡΡΡ Π½Π΅ΡΠ°Ρ ΡΠ²ΡΡΠΌ Ρ ΠΏΡΠ°ΡΡΠ²Π°ΡΠΈ Π² ΠΊΠΎΠΌΠ°Π½Π΄Ρ.
- ΠΠΎΡΠ²ΡΠ΄ Π· SQL: ΠΠΌΡΠ½Π½Ρ ΠΏΡΠ°ΡΡΠ²Π°ΡΠΈ Π· Π±Π°Π·Π°ΠΌΠΈ Π΄Π°Π½ΠΈΡ ΡΠ° Π²ΠΈΠΊΠΎΠ½ΡΠ²Π°ΡΠΈ Π·Π°ΠΏΠΈΡΠΈ Π΄Π»Ρ ΠΎΡΡΠΈΠΌΠ°Π½Π½Ρ ΡΠ½ΡΠΎΡΠΌΠ°ΡΡΡ.
- ΠΠΎΡΠ²ΡΠ΄ Π· ΡΠ΅Π°Π»ΡΠ½ΠΈΠΌΠΈ NLP ΡΠ° Recommender System ΠΏΡΠΎΠ΅ΠΊΡΠ°ΠΌΠΈ Π±ΡΠ΄Π΅ ΠΏΠ»ΡΡΠΎΠΌ
- ΠΠ΅ΠΊΠ³ΡΠ°ΡΠ½Π΄ Π² Π°Π½Π°Π»ΡΡΠΈΡΠ½ΡΠΉ ΡΡΠ΅ΡΡ Π±ΡΠ΄Π΅ ΠΏΠ»ΡΡΠΎΠΌ
ΠΠΈ ΠΏΡΠΎΠΏΠΎΠ½ΡΡΠΌΠΎ:
- ΠΠΎΠ½ΠΊΡΡΠ΅Π½ΡΠ½Ρ Π·Π°ΡΠΎΠ±ΡΡΠ½Ρ ΠΏΠ»Π°ΡΡ Π· ΠΏΡΠΈΠ²βΡΠ·ΠΊΠΎΡ Π΄ΠΎ Π²Π°Π»ΡΡΠΈ;
- ΠΠ½ΡΡΠΊΠΈΠΉ Π³ΡΠ°ΡΡΠΊ ΡΠΎΠ±ΠΎΡΠΈ/Π²ΡΠ΄Π΄Π°Π»Π΅Π½Π° ΡΠΎΠ±ΠΎΡΠ°;
- 20 ΡΠΎΠ±ΠΎΡΠΈΡ Π΄Π½ΡΠ² ΠΎΠΏΠ»Π°ΡΡΠ²Π°Π½ΠΎΡ Π²ΡΠ΄ΠΏΡΡΡΠΊΠΈ;
- ΠΠΎΠ²Π½Ρ ΠΊΠΎΠΌΠΏΠ΅Π½ΡΠ°ΡΡΡ Π»ΡΠΊΠ°ΡΠ½ΡΠ½ΠΈΡ Π΄Π½ΡΠ²;
- ΠΠ΅Π΄ΠΈΡΠ½Π΅ ΡΡΡΠ°Ρ ΡΠ²Π°Π½Π½Ρ;
- L&D ΡΠ΅Π½ΡΡ ΡΠ· ΠΊΡΡΡΠ°ΠΌΠΈ Π΄Π»Ρ ΡΠΏΡΠ²ΡΠΎΠ±ΡΡΠ½ΠΈΠΊΡΠ²;
- ΠΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ ΠΊΠ°Ρ'ΡΡΠ½ΠΎΠ³ΠΎ ΡΠΎΠ·Π²ΠΈΡΠΊΡ ΡΠ° ΡΠΎΡΠ°ΡΡΡ Π½Π°ΠΏΡΡΠΌΠΊΡΠ²;
- Π Π΅ΡΠ΅ΡΠ°Π»ΡΠ½Ρ ΠΏΡΠΎΠ³ΡΠ°ΠΌΡ;
- ΠΡΡΠΏΠΎΠ²Ρ Π΄ΠΈΡΠΊΡΡΡΡ ΡΠ° ΡΠ½Π΄ΠΈΠ²ΡΠ΄ΡΠ°Π»ΡΠ½Ρ Π·Π°Π½ΡΡΡΡ Π· ΠΊΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½ΠΈΠΌ ΠΏΡΠΈΡ ΠΎΠ»ΠΎΠ³ΠΎΠΌ.
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Β· 26 views Β· 3 applications Β· 6d
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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Senior Data Scientist (Operational and Application Software)
Full Remote Β· Ukraine Β· Product Β· 4 years of experience Β· Intermediate Ukrainian Product πΊπ¦ΠΡΠΈΠ²ΡΡ! ΠΠΈ β TemaBit Fozzy Group β ΠΊΠΎΠΌΠ°Π½Π΄Π° ΡΠ°Π½Π°ΡΡΠ² ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΠΉ, ΡΠΊΡ Π·ΠΌΡΠ½ΡΡΡΡ Π£ΠΊΡΠ°ΡΠ½Ρ. ΠΠΈ Ρ IT ΡΠ°ΡΡΠΈΠ½ΠΎΡ Fozzy Group β ΠΎΠ΄Π½ΡΡΡ Π· Π½Π°ΠΉΠ±ΡΠ»ΡΡΠΈΡ ΡΠΎΡΠ³ΠΎΠ²ΠΎ-ΠΏΡΠΎΠΌΠΈΡΠ»ΠΎΠ²ΠΈΡ Π³ΡΡΠΏ Π£ΠΊΡΠ°ΡΠ½ΠΈ. Π ΠΎΠ·ΡΠΎΠ±ΠΊΠ°ΠΌΠΈ Π½Π°ΡΠΎΡ ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ ΡΠΎΠ΄Π½Ρ ΠΊΠΎΡΠΈΡΡΡΡΡΡΡΡ 60 000 ΡΠΏΡΠ²ΡΠΎΠ±ΡΡΠ½ΠΈΠΊΡΠ², ΡΠΈΡΡΡΡ ΠΏΠ°ΡΡΠ½Π΅ΡΡΠ²...ΠΡΠΈΠ²ΡΡ!
ΠΠΈ β TemaBit Fozzy Group β ΠΊΠΎΠΌΠ°Π½Π΄Π° ΡΠ°Π½Π°ΡΡΠ² ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΠΉ, ΡΠΊΡ Π·ΠΌΡΠ½ΡΡΡΡ Π£ΠΊΡΠ°ΡΠ½Ρ. ΠΠΈ Ρ IT ΡΠ°ΡΡΠΈΠ½ΠΎΡ Fozzy Group β ΠΎΠ΄Π½ΡΡΡ Π· Π½Π°ΠΉΠ±ΡΠ»ΡΡΠΈΡ ΡΠΎΡΠ³ΠΎΠ²ΠΎ-ΠΏΡΠΎΠΌΠΈΡΠ»ΠΎΠ²ΠΈΡ Π³ΡΡΠΏ Π£ΠΊΡΠ°ΡΠ½ΠΈ. Π ΠΎΠ·ΡΠΎΠ±ΠΊΠ°ΠΌΠΈ Π½Π°ΡΠΎΡ ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ ΡΠΎΠ΄Π½Ρ ΠΊΠΎΡΠΈΡΡΡΡΡΡΡΡ 60 000 ΡΠΏΡΠ²ΡΠΎΠ±ΡΡΠ½ΠΈΠΊΡΠ², ΡΠΈΡΡΡΡ ΠΏΠ°ΡΡΠ½Π΅ΡΡΠ² ΡΠ° ΠΌΡΠ»ΡΠΉΠΎΠ½ΠΈ Π³ΠΎΡΡΠ΅ΠΉ Β«Π‘ΡΠ»ΡΠΏΠΎΒ», Β«Π€ΠΎΡΠ°Β», Fozzy, Thrash ΡΠ° ΡΠ½ΡΠΈΡ online Ρ offline Π±ΡΠ·Π½Π΅ΡΡΠ²!
ΠΠ°ΠΏΡΠΎΡΡΡΠΌΠΎ Π΄ΠΎ ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ Senior Data Scientist (Operational and Application Software).
ΠΠ»ΡΡΠΎΠ²ΠΈΠΉ Π²ΠΈΠΊΠ»ΠΈΠΊ β ΠΏΠ΅ΡΠ΅Π±ΡΠ΄ΡΠ²Π°ΡΠΈ ΡΠ° Π²Π΄ΠΎΡΠΊΠΎΠ½Π°Π»ΠΈΡΠΈ ΠΏΡΠ΄Ρ ΠΎΠ΄ΠΈ Π΄ΠΎ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΡΠ²Π°Π½Π½Ρ ΠΏΠΎΡΡΠ΅Π±ΠΈ Π² ΠΏΠ΅ΡΡΠΎΠ½Π°Π»Ρ Π² ΠΌΠ°Π³Π°Π·ΠΈΠ½Π°Ρ Β«Π‘ΡΠ»ΡΠΏΠΎΒ». ΠΠ΅ΡΠ° β Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ Π³Π»ΠΈΠ±ΠΎΠΊΠΎΠ³ΠΎ Π°Π½Π°Π»ΡΠ·Ρ Π΄Π°Π½ΠΈΡ Π·Π°ΠΏΡΠΎΠΏΠΎΠ½ΡΠ²Π°ΡΠΈ ΡΠ° ΡΠ΅Π°Π»ΡΠ·ΡΠ²Π°ΡΠΈ ΡΡΡΠ΅Π½Π½Ρ, ΡΠΊΡ Π΄ΠΎΠΏΠΎΠΌΠΎΠΆΡΡΡ Π·Π°Π±Π΅Π·ΠΏΠ΅ΡΠΈΡΠΈ ΡΡΠ²Π΅Π½Ρ ΡΠ΅ΡΠ²ΡΡΡ ΡΠ° Π½Π°ΡΠ²Π½ΡΡΡΡ Π°ΡΠΎΡΡΠΈΠΌΠ΅Π½ΡΡ, ΡΠΎ ΠΏΠ΅ΡΠ΅Π²ΠΈΡΡΡ ΠΎΡΡΠΊΡΠ²Π°Π½Π½Ρ ΠΠΎΡΡΠ΅ΠΉ.
ΠΠΎΠ½ΠΈ Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π°Π»ΡΠ½ΠΎΡΡΡ:
β ΡΠΎΠ·ΡΠΎΠ±ΠΊΠ° ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΡΠ²Π°Π½Π½Ρ: ΡΡΠ°ΡΡΡ Ρ ΡΠΎΠ·ΡΠΎΠ±ΡΡ ΡΠ° Π²Π΄ΠΎΡΠΊΠΎΠ½Π°Π»Π΅Π½Π½Ρ Π»ΠΎΠ³ΡΠΊΠΈ ΠΉ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ»ΠΎΠ³ΡΡ ΡΠΎΠ·ΡΠ°Ρ ΡΠ½ΠΊΡ ΠΏΠΎΡΡΠ΅Π±ΠΈ Π² ΠΏΠ΅ΡΡΠΎΠ½Π°Π»Ρ Π΄Π»Ρ Π²ΡΡΡ Π΅ΡΠ°ΠΏΡΠ² supply chain (ΡΠΊΠ»Π°Π΄ΠΈ, Π»ΠΎΠ³ΡΡΡΠΈΠΊΠ°, ΠΌΠ°Π³Π°Π·ΠΈΠ½ΠΈ);
β ΠΎΠΏΡΠΈΠΌΡΠ·Π°ΡΡΡ ΡΠ΅ΡΡΡΡΡΠ²: Π°Π½Π°Π»ΡΠ· Π΄Π°Π½ΠΈΡ Π΄Π»Ρ ΠΏΠΎΡΡΠΊΡ ΠΌΠΎΠΆΠ»ΠΈΠ²ΠΎΡΡΠ΅ΠΉ Π· ΠΎΠΏΡΠΈΠΌΡΠ·Π°ΡΡΡ ΡΠΎΠ·ΠΏΠΎΠ΄ΡΠ»Ρ Π»ΡΠ΄ΡΡΠΊΠΈΡ ΡΠ΅ΡΡΡΡΡΠ² Π·Π°Π»Π΅ΠΆΠ½ΠΎ Π²ΡΠ΄ ΡΠ΅Π·ΠΎΠ½Π½ΠΎΡΡΡ, ΠΏΠΎΠΏΠΈΡΡ ΡΠ° ΠΎΠΏΠ΅ΡΠ°ΡΡΠΉΠ½ΠΎΠ³ΠΎ Π½Π°Π²Π°Π½ΡΠ°ΠΆΠ΅Π½Π½Ρ, Π° ΡΠ°ΠΊΠΎΠΆ ΡΠΎΡΠΌΡΠ²Π°Π½Π½Ρ ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΡΠΉ;
β ΡΡΠ²ΠΎΡΠ΅Π½Π½Ρ ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΡΠΉΠ½ΠΈΡ ΡΠΈΡΡΠ΅ΠΌ: ΡΠΎΠ·ΡΠΎΠ±ΠΊΠ° Π±ΡΠ·Π½Π΅Ρ-Π»ΠΎΠ³ΡΠΊΠΈ ΡΠ° ΠΏΡΠ°Π²ΠΈΠ» Π΄Π»Ρ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΎΠ²Π°Π½ΠΎΠ³ΠΎ ΠΏΠ»Π°Π½ΡΠ²Π°Π½Π½Ρ Π·ΠΌΡΠ½, ΡΠΎΡΠ°ΡΡΡ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»Ρ ΡΠ° Π½Π°ΠΉΠΌΡ;
β Π°Π½Π°Π»ΡΠ· Π΅ΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ: Π°Π½Π°Π»ΡΠ· ΠΏΡΠΎΠ΄ΡΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»Ρ Π² Π»Π°Π½ΡΡΠΆΠΊΡ ΠΏΠΎΡΡΠ°ΡΠ°Π½Π½Ρ, ΡΠΎΠ·ΡΠΎΠ±ΠΊΠ° ΡΠ° Π²Π°Π»ΡΠ΄Π°ΡΡΡ ΠΊΠ»ΡΡΠΎΠ²ΠΈΡ KPI ΡΠ° ΠΌΠ΅ΡΡΠΈΠΊ ΠΎΡΡΠ½ΠΊΠΈ;
β ΠΏΠΎΠ±ΡΠ΄ΠΎΠ²Π° Π΄Π°ΡΠ±ΠΎΡΠ΄ΡΠ²: ΡΡΠ²ΠΎΡΠ΅Π½Π½Ρ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΈΡ Π΄Π°ΡΠ±ΠΎΡΠ΄ΡΠ² ΡΠ° Π·Π²ΡΡΠ½ΠΎΡΡΡ Π΄Π»Ρ HR Ρ ΠΎΠΏΠ΅ΡΠ°ΡΡΠΉΠ½ΠΈΡ ΠΌΠ΅Π½Π΅Π΄ΠΆΠ΅ΡΡΠ², Π²ΡΠ·ΡΠ°Π»ΡΠ·Π°ΡΡΡ ΠΊΠ»ΡΡΠΎΠ²ΠΈΡ HR-ΠΌΠ΅ΡΡΠΈΠΊ Π΄Π»Ρ ΠΌΠΎΠ½ΡΡΠΎΡΠΈΠ½Π³Ρ ΡΠ° ΠΏΡΠΈΠΉΠ½ΡΡΡΡ ΡΡΡΠ΅Π½Ρ;
β ΠΊΡΠΎΡ-ΡΡΠ½ΠΊΡΡΠΎΠ½Π°Π»ΡΠ½Π° Π²Π·Π°ΡΠΌΠΎΠ΄ΡΡ: ΡΡΡΠ½Π° ΡΠΏΡΠ²ΠΏΡΠ°ΡΡ Π· HR, Π»ΠΎΠ³ΡΡΡΠΈΡΠ½ΠΈΠΌΠΈ ΡΠ° ΠΎΠΏΠ΅ΡΠ°ΡΡΠΉΠ½ΠΈΠΌΠΈ ΠΊΠΎΠΌΠ°Π½Π΄Π°ΠΌΠΈ Π΄Π»Ρ Π²ΠΏΡΠΎΠ²Π°Π΄ΠΆΠ΅Π½Π½Ρ data-driven ΠΏΡΠ΄Ρ ΠΎΠ΄ΡΠ² ΡΠ° ΠΏΠΎΡΡΠ½Π΅Π½Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡΠ² Π°Π½Π°Π»ΡΡΠΈΠΊΠΈ.
ΠΠ°Ρ ΡΠ΄Π΅Π°Π»ΡΠ½ΠΈΠΉ ΠΊΠ°Π½Π΄ΠΈΠ΄Π°Ρ:
β ΠΌΠ°Ρ ΡΡΠ½Π΄Π°ΠΌΠ΅Π½ΡΠ°Π»ΡΠ½Ρ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ½Ρ, ΡΠ½ΠΆΠ΅Π½Π΅ΡΠ½Ρ ΠΎΡΠ²ΡΡΡ Π°Π±ΠΎ ΠΎΡΠ²ΡΡΡ Π² Π³Π°Π»ΡΠ·Ρ ΡΡΠ·ΠΈΠΊΠΈ;
β ΠΌΠ°Ρ Π²ΡΠ΄ 4 ΡΠΎΠΊΡΠ² Π΄ΠΎΡΠ²ΡΠ΄Ρ Π½Π° ΠΏΠΎΠ·ΠΈΡΡΡ Data Science;
β Π²ΠΏΠ΅Π²Π½Π΅Π½ΠΎ Π²ΠΈΠΊΠΎΡΠΈΡΡΠΎΠ²ΡΡ Python (Π·ΠΎΠΊΡΠ΅ΠΌΠ°, pandas) Π΄Π»Ρ Π°Π½Π°Π»ΡΠ·Ρ ΡΠ° ΠΎΠ±ΡΠΎΠ±ΠΊΠΈ Π΄Π°Π½ΠΈΡ ;
β Π²ΡΠ΄ΠΌΡΠ½Π½ΠΎ Π²ΠΎΠ»ΠΎΠ΄ΡΡ SQL Π΄Π»Ρ ΡΠΎΠ±ΠΎΡΠΈ Π·Ρ ΡΠΊΠ»Π°Π΄Π½ΠΈΠΌΠΈ Π·Π°ΠΏΠΈΡΠ°ΠΌΠΈ ΡΠ° Π²Π΅Π»ΠΈΠΊΠΈΠΌΠΈ ΠΌΠ°ΡΠΈΠ²Π°ΠΌΠΈ Π΄Π°Π½ΠΈΡ ;
β Π΄ΠΎΡΠ»ΡΠ΄ΠΆΡΡ Π΄Π°Π½Ρ, Π·Π½Π°Ρ ΠΎΠ΄ΠΈΡΡ Π½Π΅ΠΎΡΠ΅Π²ΠΈΠ΄Π½Ρ Π·Π²βΡΠ·ΠΊΠΈ ΡΠ° ΠΏΡΠΎΠΏΠΎΠ½ΡΡ Π²Π»Π°ΡΠ½Ρ ΡΠΏΠΎΡΠΎΠ±ΠΈ ΡΠΎΠ·ΡΠ°Ρ ΡΠ½ΠΊΡΠ²
ΠΡΠ΄Π΅ ΠΏΠ΅ΡΠ΅Π²Π°Π³ΠΎΡ:
β Π΄ΠΎΡΠ²ΡΠ΄ Ρ ΡΡΠ²ΠΎΡΠ΅Π½Π½Ρ ΡΠΊΠ»Π°Π΄Π½ΠΈΡ ΡΠ½ΡΠ΅ΡΠ°ΠΊΡΠΈΠ²Π½ΠΈΡ Π΄Π°ΡΠ±ΠΎΡΠ΄ΡΠ² Π² Power BI Π°Π±ΠΎ ΡΠ½ΡΡΠΉ BI ΡΠΈΡΡΠ΅ΠΌΡ;
β Π΄ΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Ρ ΡΡΠ΅ΡΡ ΡΠΈΡΠ΅ΠΉΠ»Ρ, Π»ΠΎΠ³ΡΡΡΠΈΠΊΠΈ Π°Π±ΠΎ workforce management;
β ΡΠΎΠ·ΡΠΌΡΠ½Π½Ρ Π±Π°Π·ΠΎΠ²ΠΈΡ ΠΏΡΠΈΠ½ΡΠΈΠΏΡΠ² ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ Π½Π°Π²ΡΠ°Π½Π½Ρ (ΠΏΡΠΎΠ³Π½ΠΎΠ·ΡΠ²Π°Π½Π½Ρ, ΠΊΠ»Π°ΡΠΈΡΡΠΊΠ°ΡΡΡ) ΡΠ° Π΄ΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· Π±ΡΠ±Π»ΡΠΎΡΠ΅ΠΊΠΎΡ scikit-learn;
β Π΄ΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· Apache Spark.
ΠΠΈ ΠΏΡΠΎΠΏΠΎΠ½ΡΡΠΌΠΎ:
β ΠΌΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ Π²ΡΠ΄Π΄Π°Π»Π΅Π½ΠΎΡ ΡΠΎΠ±ΠΎΡΠΈ;
β ΠΌΠ΅Π΄ΠΈΡΠ½Π΅ Ρ ΡΠΈΠ·ΠΈΠΊΠΎΠ²Π΅ ΡΡΡΠ°Ρ ΡΠ²Π°Π½Π½Ρ ΠΆΠΈΡΡΡ;
β ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΠ° Π€ΠΠ/ΠΠΠ- ΠΊΠΎΠ½ΡΡΠ°ΠΊΡΡ;
β Π·Π½ΠΈΠΆΠΊΠΈ Ρ ΠΌΠ°Π³Π°Π·ΠΈΠ½Π°Ρ ΡΠ° ΡΠ΅ΡΡΠΎΡΠ°Π½Π°Ρ Fozzy Group;
β ΠΏΡΠΈΡ ΠΎΠ»ΠΎΠ³ΡΡΠ½Π° ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΠ°;
β ΡΡΡΠ±ΠΎΡΠ»ΠΈΠ²Ρ ΠΊΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½Ρ ΠΊΡΠ»ΡΡΡΡΡ, ΡΠΊΡ ΠΌΠΈ ΡΠ°Π·ΠΎΠΌ Π· ΡΠΎΠ±ΠΎΡ Π±ΡΠ΄Π΅ΠΌΠΎ ΡΠΎΠ·Π²ΠΈΠ²Π°ΡΠΈ;
β ΠΊΠΎΠΌΠ°Π½Π΄Ρ, Π· ΡΠΊΠΎΡ ΡΠΈ Π·ΠΌΠΎΠΆΠ΅Ρ ΡΠ΅Π°Π»ΡΠ·ΡΠ²Π°ΡΠΈ ΡΠ²ΠΎΡ ΡΠ΄Π΅Ρ, Π΅ΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΡΠ²Π°ΡΠΈ Ρ Π²ΡΠ΄ΡΡΠ²Π°ΡΠΈ ΡΠ΅Π±Π΅ Π² ΠΊΠΎΠ»Ρ Π΄ΡΡΠ·ΡΠ²;
β Ρ ΡΡΠ»Π° ΠΊΡΠΏΠ° ΡΡΡΠ±ΠΎΡΠΈΠ½ΠΎΠΊ (ΡΡΡΠ°Ρ ΡΠ²Π°Π½Π½Ρ (Π² Π£ΠΊΡΠ°ΡΠ½Ρ), Π·Π½ΠΈΠΆΠΊΠΈ Π² Π½Π°ΡΠΈΡ Π±ΡΠ·Π½Π΅ΡΠ°Ρ ΡΠ° Ρ ΠΏΠ°ΡΡΠ½Π΅ΡΡΠ²).
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Data Scientist (Research and Development)
Full Remote Β· Countries of Europe or Ukraine Β· Product Β· 2 years of experience Β· Intermediate Ukrainian Product πΊπ¦TemaBit Fozzy Group β ΡΠ΅ Π±ΡΠ»ΡΡ ΡΠΊ ΡΠΈΡΡΡΠ° IT-ΠΏΡΠΎΡΠ΅ΡΡΠΎΠ½Π°Π»ΡΠ², ΠΏΠΎΠ½Π°Π΄ 20 ΡΠΎΠΊΡΠ² Π΄ΠΎΡΠ²ΡΠ΄Ρ ΡΠ° ΡΠΎΡΠ½Ρ ΡΠ΅Π°Π»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΊΠ΅ΠΉΡΡΠ². TemaBit ΡΡΠ²ΠΎΡΡΡ ΠΎΡΠΈΠ³ΡΠ½Π°Π»ΡΠ½Ρ end-to-end ΡΡΡΠ΅Π½Π½Ρ: ΡΠΏΠ΅ΡΡΠ°Π»ΡΠ·ΡΡΡΡΡΡ Π½Π° Π²Π΅Π±- ΡΠ° ΠΌΠΎΠ±ΡΠ»ΡΠ½ΡΠΉ ΡΠΎΠ·ΡΠΎΠ±ΡΡ, Π° ΡΠ°ΠΊΠΎΠΆ ΡΠΎΡΡΡ Π΄Π»Ρ ΠΌΠ΅Π½Π΅Π΄ΠΆΠΌΠ΅Π½ΡΡ Π±ΡΠ·Π½Π΅Ρ-ΠΏΡΠΎΡΠ΅ΡΡΠ² ΡΠ°...TemaBit Fozzy Group β ΡΠ΅ Π±ΡΠ»ΡΡ ΡΠΊ ΡΠΈΡΡΡΠ° IT-ΠΏΡΠΎΡΠ΅ΡΡΠΎΠ½Π°Π»ΡΠ², ΠΏΠΎΠ½Π°Π΄ 20 ΡΠΎΠΊΡΠ² Π΄ΠΎΡΠ²ΡΠ΄Ρ ΡΠ° ΡΠΎΡΠ½Ρ ΡΠ΅Π°Π»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΊΠ΅ΠΉΡΡΠ².
TemaBit ΡΡΠ²ΠΎΡΡΡ ΠΎΡΠΈΠ³ΡΠ½Π°Π»ΡΠ½Ρ end-to-end ΡΡΡΠ΅Π½Π½Ρ: ΡΠΏΠ΅ΡΡΠ°Π»ΡΠ·ΡΡΡΡΡΡ Π½Π° Π²Π΅Π±- ΡΠ° ΠΌΠΎΠ±ΡΠ»ΡΠ½ΡΠΉ ΡΠΎΠ·ΡΠΎΠ±ΡΡ, Π° ΡΠ°ΠΊΠΎΠΆ ΡΠΎΡΡΡ Π΄Π»Ρ ΠΌΠ΅Π½Π΅Π΄ΠΆΠΌΠ΅Π½ΡΡ Π±ΡΠ·Π½Π΅Ρ-ΠΏΡΠΎΡΠ΅ΡΡΠ² ΡΠ° ΡΠ½ΡΡΠ°ΡΡΡΡΠΊΡΡΡΠ½ΠΈΡ ΠΏΡΠΎΡΠΊΡΠ°Ρ .
ΠΠ»Π°ΡΠ½ΠΈΠΉ R&D ΡΠ΅Π½ΡΡ Β«ΠΠ°Π±ΠΎΡΠ°ΡΠΎΡΡΡ 3ΠΒ» Π΅ΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΡΡ Π· Π½ΠΎΠ²ΠΈΠΌΠΈ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΡΠΌΠΈ. Π’ΡΡ Π½Π°ΡΠΎΠ΄ΠΆΡΡΡΡΡΡ ΡΠ΄Π΅Ρ ΡΠ· Π·Π°ΡΡΠΎΡΡΠ²Π°Π½Π½ΡΠΌ ΡΡΡΡΠ½ΠΎΠ³ΠΎ ΡΠ½ΡΠ΅Π»Π΅ΠΊΡΡ, ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ Π½Π°Π²ΡΠ°Π½Π½Ρ, ΠΊΠΎΠΌΠΏβΡΡΠ΅ΡΠ½ΠΎΠ³ΠΎ Π·ΠΎΡΡ Ρ ΡΠΎΠ±ΠΎΡΡΠ².
ΠΠΈ ΠΏΡΠ°ΡΡΡΠΌΠΎ Π½Π°Π΄ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΡΠΌΠΈ, ΡΠΊΡ Π½Π°Π±Π»ΠΈΠΆΠ°ΡΡΡ Π½Π°ΡΡ ΠΠ΅ΡΠ΅ΠΌΠΎΠ³Ρ ΡΠ° Π²ΡΡΠ»ΡΠΊΠΎ ΠΏΡΠ΄ΡΡΠΈΠΌΡΡΠΌΠΎ ΠΠΎΡΡΡ.
ΠΠΈ ΠΏΡΠ°ΡΡΡΠΌΠΎ Ρ Π³ΡΠ±ΡΠΈΠ΄Π½ΠΎΠΌΡ ΡΠΎΡΠΌΠ°ΡΡ, Π° Π½Π°ΡΠΈΠΌ ΠΎΡΡΡΠ°ΠΌ Π½Π΅ ΡΡΡΠ°ΡΠ½Ρ Π±Π»Π΅ΠΊΠ°ΡΡΠΈ: Π³Π΅Π½Π΅ΡΠ°ΡΠΎΡΠΈ ΡΠ° ΡΡΠ°ΡΠ»ΡΠ½ΠΊΠΈ Ρ ΠΏΠΎΠΌΡΡ ΡΠ΅Ρ Π½ΠΎΠ³Π΅ΡΠΎΡΠΌ. Π ΡΠ΅ β Ρ Π½Π°Ρ Π½Π΅ΠΌΠ°Ρ ΠΎΠ±ΠΎΠ²βΡΠ·ΠΊΠΎΠ²ΠΈΡ Π³ΠΎΠ΄ΠΈΠ½ ΡΠΎΠ±ΠΎΡΠΈ, ΡΠΈ ΡΠ°ΠΌ Π²ΠΈΠ·Π½Π°ΡΠ°ΡΡ ΡΠ²ΡΠΉ Π³ΡΠ°ΡΡΠΊ. ΠΠ»Ρ Π½Π°Ρ Π³ΠΎΠ»ΠΎΠ²Π½Π΅ β ΡΠ΅ ΠΏΠΎΠ·ΠΈΡΠΈΠ²Π½Π° Π΅ΡΠ΅ΠΊΡΠΈΠ²Π½Π° ΡΠΏΡΠ²ΠΏΡΠ°ΡΡ ΡΠ° ΠΏΡΠΎΠ΄ΡΠΊΡ, ΡΠΎ ΠΏΡΠ°ΡΡΡ, Π° ΠΏΡΠΎ ΡΠ΅ΡΡΡ Π΄ΠΎΠΌΠΎΠ²ΠΈΠΌΠΎΡΡ!
ΠΠ°Ρ dream ΠΊΠ°Π½Π΄ΠΈΠ΄Π°Ρ ΡΠΈ ΠΊΠ°Π½Π΄ΠΈΠ΄Π°ΡΠΊΠ°:
- ΠΌΠ°Ρ Π΄ΠΎΡΠ²ΡΠ΄ Π½Π΅ ΠΌΠ΅Π½ΡΠ΅, Π½ΡΠΆ 2 ΡΠΎΠΊΠΈ Π½Π° Π°Π½Π°Π»ΠΎΠ³ΡΡΠ½ΡΠΉ ΠΏΠΎΡΠ°Π΄Ρ Π² ΡΡΠ΅ΡΡ Data Science;
- Π²ΠΏΠ΅Π²Π½Π΅Π½ΠΎ Π²ΠΎΠ»ΠΎΠ΄ΡΡ Python 3.x ΡΠ° ΠΌΠ°Ρ Π·Π½Π°Π½Π½Ρ ΠΏΡΠΈΠ½ΡΠΈΠΏΡΠ² Clean Code, ΡΡΠ°Π½Π΄Π°ΡΡΡΠ² PEP;
- Π΄ΠΎΠ±ΡΠ΅ Π²ΠΎΠ»ΠΎΠ΄ΡΡ SQL;
- Π·Π½Π°Ρ ΡΠΈΡΡΠ΅ΠΌΠΈ ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ Π²Π΅ΡΡΡΠΉ (Git);
- ΡΠΎΠ·ΡΠΌΡΡ MLOps ΠΏΡΠ°ΠΊΡΠΈΠΊΠΈ (ΠΏΠ°ΠΉΠΏΠ»Π°ΠΉΠ½ΠΈ, Π²Π°Π»ΡΠ΄Π°ΡΡΡ, Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·Π°ΡΡΡ ΡΡΠ΅Π½ΡΠ²Π°Π½Ρ, Π²Π΅ΡΡΡΠΎΠ½ΡΠ²Π°Π½Π½Ρ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ/Π΄Π°Π½ΠΈΡ , Π²ΡΠ·ΡΠ°Π»ΡΠ·Π°ΡΡΡ ΠΌΠ΅ΡΡΠΈΠΊ);
- ΠΌΠ°Ρ Π΄ΠΎΡΠ²ΡΠ΄ Π· ΡΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠ°ΠΌΠΈ: pandas, polars, poetry.
ΠΡΠ΄Π΅ ΠΏΠ»ΡΡΠΎΠΌ:
- ΠΏΡΠ°ΠΊΡΠΈΠΊΠ° Π· Ρ ΠΌΠ°ΡΠ½ΠΈΠΌΠΈ ΡΠ΅ΡΠ²ΡΡΠ°ΠΌΠΈ (ΠΎΡΠΎΠ±Π»ΠΈΠ²ΠΎ AWS);
- Π΄ΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· deep learning ΡΡΠ΅ΠΉΠΌΠ²ΠΎΡΠΊΠ°ΠΌΠΈ: TensorFlow, Keras, PyTorch;
- Π·Π½Π°Π½Π½Ρ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΠΉ Big Data: PySpark, Vaex, Dask, Apache Beam;
- Π΄ΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· ΡΠ°ΡΠΎΠ²ΠΈΠΌΠΈ ΡΡΠ΄Π°ΠΌΠΈ (ARIMA, RNN, STL, Π±Π΅ΠΊΡΠ΅ΡΡΠΈΠ½Π³, ΠΊΡΠΎΡ-Π²Π°Π»ΡΠ΄Π°ΡΡΡ ΠΏΠΎ ΡΠ°ΡΡ);
- Π΄ΠΎΡΠ²ΡΠ΄ ΡΠΌΠΏΠ»Π΅ΠΌΠ΅Π½ΡΠ°ΡΡΡ Π½Π°ΡΠΊΠΎΠ²ΠΈΡ ΠΏΡΠ±Π»ΡΠΊΠ°ΡΡΠΉ Ρ ΠΊΠΎΠ΄;
- ΡΠ΅ΡΡΠΈΡΡΠΊΠ°ΡΠΈ ML/AI ΠΊΡΡΡΡΠ² (Coursera, edX ΡΠΎΡΠΎ);
- ΡΡΠ°ΡΡΡ Π°Π±ΠΎ ΠΏΠ΅ΡΠ΅ΠΌΠΎΠ³ΠΈ Π² ML-Π·ΠΌΠ°Π³Π°Π½Π½ΡΡ (Kaggle, Zindi, ΡΠΎΡΠΎ).
Π’ΠΈ Π±ΡΠ΄Π΅Ρ Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π°ΡΠΈ Π·Π°:
- ΡΠΎΠ·ΡΠΎΠ±ΠΊΡ ΡΠ° Π²Π΄ΠΎΡΠΊΠΎΠ½Π°Π»Π΅Π½Π½Ρ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ Π½Π°Π²ΡΠ°Π½Π½Ρ Π΄Π»Ρ ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΡΡ ΡΡΠ½ ΡΠ° Π·Π½ΠΈΠΆΠΎΠΊ Π½Π° ΡΠΎΠ²Π°ΡΠΈ;
- Π°Π½Π°Π»ΡΠ· Π΄Π°Π½ΠΈΡ Π΄Π»Ρ ΡΠ΄Π΅Π½ΡΠΈΡΡΠΊΠ°ΡΡΡ ΡΠ΅Π½Π΄Π΅Π½ΡΡΠΉ ΡΠ° ΡΠ°Π±Π»ΠΎΠ½ΡΠ², ΡΠΊΡ ΠΌΠΎΠΆΡΡΡ Π²ΠΏΠ»ΠΈΠ½ΡΡΠΈ Π½Π° ΡΡΠ½ΠΎΡΡΠ²ΠΎΡΠ΅Π½Π½Ρ ΡΠ° Π·Π½ΠΈΠΆΠΊΠΈ;
- ΡΠΏΡΠ²ΠΏΡΠ°ΡΡ Π· ΠΊΠΎΠΌΠ°Π½Π΄ΠΎΡ ΠΏΡΠΎΠ΄ΡΠΊΡΡ Π΄Π»Ρ Π²ΠΈΠ·Π½Π°ΡΠ΅Π½Π½Ρ Π²ΠΈΠΌΠΎΠ³ Π΄ΠΎ Π΄Π°Π½ΠΈΡ Ρ ΡΠΎΡΠΌΡΠ²Π°Π½Π½Ρ ΡΡΡΠ°ΡΠ΅Π³ΡΡ Π·Π±ΠΎΡΡ Π΄Π°Π½ΠΈΡ ;
- ΡΠΎΠ·ΡΠΎΠ±ΠΊΡ Ρ Π²ΠΏΡΠΎΠ²Π°Π΄ΠΆΠ΅Π½Π½Ρ ΡΠΈΡΡΠ΅ΠΌ ΠΌΠΎΠ½ΡΡΠΎΡΠΈΠ½Π³Ρ ΡΠ° ΠΎΡΡΠ½ΠΊΠΈ Π΅ΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½ΠΈΡ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ;
- ΠΏΡΠ΄Π³ΠΎΡΠΎΠ²ΠΊΡ ΡΠ° ΠΏΡΠ΅Π·Π΅Π½ΡΠ°ΡΡΡ Π°Π½Π°Π»ΡΡΠΈΡΠ½ΠΈΡ Π·Π²ΡΡΡΠ² Π΄Π»Ρ ΠΊΠ΅ΡΡΠ²Π½ΠΈΡΡΠ²Π° Π· ΡΡΠ»Π»Ρ ΠΏΠΎΡΡΠ½ΠΈΡΠΈ ΡΡΡΠ΅Π½Π½Ρ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΡΠ° Π²ΠΏΠ»ΠΈΠ² Π½Π° Π±ΡΠ·Π½Π΅Ρ-ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΈ.
ΠΠΈ ΠΏΡΠΎΠΏΠΎΠ½ΡΡΠΌΠΎ:
- ΠΌΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ Π²ΡΠ΄Π΄Π°Π»Π΅Π½ΠΎΡ ΡΠΎΠ±ΠΎΡΠΈ Π°Π±ΠΎ ΠΎΡΡΡ Π· Π³Π΅Π½Π΅ΡΠ°ΡΠΎΡΠΎΠΌ ΡΠ° ΡΡΠ°ΡΠ»ΡΠ½ΠΊΠΎΠΌ;
- Π³Π½ΡΡΠΊΠΈΠΉ Π³ΡΠ°ΡΡΠΊ;
- ΠΌΠ΅Π΄ΠΈΡΠ½Π΅ ΡΡΡΠ°Ρ ΡΠ²Π°Π½Π½Ρ;
- ΡΠΈΠ·ΠΈΠΊΠΎΠ²Π΅ ΡΡΡΠ°Ρ ΡΠ²Π°Π½Π½Ρ ΠΆΠΈΡΡΡ;
- ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΠ° Π€ΠΠ/ΠΠΠ- ΠΊΠΎΠ½ΡΡΠ°ΠΊΡ;
- Π·Π½ΠΈΠΆΠΊΠΈ Ρ ΠΌΠ°Π³Π°Π·ΠΈΠ½Π°Ρ ΡΠ° ΡΠ΅ΡΡΠΎΡΠ°Π½Π°Ρ Fozzy Group;
- ΠΏΡΠΈΡ ΠΎΠ»ΠΎΠ³ΡΡΠ½Π° ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΠ°;
- ΡΡΡΠ±ΠΎΡΠ»ΠΈΠ²Ρ ΠΊΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½Ρ ΠΊΡΠ»ΡΡΡΡΡ, ΡΠΊΡ ΠΌΠΈ ΡΠ°Π·ΠΎΠΌ Π· ΡΠΎΠ±ΠΎΡ Π±ΡΠ΄Π΅ΠΌΠΎ ΡΠΎΠ·Π²ΠΈΠ²Π°ΡΠΈ;
- ΠΊΠΎΠΌΠ°Π½Π΄Ρ, Π· ΡΠΊΠΎΡ ΡΠΈ Π·ΠΌΠΎΠΆΠ΅Ρ ΡΠ΅Π°Π»ΡΠ·ΡΠ²Π°ΡΠΈ ΡΠ²ΠΎΡ ΡΠ΄Π΅Ρ, Π΅ΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΡΠ²Π°ΡΠΈ Ρ Π²ΡΠ΄ΡΡΠ²Π°ΡΠΈ ΡΠ΅Π±Π΅ Π² ΠΊΠΎΠ»Ρ Π΄ΡΡΠ·ΡΠ²;
- Ρ ΡΡΠ»Π° ΠΊΡΠΏΠ° ΡΡΡΠ±ΠΎΡΠΈΠ½ΠΎΠΊ (ΡΡΡΠ°Ρ ΡΠ²Π°Π½Π½Ρ (Π² Π£ΠΊΡΠ°ΡΠ½Ρ), Π·Π½ΠΈΠΆΠΊΠΈ Π² Π½Π°ΡΠΈΡ Π±ΡΠ·Π½Π΅ΡΠ°Ρ ΡΠ° Ρ ΠΏΠ°ΡΡΠ½Π΅ΡΡΠ²).
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Β· 71 views Β· 9 applications Β· 13d
Data Scientist (CVM)
Full Remote Β· Countries of Europe or Ukraine Β· Product Β· 2 years of experience Β· Intermediate Ukrainian Product πΊπ¦ΠΡΠΈΠ²ΡΡ! ΠΠΈ β E-Com β ΠΊΠΎΠΌΠ°Π½Π΄Π° Π·Π°ΠΊΠΎΡ Π°Π½ΠΈΡ Ρ Foodtech ΡΠ° ΡΠΊΡΠ°ΡΠ½ΡΡΠΊΠΈΠΉ ΠΏΡΠΎΠ΄ΡΠΊΡ. ΠΠΈ β ΠΎΠΊΡΠ΅ΠΌΠΈΠΉ Π°ΠΌΠ±ΡΡΠ½ΠΈΠΉ ΡΡΠ°ΡΡΠ°ΠΏ Π² ΡΠ°ΠΌΠΊΠ°Ρ ΠΏΠΎΡΡΠΆΠ½ΠΎΡ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ. Π ΠΊΡΡΡΠΎΡ ΡΠΈΠ»ΡΠ½ΠΎΡ ΠΊΠΎΠΌΠ°Π½Π΄ΠΎΡ ΡΠ° Π²Π΅Π»ΠΈΠΊΠΈΠΌΠΈ ΠΌΡΡΡΠΌΠΈ. Π ΡΠ΅ β ΠΌΠΈ Π»Π°ΠΌΠ°ΡΠΌΠΎ ΡΡΠ΅ΡΠ΅ΠΎΡΠΈΠΏΠΈ, ΡΠΎ ΡΠΈΡΠ΅ΠΉΠ» β ΡΠΎ Π»ΠΈΡΠ΅ ΠΏΡΠΎ ΠΏΠΎΠΌΡΠ΄ΠΎΡΡΠΈΠΊΠΈ....ΠΡΠΈΠ²ΡΡ!
ΠΠΈ β E-Com β ΠΊΠΎΠΌΠ°Π½Π΄Π° Π·Π°ΠΊΠΎΡ Π°Π½ΠΈΡ Ρ Foodtech ΡΠ° ΡΠΊΡΠ°ΡΠ½ΡΡΠΊΠΈΠΉ ΠΏΡΠΎΠ΄ΡΠΊΡ.
ΠΠΈ β ΠΎΠΊΡΠ΅ΠΌΠΈΠΉ Π°ΠΌΠ±ΡΡΠ½ΠΈΠΉ ΡΡΠ°ΡΡΠ°ΠΏ Π² ΡΠ°ΠΌΠΊΠ°Ρ ΠΏΠΎΡΡΠΆΠ½ΠΎΡ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ. Π ΠΊΡΡΡΠΎΡ ΡΠΈΠ»ΡΠ½ΠΎΡ ΠΊΠΎΠΌΠ°Π½Π΄ΠΎΡ ΡΠ° Π²Π΅Π»ΠΈΠΊΠΈΠΌΠΈ ΠΌΡΡΡΠΌΠΈ.
Π ΡΠ΅ β ΠΌΠΈ Π»Π°ΠΌΠ°ΡΠΌΠΎ ΡΡΠ΅ΡΠ΅ΠΎΡΠΈΠΏΠΈ, ΡΠΎ ΡΠΈΡΠ΅ΠΉΠ» β ΡΠΎ Π»ΠΈΡΠ΅ ΠΏΡΠΎ ΠΏΠΎΠΌΡΠ΄ΠΎΡΡΠΈΠΊΠΈ.
ΠΠΎΠ²ΡΡ, ΡΠ΅Ρ Π½ΡΡΠ½Π° ΡΠ°ΡΡΠΈΠ½Π° Π½Π°ΡΠΈΡ ΠΏΡΠΎΠ΅ΠΊΡΡΠ² Π΄Π°Ρ ΡΡΠ»Π΅ ΠΏΠΎΠ»Π΅ Π΄Π»Ρ ΠΊΡΠ΅Π°ΡΠΈΠ²Ρ ΡΠ° ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΡΠ½ΠΎΠ³ΠΎ ΠΌΠΈΡΠ»Π΅Π½Π½Ρ.
ΠΠΈ ΡΡΠΊΠ°ΡΠΌΠΎ Data Scientist Π² ΠΊΠΎΠΌΠ°Π½Π΄Ρ Customer Value Management.
ΠΠ°Π½Π° ΠΊΠΎΠΌΠ°Π½Π΄Π° Π²ΠΏΡΠΎΠ²Π°Π΄ΠΆΡΠ²Π°Π΄ΠΆΡΡ Π½Π°ΠΉΠΊΡΠ°ΡΡ ΠΏΡΠ°ΠΊΡΠΈΠΊΠΈ ΡΠ²ΡΡΠΎΠ²ΠΈΡ Π»ΡΠ΄Π΅ΡΡΠ² e-commerce, Π·Π°ΡΠ½ΠΎΠ²Π°Π½Ρ Π½Π° Π΄Π°ΡΠ°-Π΄ΡΡΠ²Π΅Π½ ΠΏΡΠ΄Ρ ΠΎΠ΄Π°Ρ , ΠΏΠΎΠ±ΡΠ΄ΠΎΠ²Ρ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΊΠΎΠΌΡΠ½ΡΠΊΠ°ΡΡΠΉ, ΡΠΎΠ·ΡΠΎΠ±ΡΡ ΠΊΡΠ°ΡΠΈΡ ΠΏΠ΅ΡΠ΅Π΄ΠΈΠΊΡΠΈΠ²Π½ΠΈΡ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΡΠ° Π±Π°Π³Π°ΡΠΎ ΡΠ½ΡΠΎΠ³ΠΎ. ΠΠΎΠΌΠ°Π½Π΄Π° ΠΌΠ°Ρ ΠΏΠΎΠ²Π½ΠΈΠΉ Π½Π°Π±ΡΡ ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΡΠΉ, ΡΠΊΡ Π·ΠΎΡΠ΅ΡΠ΅Π΄ΠΆΠ΅Π½Ρ Π½Π° ΡΠΎΠ±ΠΎΡΡ Π·Π° ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΠΈΠΌΠΈ ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊΠ°ΠΌΠΈ (ΡΠΎΠ·Π²ΠΈΡΠΎΠΊ, ΡΡΡΠΈΠΌΠ°Π½Π½Ρ ΠΏΠΎΠΊΡΠΏΡΡΠ², Π·Π±ΡΠ»ΡΡΠ΅Π½Π½Ρ ΡΠ°ΡΡΠΎΡΠΈ ΠΏΠΎΠΊΡΠΏΠΎΠΊ Π°Π±ΠΎ ΠΎΠ±ΡΡΠ³Ρ ΠΏΠΎΠΊΡΠΏΠΎΠΊ ΡΠΎΡΠΎ). Π ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΡΠΎΠ±ΠΎΡΠΈ Π·Π°ΠΏΡΡΠΊΠ°ΡΡΡΡΡ ΠΊΠ°ΠΌΠΏΠ°Π½ΡΡ Π· ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΎΠΏΠΎΠ·ΠΈΡΡΠΉ, ΡΠΊΡ ΡΡΠ²ΠΎΡΡΡΡΡΡΡ Π·Π° Π΄ΠΎΠΏΠΎΠΌΠΎΠ³ΠΎΡ ΡΡΡΠ°ΡΠ½ΠΈΡ Π°Π½Π°Π»ΡΡΠΈΡΠ½ΠΈΡ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ, Π²ΡΠ΄Π±ΡΠ²Π°ΡΡΡΡΡ Π°Π½Π°Π»ΡΠ· ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡΠ² ΡΠ° ΠΏΠΎΠΊΡΠ°ΡΠ΅Π½Π½Ρ Π΅ΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΏΡΠΎΠΏΠΎΠ·ΠΈΡΡΠΉ.
Π§ΠΈΠΌ ΠΏΠΎΡΡΡΠ±Π½ΠΎ Π±ΡΠ΄Π΅ Π·Π°ΠΉΠΌΠ°ΡΠΈΡΡ:
- Π·Π°ΠΏΠΈΡ, ΠΏΡΠ΄Π³ΠΎΡΠΎΠ²ΠΊΠ° ΡΠ° Π°Π½Π°Π»ΡΠ· Π΄Π°Π½Π½ΠΈΡ ;
- ΠΏΠΎΠ±ΡΠ΄ΠΎΠ²Π° ΡΠ° Π²Π΄ΠΎΡΠΊΠΎΠ½Π°Π»Π΅Π½Π½Ρ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ Π΄Π°Π½ΠΈΡ ;
- ΡΠΎΡΠΈΠΆΠ½Π΅Π²ΠΈΠΉ Π°Π½Π°Π»ΡΠ· ΡΠ½ΡΡΡΠ°ΡΠΈΠ² Π΄ΠΎ Ρ ΠΏΡΡΠ»Ρ ΡΡ ΡΠ΅Π°Π»ΡΠ·Π°ΡΡΡ;
- ΡΠΈΠ½ΡΠ΅Π· ΡΠ° ΠΏΡΠ΅Π·Π΅Π½ΡΠ°ΡΡΡ Π°Π½Π°Π»ΡΠ·Ρ ΡΠ½ΡΡΡΠ°ΡΠΈΠ²;
- ΠΏΠΎΡΡΠΊ Π½ΠΎΠ²ΠΈΡ ΡΠ»ΡΡ ΡΠ² ΡΠ΅Π°Π»ΡΠ·Π°ΡΡΡ ΡΠ½ΡΡΡΠ°ΡΠΈΠ² Ρ Π΄Π°Π½ΠΈΡ ;
- ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Π½Ρ A/B ΡΠ΅ΡΡΡΠ².
Π©ΠΎ Π΄Π»Ρ Π½Π°Ρ Π²Π°ΠΆΠ»ΠΈΠ²ΠΎ:
- Π΄ΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π²ΡΠ΄ 2-Ρ ΡΠΎΠΊΡΠ² ΡΠΊ Data Scientist Π² ΡΠΎΠ·Π΄ΡΡΠ±Π½ΡΠΉ ΡΠΎΡΠ³ΡΠ²Π»Ρ, ΡΠΎΠ·Π΄ΡΡΠ±Π½ΠΈΡ Π±Π°Π½ΠΊΠ°Ρ Π°Π±ΠΎ ΡΠ΅Π»Π΅ΠΊΠΎΠΌΡΠ½ΡΠΊΠ°ΡΡΠΉΠ½ΠΈΡ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡΡ (Π±Π°ΠΆΠ°Π½ΠΎ, Π½Π΅ ΠΎΠ±ΠΎΠ²'ΡΠ·ΠΊΠΎΠ²ΠΎ);
- Π²ΠΈΡΠ° ΡΠ΅Ρ Π½ΡΡΠ½Π°/Π΅ΠΊΠΎΠ½ΠΎΠΌΡΡΠ½Π° ΠΎΡΠ²ΡΡΠ°;
- Π²ΠΏΠ΅Π²Π½Π΅Π½Ρ Π·Π½Π°Π½Π½Ρ Data Analysis, Python, SQL - ΠΎΠ±ΠΎΠ²'ΡΠ·ΠΊΠΎΠ²ΠΎ;
- Π²ΠΏΠ΅Π²Π½Π΅Π½Ρ Π·Π½Π°Π½Π½Ρ ΡΠ° Π·Π°ΡΡΠΎΡΡΠ²Π°Π½Π½Ρ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ½ΠΎΡ ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠΈ;
- Π²ΠΎΠ»ΠΎΠ΄ΡΠ½Π½Ρ Π½Π°Π²ΠΈΡΠΊΠ°ΠΌΠΈ Machine Learning;
- Π²ΠΎΠ»ΠΎΠ΄ΡΠ½Π½Ρ Π°Π½Π³Π»ΡΠΉΡΡΠΊΠΎΡ ΠΌΠΎΠ²ΠΎΡ Π½Π° ΡΡΠ²Π½Ρ Π½Π΅ Π½ΠΈΠΆΡΠ΅ Intermediate.
ΠΠΈ ΠΏΡΠΎΠΏΠΎΠ½ΡΡΠΌΠΎ:
- ΠΌΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ Π²ΡΠ΄Π΄Π°Π»Π΅Π½ΠΎΡ ΡΠΎΠ±ΠΎΡΠΈ Π°Π±ΠΎ ΠΎΡΡΡ Π· Π³Π΅Π½Π΅ΡΠ°ΡΠΎΡΠΎΠΌ ΡΠ° ΡΡΠ°ΡΠ»ΡΠ½ΠΊΠΎΠΌ;
- Π³Π½ΡΡΠΊΠΈΠΉ Π³ΡΠ°ΡΡΠΊ;
- ΠΌΠ΅Π΄ΠΈΡΠ½Π΅ ΡΡΡΠ°Ρ ΡΠ²Π°Π½Π½Ρ;
- ΡΠΈΠ·ΠΈΠΊΠΎΠ²Π΅ ΡΡΡΠ°Ρ ΡΠ²Π°Π½Π½Ρ ΠΆΠΈΡΡΡ;
- ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΠ° Π€ΠΠ/ΠΠΠ- ΠΊΠΎΠ½ΡΡΠ°ΠΊΡ;
- Π·Π½ΠΈΠΆΠΊΠΈ Ρ ΠΌΠ°Π³Π°Π·ΠΈΠ½Π°Ρ ΡΠ° ΡΠ΅ΡΡΠΎΡΠ°Π½Π°Ρ Fozzy Group;
- ΠΏΡΠΈΡ ΠΎΠ»ΠΎΠ³ΡΡΠ½Π° ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΠ°;
- ΡΡΡΠ±ΠΎΡΠ»ΠΈΠ²Ρ ΠΊΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½Ρ ΠΊΡΠ»ΡΡΡΡΡ, ΡΠΊΡ ΠΌΠΈ ΡΠ°Π·ΠΎΠΌ Π· ΡΠΎΠ±ΠΎΡ Π±ΡΠ΄Π΅ΠΌΠΎ ΡΠΎΠ·Π²ΠΈΠ²Π°ΡΠΈ;
- ΠΊΠΎΠΌΠ°Π½Π΄Ρ, Π· ΡΠΊΠΎΡ ΡΠΈ Π·ΠΌΠΎΠΆΠ΅Ρ ΡΠ΅Π°Π»ΡΠ·ΡΠ²Π°ΡΠΈ ΡΠ²ΠΎΡ ΡΠ΄Π΅Ρ, Π΅ΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΡΠ²Π°ΡΠΈ Ρ Π²ΡΠ΄ΡΡΠ²Π°ΡΠΈ ΡΠ΅Π±Π΅ Π² ΠΊΠΎΠ»Ρ Π΄ΡΡΠ·ΡΠ²;
- Ρ ΡΡΠ»Π° ΠΊΡΠΏΠ° ΡΡΡΠ±ΠΎΡΠΈΠ½ΠΎΠΊ (ΡΡΡΠ°Ρ ΡΠ²Π°Π½Π½Ρ (Π² Π£ΠΊΡΠ°ΡΠ½Ρ), Π·Π½ΠΈΠΆΠΊΠΈ Π² Π½Π°ΡΠΈΡ Π±ΡΠ·Π½Π΅ΡΠ°Ρ ΡΠ° Ρ ΠΏΠ°ΡΡΠ½Π΅ΡΡΠ²).
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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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