Jobs Data Science

About project: Mobile marketing and audience platform

Project: sell-side omnichannel advertising platform powered by real-time mobile audiences. We deliver hundreds of millions of ads per day across thousands of global leading apps and websites. Our platform uses AI to deliver more efficient, effective and precise digital advertising campaigns. Our direct integration with thousands of mobile publishers gives us access to more than 50 billion first-party data signals per day across the globe. Marketers use these anonymized signals to understand and predict consumer behavior, identify new opportunities and fuel business growth.
 

Role Overview:

If you’re a talented Data Scientist looking to be part of a fast-growing, innovative R&D team, shape the future of decision-making in business, work on cutting-edge technologies and vast amounts of global real-time data, affect billions of mobile users, and become an industry leader - then let’s talk!

Our mobile data platform enables organizations to uncover insights and make data-driven decisions that enhance strategies and drive growth. We are seeking a Senior Data Scientist to join our Data Science & Engineering team.
 

Responsibilities:

• Analyze large and complex data to discover hidden patterns and opportunities.
• Develop and own end-to-end highly scalable data pipelines.
• Develop state-of-the-art machine learning models, and algorithms and build solutions in monetization fields.
• Provisioning data models for real-time (online) processing
• Shape new data products, and constantly improve the existing products.
• Collaborate with multiple teams including Product, BI, Analysts, DevOps, R&D and Marketing to lead and accomplish overall solutions.
• Oversee a complete release to production, including analyzing requirements, testing, monitoring and measuring results, multitasking, and quickly responding to critical issues.

Requirements:

Must:

• B.Sc./M.A/M.Sc. degree in Computer Science, Engineering, Math, Statistics, or other equivalent fields.
• 6 years as a Data Scientist - a strong background in ML concepts and models.
• Hands-on experience with Python, especially with data science ML libraries (regression & classification algorithms e.g. XGB or LGBM, recommendation system algorithms, etc.)
• Understanding of databases and SQL for data retrieval.
• Monitoring and alerting tools (e.g. Grafana, Kibana)
   

Advantage:

• Knowledge in the programmatic advertising landscape – DSPs, SSPs, exchanges – significant advantage.
• Experience with relational (e.g. Vertica, VoltDB) and non-relational (e.g. MongoDB) databases.
• Experience with PMML
  
  Interview process: 2-3 stages (technical/test task and interpersonal + HR).

Responsibilities:

‌

• Design, develop and optimize Large Language Models for various NLP tasks such as text generation, summarization, translation, and question-answering
• Conduct research and experiments to push the boundaries of LLM capabilities and performance
• Collaborate with cross-functional teams (engineering, product, research) to integrate LLMs into product offerings
• Develop tools, pipelines and infrastructure to streamline LLM training, deployment and monitoring
• Analyze and interpret model outputs, investigate errors/anomalies, and implement strategies to improve accuracy
• Stay current with the latest advancements in LLMs, NLP and machine learning research
• Communicate complex technical concepts to both technical and non-technical stakeholders

Requirements:

‌

• MS or PhD degree in Computer Science, Data Science, AI, or a related quantitative field
• 4+ years of hands-on experience developing and working with deep learning models, especially in NLP/LLMs
• Expert knowledge of Python, PyTorch, TensorFlow, and common deep learning libraries
• Strong understanding of language models, attention mechanisms, transformers, sequence-to-sequence modeling
• Experience training and fine-tuning large language models
• Proficiency in model deployment, optimization, scaling and serving
• Excellent problem-solving, analytical and quantitative abilities
• Strong communication skills to present technical information clearly
• Ability to work collaboratively in a team environment
• Fluency in Ukrainian and English

Preferred:

• Research experience in LLMs, NLP, machine learning
• Experience working with multi-modal data (text, image, audio)
• Knowledge of cloud platforms like AWS, GCP for model training
• Understanding of MLOps and production ML workflows
• Background in information retrieval, knowledge graphs, reasoning

Data Science Lead

Full-time | Remote

About Us

ExpoPlatform is a UK-based product company that builds a SaaS platform for large-scale events, exhibitions, and online communities. We’re a distributed team — people work from the UK, Ukraine, Poland, Brazil, Pakistan, and other locations — so communication is mostly in English, though some internal teams also use Ukrainian. The platform itself is mature, high-load, and cloud-native — fully hosted on AWS. We are expanding our AI and data science capabilities and looking for a strong Data Science Lead to drive this direction.

About the Role

We’re looking for a Data Science Lead with a strong background in Data Science, Leadership and Operations to join our team. In this role, you will lead and execute AI-driven initiatives, build scalable data solutions, and shape the data science strategy. You will collaborate closely with product, engineering, and business teams, taking ownership of the full ML lifecycle — from data preparation and experimentation to deployment, monitoring, and retraining — while ensuring data quality, governance, and responsible AI practices.

Responsibilities

• Lead and execute AI and data science initiatives, including recommendation systems, analytics solutions, and intelligent automation
• Define and shape data science strategy aligned with business and product goals
• Own the full ML lifecycle: data preparation, experimentation, deployment, monitoring, and retraining
• Build and maintain high-performance REST APIs and asynchronous services using FastAPI
• Design and maintain scalable cloud-native solutions on AWS
• Collaborate with Product, Engineering, and business stakeholders to translate data insights into business value
• Ensure data quality, governance, and responsible AI practices
• Contribute to architectural and infrastructure decisions.

Requirements

• 4+ years of hands-on experience in Data Science / Applied Machine Learning
• Strong Python expertise (Python 3.13 as primary language)
• Solid experience with Pandas, NumPy, Scikit-Learn
• Strong experience with FastAPI for high-performance REST API development
• Production experience with AWS services, including AWS Lambda and serverless infrastructure
• Strong experience with Terraform for Infrastructure as Code (IaC) and infrastructure provisioning
• Experience working with analytical databases (ClickHouse is a strong advantage)
• Strong SQL skills and experience with large-scale data processing
• Experience leading projects and working cross-functionally
• English — Upper-Intermediate or higher.

Nice to Have

• Experience with LLMs (OpenAI, Amazon Bedrock, Hugging Face)
• Experience with vector databases
• Experience building AI-driven search or chatbot systems
• Understanding of agile methodologies and end-to-end data science project management

Technology Stack

• Python 3.13
• FastAPI
• AWS (including AWS Lambda)
• ClickHouse
• Terraform

What We Offer

• The opportunity to architect and build scalable, cloud-native systems 
• Work with cutting-edge AI and data science technologies
• Career growth within an international and fast-scaling SaaS company
• Competitive salary and long-term financial stability

• Paid vacation, sick leave, and public holidays
• Semi-flexible working hours (Mon–Fri, 8 hours)
• Fully remote and distributed team environment
• Free English courses and education budget.

We are looking for a Senior Data Scientist to help build data-driven products that significantly improve customer experience on a large-scale betting platform. You will work on high-impact machine learning models, take full ownership from research to production, and directly influence product and business decisions. This role goes beyond modeling – you will deploy models into production, run experiments, monitor results, and partner closely with product and business teams.

You will work on models and systems such as:

• Churn, retention and LTV prediction;
• Risk scoring, fraud and responsible gambling models;
• Personalization and recommendation systems;
• Uplift and causal models to drive product decision;
• Real-time ML systems powering user experience;

• Experimentation frameworks and A/B testing.

   

What you’ll be doing:

• Design, train and productionize machine learning models for product, risk and personalization use cases;
• Own models end-to-end: data exploration, feature engineering, modeling, deployment and monitoring;
• Design and analyze A/B tests to measure the real business impact of models and product changes;
• Collaborate closely with product managers, engineers and analysts to translate business problems into ML solutions;
•  Monitor model performance, data quality, drift and key business metrics after deployment;

• Communicate insights and recommendations to business and product stakeholders.

   

What we’re looking for:

• 4+ years of experience as a Data Scientist or in a similar applied ML role;
• Strong hands-on experience with Python and SQL;
• Proven experience building and deploying ML models in production;
• Experience with churn, LTV, behavioral or risk-related models;
• Solid understanding of statistics, experimentation and A/B testing;
• Ability to clearly explain technical results to non-technical stakeholders.

Nice to have:

• Experience in gaming, fintech or other high-scale consumer products;
• Experience with recommendation systems or risk/fraud models;
• Familiarity with cloud environments and basic MLOps practices;
• Experience working with real-time or near real-time ML systems.

Who We Are:

We are DATA.BET, a product IT company that develops comprehensive technological solutions for sports, esports, and virtual sports betting in a single sportsbook.
Our key products - Odds Feed, AI-powered risk management system, and fast Single Page Application - help partners gain stable profits and a competitive advantage.
Our team consists of more than 250 professionals. 

An international product company — a leader in the live-streaming industry — is looking for an AI / Data Architect in Warsaw.

The platform has over 450 million registered users and enables creators worldwide to monetize their talents through live streaming. The team consists of 350+ global professionals.

Requirements:

• experience in data / AI architecture
• building data pipelines, Data Lakes, DWH
• strong SQL + NoSQL
• experience with distributed systems
• deploying AI/ML models to production
• experience with LLM / AI APIs
• cloud platforms (GCP / AWS / others), Docker / Kubernetes

They offer:

• Stock options
• Medical insurance (100% for employees and 75% for family members)
• Office lunches
• Parking
• Multisport card

Location: Warsaw (office-based).

Responsibilities

Model Fine-Tuning and Deployment:

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

RAG Workflows:

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

MLOps Integration:

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

Scalable and Customizable Solutions:

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

End-to-End Workflow Automation:

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

Advanced Monitoring and Analytics:

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

Model Monitoring and Maintenance:

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

Collaboration:

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

Documentation:

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

Must-Have Skills

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

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

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

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

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

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

Version Control: Familiarity with version control systems like Git.

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

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

Nice-to-Have Skills

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

Deep Learning: Experience with deep learning models and techniques.

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

Containerization: Experience with Docker and Kubernetes (EKS).

Serverless Architectures: Experience with AWS Lambda and Step Functions.

Rule engine frameworks: Like Drools or similar

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

We are looking for an experienced Data Scientist for a full-time job to join our team.


Requirements:
- 2+ years of experience as a data scientist
- BSc in Mathematics, Statistics, Computer Science, Economics, or another related field.
- Experience in using Python & SQL
- Experience with Airflow and GCP
- Experience with Git & CI/CD
- Upper Intermediate English — written and spoken
- Ability to design creative solutions for complex requirements
- Ability to learn and lead projects independently, and to work with minimal supervision with customers (tech & business)

Responsibilities:
- Conduct independent research, including defining research problems, creating research plans, designing experiments, developing algorithms, implementing code, and performing comprehensive comparisons against existing benchmarks;
- Clearly communicate your research findings to both technical and non-technical audiences
- Work on various data sources and apply sophisticated feature engineering capabilities
- Bring and use business knowledge
- Build and manage technical relationships with customers and partners.

We offer:
- remote time job, B2B contract
- 12 sick leaves and 18 paid vacation business days per year
- Comfortable work conditions (including MacBook Pro and Dell monitor on each workplace)
- Smart environment
- Interesting projects from renowned clients
- Flexible work schedule
- Competitive salary according to the qualifications
- Guaranteed full workload during the term of the contract
 

We are looking for a Computer Vision Engineer with a solid foundation in classical computer vision and hands-on experience implementing low-level CV algorithms. The ideal candidate will have exposure to SLAM, Visual-Inertial Odometry (VIO), and sensor fusion, with responsibilities adjusted according to experience level.

Responsibilities: 

• Implement and optimize low-level computer vision algorithms under guidance
• Contribute to modules related to feature extraction, tracking, matching, and geometric processing
• Assist in the integration and testing of SLAM/VIO components
• Perform experiments, analyze results, and help prepare reports
• Work closely with senior engineers to explore and apply recent CV research.

Required Qualifications:

• 1+ years of hands-on experience with classical computer vision
• Basic understanding of geometric computer vision principles
• Experience implementing low-level CV algorithms (e.g., filtering, feature detection, camera models)
• Familiarity with SLAM or VIO concepts
• Working knowledge of C++ and Linux
• Ability to learn quickly and navigate recent CV researc
• Degree or ongoing studies in a STEM field.

Nice to Have:

• Experience with Python
• Knowledge of neural networks and CV libraries/frameworks (OpenCV, NumPy, PyTorch, ONNX, Eigen, etc.)
• Experience with sensor fusion techniques
• Familiarity with embedded or real-time systems

About the Company

 European deep-tech company developing innovative AI solutions at the intersection of sensor technology, data science, and machine learning. Our products transform complex data from physical sensors into accurate analytical insights for real-time decision-making across multiple industries, including environmental monitoring, product quality control, and applied research.

The team works at the intersection of artificial intelligence, signal processing, and experimental analytics, building advanced models for pattern recognition and predictive analysis based on complex datasets.

Responsibilities

-Process, clean, and validate data for building ML/DL models.

-Design, implement, and optimize Machine Learning and Deep earning models.

-Conduct experiments including training, testing, fine-tuning, and hyperparameter optimization.

-Analyze results and present insights clearly to the team.

-Improve data collection and preprocessing pipelines.

-Collaborate closely with the ML Lead and engineering team.

-Participate in selecting modeling approaches and experimental strategies.

-Work with sensor-generated and multidimensional datasets.

Requirements

-5+ years of experience in Data Science / Machine Learning / Deep Learning.

-Bachelor’s or Master’s degree in Computer Science, Engineering, Applied Mathematics, or a related field.

-Commercial experience building and deploying ML/DL models.

Strong understanding of algorithms:

-Classification and regression

-Clustering

-CNN and fully connected neural networks

-Time-series analysis

-Experience working with databases (SQL or solutions from Oracle Corporation).

Practical experience with:

-Python

-PyTorch or TensorFlow

-scikit-learn

-NumPy, Pandas

-Experience training models in cloud environments (e.g., Amazon Web Services).

-Experience working in a Linux environment.

-Experience using Git.

-English level sufficient for technical communication.

Nice to Have

-Experience working with sensor or experimental datasets.

-Understanding of signal processing or pattern recognition.

-Experience building experimental ML pipelines. 

Requirements:

• 5+ experience in applying data science and machine learning techniques
• Python programming using classic scientific stack (numpy, pandas, scipy, scikit-learn, pytorch, lightgbm, matplotlib or any other visualization library)
• Understanding of classic machine learning algorithms and theory (supervised/unsupervised learning techniques, metrics and visualizations for interpretation of model performance, time-series analysis, anomaly detections, recommendation models, hyperparameter optimization)
• NLP (NLP techniques, GenAI, LLM finetuning)
• Statistics and optimization basic understanding
• Data manipulation and cleaning skills (fill missing entries / filter dataframes / apply certain changes to columns)
• Prior experience with Python web frameworks would be great, but not critical (FastAPI, Flask or anything else)
• Dockerization technologies
• Cloud solutions (any of AWS, GCP, Azure)
• Understanding of prompt engineering or experience with building scripts or applications around LLMs
• Proficient with SQL (Postgres, Snowflake), NoSQL (ElasticSearch, Mongo(optional))
• Monitoring services
   
• Responsibilities:
  Work on time series forecasting problems with real, complex data. Implement features, interpret their importance, train models and incorporate them in the production pipeline.
• Work on LLM-based solutions, which use techniques like Contextual RAG (Retrieval Augmented Generation), LLM as Judge, semantic search, chatbot services, etc.
• Create anomaly detection models and clustering methods for non-stationary, “dirty”, semi-organized data

We’re opening applications for a 3-month intensive internship where participants will work on real company projects under the guidance of Senior Data Scientists with years of hands-on experience.

PARTICIPANTS

We’re looking for motivated and ambitious individuals eager to sharpen their Data Science skills.

To participate in the internship program, you need:

- Strong Python knowledge;
- Good skills in programming and algorithms;
- Good background in Math;
- Hand on experience in Machine Learning/Computer Vision/NLP (including pet projects);
- Knowledge of base ML libraries in Python;
- Strong knowledge of Git;
- English level Intermediate (B2).

STRUCTURE / MENTOR

- Kick-off Session — meet your mentor, fellow interns, and dive into the Skill Evaluation stage.
- Project-Based Learning — solve real Data Science tasks, collaborate on projects, and gain experience in a structured learning environment.
- Continuous Mentorship — benefit from ongoing guidance, code reviews, and feedback sessions designed to accelerate your growth.
- Every intern will work under the guidance of Senior Data Scientists with 5+ years of industry experience.

RESULTS

- Practical experience in AI, Machine Learning, Deep Learning, NLP, and Computer Vision.
- The chance to work on impactful, production-level projects.
- Opportunity to receive a full-time job offer at WINSTARS.AI upon successful completion of the internship.

Fozzy Group is one of the largest trade industrial groups in Ukraine and one of the leading Ukrainian retailers, with over 700 outlets all around the country. It is also engaged in e-commerce, food processing & production, agricultural business, parcel delivery, logistics and banking.
 

The Foodtech team is looking for a Data Scientist to develop the Operational Analytics function for a fast growing food delivery business. In this role, you will focus on time series forecasting, regression modeling, simulation modeling, and end-to-end machine learning pipelines to support resource planning and operational decision-making.

You will be responsible for developing simulation-based models that serve as a foundation for a digital twin of operational processes, enabling scenario analysis, stress testing, and what-if simulations for capacity planning and operational optimization.

You will work closely with product, engineering, and operations teams to transform data into measurable business impact through production-ready ML and simulation solutions.
 

Job Responsibilities:

• Develop and implement time series forecasting models for resource planning (demand, capacity, couriers, delivery slots, operational load);
• Build regression and machine learning models to explain key drivers and support operational decisions;
• Apply a wide range of time series approaches from classical models (SARIMA, ETS, Prophet) and ML models (GB) to modern Deep Learning models (LSTM, Temporal CNNs, Transformers for TS);
• Design, build, and maintain end-to-end automated ML pipelines, deploy and operate models in production using AWS SageMaker;
• Orchestrate training and inference workflows with Apache Airflow;
• Analyze large-scale operational datasets and convert results into insights, forecasts, and actionable recommendations;
• Collaborate with product managers, engineers, and operations teams to define business problems and validate analytical solutions;
• Monitor model performance, forecast stability, and business impact over time.
   

Requirements:

• Bachelor’s Degree in Mathematics / Engineering / Computer Sciences / Quantitative Economics / Econometrics;
• Strong mathematical background in Linear algebra, Probability, Statistics & Optimization Techniques;
• At least 2 years working experience on Data Science;
• Experience of the full cycle of model implementation (data collection, model training and evaluation, model deployment and monitoring);
• Ability to work independently, proactively, and to decompose complex problems into actionable tasks.
   

Skills:

• Strong proficiency in Python with solid application of object-oriented programming (OOP) principles (modular design, reusable components, maintainable code);
• Solid experience in time series forecasting and regression modeling;
• Practical knowledge of:o Classical and ML forecasting techniques;
  o Statistical methods (hypothesis testing, confidence intervals, A/B testing);
• Advanced SQL skills (window functions, complex queries);
• Experience building automated ML pipelines;
• Understanding of MLOps principles (model versioning, monitoring, CI/CD for ML).
   

Nice to have:

• Hands-on experience with AWS SageMaker (training jobs, endpoints, model registry);
• Experience with Apache Airflow for data and ML workflow orchestration;
• Knowledge of Reporting and Business Intelligence Software (Power BI, Tableau);
• Experience working with large-scale production data systems.
   

What We Offer:

• Competitive salary;
• Professional & personal development opportunities;
• Being part of dynamic team of young & ambitious professionals;
• Corporate discounts for sport clubs and language courses;
• Medical insurance package.

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

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

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

Responsibilities

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

What We Look For

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

• English level: Intermediate+

   

Nice to have

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

• Experience with AutoML and Bayesian models

   

We Offer

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

We are looking for a Senior Data Scientist who can deliver end-to-end solutions, communicate both technical and business aspects to clients, and take ownership of production outcomes — helping Dastellar fulfill its mission to build custom AI/ML/Data Science solutions for businesses.

General Requirements:

• 5+ years in ML/Data Science.
• Strong experience with classical ML: sklearn, XGBoost/LightGBM, time-series models (ARIMA/Prophet/ETS or deep models), and recommender systems.
• Python 3.8+ proficiency; solid Pandas/NumPy; production-level experience with PyTorch or TensorFlow.
• Experience deploying ML models (Docker, CI/CD); basic cloud skills (AWS/GCP/Azure) preferred.
• Ability to communicate technical concepts to clients and translate risks into business impact.
• Autonomous and proactive; history of leading end-to-end tasks.
• English: B2+.
   

Nice-to-have / Growth Opportunities:

• LLM/GenAI experience: fine-tuning GPT/LLaMA/Hugging Face, building RAG/embedding pipelines, LangChain.
• Vector DBs: FAISS, Qdrant, Pinecone.
• MLOps basics: MLflow, W&B, CI/CD pipelines.
• Big Data: Spark, advanced SQL optimization.
• Domain expertise: energy or retail.
   

Main Responsibilities:

• Build classical ML models (classification, regression, clustering, recommendation, time-series).
• Data cleaning, validation, feature engineering, ETL/ELT pipelines.
• Design and deploy ML/GenAI solutions from POC to production.
• Integrate models with infrastructure (Docker, CI/CD; optionally Kubernetes/serverless).
• Monitor model performance and system health (metrics, data drift, alerts).
• Document solutions, create client presentations, and conduct R&D experiments.
• Maintain code quality: tests, reviews, reproducibility.
   

Soft Skills:

• Responsibility & ownership: end-to-end project delivery, meeting deadlines, post-release SLAs.
• Proactivity: proposing and implementing process or architecture improvements.
• Autonomy & problem-solving: translating ambiguous business goals into MVP roadmaps.
• Critical thinking: identifying data/assumption biases and proposing mitigation strategies.
   

What We Offer:

• Flexible part-time / contract work.
• Fast decision-making and low bureaucracy.
• Influence over architecture and stack choices.
• Remote collaboration and flexible schedule.
• Paid R&D participation (by agreement).

We are looking for a Computer Vision Engineer with solid ML/DL experience to join our team on challenging projects.
We consider engineers at Middle and Senior levels - tasks and responsibilities will be adjusted accordingly. 

Required Qualifications:

• 3+ years of experience in computer vision
• High proficiency in Python
• Strong experience with deep learning and the PyTorch framework for computer vision tasks
• Research experience in computer vision with a focus on delivering results
• Ability to work independently or within a small team, with a strong sense of ownership over results

Nice to Have:

• Experience with deep learning solutions for detection, tracking, image retrieval, and segmentation tasks
• Understanding of geometric computer vision principles
• Model optimization: quantization, pruning, neural network compilers
• Hands-on experience implementing low-level CV algorithms
• Experience with sensor fusion (IMU, magnetometer, GNSS, camera)
• Knowledge of C++