Jobs Data Science

The product that needs your engagement:

Mobion & OnTaxi

Our mission is to propel the success of global ride-hailing companies by delivering intelligent, scalable SaaS solutions. Since 2016, we have optimized operations, enhanced user experience, and driven profitability with powerful analytics, intuitive interfaces, and flexible customization. With deep industry expertise and cutting-edge technology, we provide the agility and insights needed to thrive in any market, empowering businesses to adapt and grow in a fast-changing environment.

Technical stack: Python, Strong SQL, Kubeflow, MLflow, ETL.

Your future team of professionals:

You will be part of the Mobion product Data & Research team and work with highly motivated professionals who are passionate about the project. You will collaborate with engineers, product managers and analysts.

Your future challenges:

Design, launch and continuously improve ML-based dynamic pricing algorithms for a real-time marketplace.

Impact you will make:

— Development of machine learning models: Building and supporting models for pricing, demand forecasting, routing and analyzing customer behavior

— Data Science: Work with large volumes of data to uncover hidden dependencies and anomalies

— Deploying models to production: Collaborate with engineers to integrate models into the system

— Model monitoring and optimization: Constantly monitoring model latency, scalability, and implement versioning strategy

— Working with Business Teams: Collaborate with product managers and other teams to integrate models into the decision-making process

— A/B Testing and Model Evaluation: Analyze the results of experiments to improve the product

— Own the full lifecycle of ML solutions, from hypothesis and experimentation to production rollout and business impact measurement

Skills we are looking for:

— 4+ years of experience in data science

— Proven experience developing ML models for Surge Pricing, Demand Forecasting, or similar high-load, real-time prediction systems

— Experience working with time-series, geospatial data, and dynamic pricing algorithms is a strong plus

— Experience with Python (statistical and ML packages) and Spark

— Expert knowledge in SQL

— Knowledge of mathematical and applied statistics, probability theory, regression analysis

— Experience with ML tools: Kubeflow, MLflow, and ETL tools

— Experience in data preparation, feature engineering, and model evaluation.

— Git and Gitflow Workflow knowledge

— Successful experience in developing and implementing DS solutions into a product that led to improved business results

— English Upper-Intermediate or higher

Additional skills we would appreciate:

— Familiarity with mobility, logistics, or transportation data and KPIs (ETA predictions, surge pricing, driver utilization)

Benefits you get

Maintain your work-life balance

• Take control of your workday with our flexible schedule.
• 19 days of annual vacation granted on a pro-rata basis.
• No overtime required.

Hybrid/remote type of cooperation

Whether you prefer to work remotely in the comfort of your home office or from one of our 4 offices, you can help promote the global visibility of exceptional Ukrainian products from anywhere in the world.

Stay healthy

• Comprehensive medical insurance from a leading Ukrainian provider, including a wide range of services and special programs for relatives, and special options for those who live abroad.
• 15 working days of sick leave per year — because your health is our priority.

Grow with us

• In-house English courses to increase your proficiency through our online courses and speaking clubs with a native speaker.
• Online learning activities, including workshops, lectures, and webinars to enhance your leadership, management, and self-awareness skills.
• Experience exchange with our team of professionals who eagerly share their deep expertise across various fields.
• Opportunities for promotions, rotations, and transitions when you believe your skills could benefit a different role or team.

Stand with Ukraine!

We have an active pro-Ukrainian position! This means that we help our army, our servicemen, and their families not only through constant donations and volunteering but also through our military leave policy, special health insurance options, and financial assistance.

We are a perfect match if you share our values

We are professionals in our field.

We know how to turn up our professionalism to the maximum, because we are experts who enjoy solving complex tasks.

We know how to focus on the product, think big, and at the same time delve deep into the details.

We create a calm and friendly atmosphere within the company.

We value the freedom of open and transparent communications.

Radical honesty is embedded in the foundation of our culture.

We are open to new things and constantly develop and practice regular knowledge sharing, which helps us evolve continuously.

Most importantly, the company creates an environment where we can fulfill our potential upon a solid foundation.

Our Hiring process

• CV screening
• Test Task
• Recruiting interview
• Technical interview
• Offer call

We strive to ensure transparency and openness in the hiring process, and we understand the importance of providing timely feedback. If your resume meets the job requirements, we will contact you to clarify details and schedule an interview within 2 weeks after your application is submitted.
The timeframe for feedback between interview stages may take up to 2 weeks.

We are building a next generation cross asset data fabric, which will bring together disparate data sources for the global bank. It will enable industry leading analytics, reporting and data science solutions. There are multiple streams within application covering Reporting, Data and Analytics.

Responsibilities

This role is for a Senior Business Intelligence (BI) Developer to create reporting framework and set up the ground work for creation of Tableau reporting via Dashboards and scheduled reporting. The ideal candidate will help to ensure highest level of quality of a product being developed while working very closely with different stakeholders spread across globally

Candidate will work independently and is highly self-motivated. Applies skills and knowledge of the business to test creative solutions to meet client and business needs.

Skills

Must have

Strong SQL and Data Analyst experience.

Python skills

Proven experience as a BI developer/Data Engineer

Background in data warehouse design (e.g., dimensional modeling) and data mining

Understanding of database management systems, online analytical processing (OLAP) and ETL (Extract, transform, load) framework

Team player, good on communication with Business Analysts or Traders,

Highly Motivated, quick learner, general interest in Equity/Equity

Derivatives Trading Business. Experience in Risk Department (Risk area).

Exposure to Equities business

Must have worked in Agile [min 2+ years]

Must have good analytical & logical skills

Strong interpersonal, oral and written communication

Nice to have

Experience in Tableau

Understanding of Big Data concepts and technologies (HDFS, Spark, Hive, Impala), enabling effective handling and analysis of large datasets.

Eastern Peak is looking for an experienced Data Scientist to join a fast-growing gaming-tech company building an AI-driven analytics and prediction engine on top of real-time sports data feeds. The team is currently small and hands-on, working on an MVP ahead of a funding round, with football as the initial focus sport, followed by tennis and American football.

What You'll Do:

• Work with large-scale, real-time sports data feeds (odds, live match events, player/team stats) from various providers.
• Design and build an intelligent algorithmic layer on top of raw real-time sports data to generate predictive insights and models.
• Develop and iterate on AI/ML-driven models that turn continuous streams of sports data into actionable, betting-relevant outputs.
• Work closely with a small, focused product team to help shape and ship the MVP.
• Adapt models and pipelines as the product expands from football into additional sports (tennis, American football, etc.).
  
  What We're Looking For:
• 4+ years of hands-on Data Science experience.
• Proven experience working with large, real-time sports datasets, the ability to think in terms of live, fast-changing sporting events rather than financial time series.
• Hands-on experience with at least one major sports data provider (Sportradar, Stats Perform, Sportmonks, Huddle, LSports, or similar)
• Strong applied Data Science / ML background, able to independently build predictive algorithms from raw data feeds.
• Experience within the sports iGaming industry.

Will be a plus:

• Background from a strong technical/analytics academic program.
• Experience building AI-native solutions (e.g., LLM-based or agentic tooling) on top of structured/real-time data.
• Prior experience in a startup or MVP-stage environment ahead of a fundraising milestone.

Data Science UA is a service company with deep expertise in AI and Data Science. Our story began in 2016 with the first Data Science UA Conference in Kyiv, and since then we've built one of the largest AI communities in Europe.

About the role:
We are looking for a Data Scientist to design and build data-driven and machine-learning capabilities for clients products - a global leader in Advertising Resource Management. 
You will work with Product, Engineering, and domain experts to translate business questions into analytically sound agentic solutions. The role covers the full data science lifecycle: problem definition, data exploration, experimentation, modelling, evaluation, and production-readiness assessment.

Responsibilities:
- Agentic Solutions & AI Orchestration (Core Focus)
- Autonomous Workflow Design: Architect, build, and deploy autonomous and semi-autonomous AI agents (using modern LLM orchestration, tool-use, and multi-agent frameworks) to automate complex, multi-step media management workflows.
- Tool-Using Agents: Develop agents capable of interacting directly with client's internal tools and external APIs (e.g., DSPs, ad servers, ERPs) to execute actions, run diagnostics, and fetch real-time campaign data.
- Human-in-the-Loop (HITL) Guardrails: Design robust evaluation, permissioning, and safety frameworks to ensure agentic decisions—especially those involving live budget allocations or financial adjustments—operate safely with appropriate human oversight.
- Media Planning & Predictive Intelligence
- Agent-Driven Media Planning: Build predictive frameworks (e.g., MMM, reach & frequency forecasting) and feed them into autonomous planning agents that can generate, critique, and optimize cross-channel media plans automatically.
- Scenario Simulation: Develop models that allow AI agents to simulate "what-if" budget scenarios, channel mixes, and KPI trade-offs to present optimized strategies to media planners.
- Autonomous Buying & In-Flight Optimization
- Real-Time Execution Agents: Create autonomous optimization agents that continuously monitor in-flight campaign performance, automatically reallocating budgets, adjusting pacing, and executing "next-best-action" optimizations.
- Multi-Platform Analytics: Model performance trends across programmatic platforms, direct publisher buys, and walled gardens to feed real-time decision loops for buying agents.
- Agentic Financial Reconciliation & Audit
- Automated Audit Agents: Build autonomous auditing agents that cross-reference planned spend, ad server delivery logs, publisher invoices, and contract terms to detect discrepancies and flag billing leakage.
- Anomaly & Leakage Detection: Design predictive algorithms for automated discrepancy resolution, overspend prevention, and financial clearing integrations (e.g., SAP, ERPs).
- Technical Leadership & Agent Governance
- Agent Observability & Evaluation: Establish team standards for agent benchmarking, prompt/tool evaluation, latency optimization, and failure mode analysis in production environments.
- Mentorship & Strategy: Mentor data scientists on modern LLM architectures, agentic design patterns (RAG, multi-agent systems, tool execution), and production deployment best practices. 

Requirements:
- Agentic AI & Modern LLM Architecture
- Agentic Frameworks: 3+ years of Data Science / ML experience, with hands-on expertise building and deploying production-grade agentic architectures using frameworks like LangGraph, CrewAI, AutoGen, or LlamaIndex.
- Tool Use & Function Calling: Proven experience implementing function calling, tool use, structured outputs (e.g., Pydantic/Instructor), and multi-agent coordination.
- LLM Engineering & RAG: Deep practical understanding of prompt engineering, fine- tuning, retrieval-augmented generation (RAG), context management, and vector database architectures (e.g., Qdrant, Pinecone, Milvus, PGVector).
- AI Guardrails & HITL: Experience designing safety mechanisms, rate/cost controls, human-in-the-loop (HITL) approval gates, and deterministic fallbacks for autonomous agent execution.
- Machine Learning, Statistics & Optimization
- Classical ML & Optimization: Proficiency in supervised/unsupervised ML (Scikit- learn, XGBoost/LightGBM) and mathematical optimization (e.g., SciPy, Pyomo, Google OR-Tools) for resource allocation, pricing, and budget pacing algorithms.
- Time Series & Anomaly Detection: Demonstrated experience building forecasting models (Prophet, ARIMA, NeuralProphet) and anomaly detection frameworks for automated financial/data audit workflows.

Will be a plus:
- Master’s degree or PhD in a quantitative field.
- Experience in advertising, marketing technology, or media optimisation.
- Experience with causal inference, uplift modelling, or marketing measurement.
- Familiarity with AWS, ETL processes, Docker, CI/CD, and model-monitoring tools.

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.   
 

At Fozzy Group, we are developing a next-generation Decision Support System that connects operational, commercial, and investment data to optimize financial effectiveness and drive guest experience. As a Data Scientist you will own the measurement layer to derive Initiative & Investment Performance Insights. The role 

is organized around three streams: 

• Effect Measurement & Causal Inference: quantify the true incremental impact of initiatives using A/B testing and quasi-experimental methods. Measure uplift in purchase frequency, guest spend, average check, and retention against properly designed control groups, and form clear conclusions on whether an initiative works and is worth its cost. 

• Driver & Heterogeneity Analysis: identify the most significant factors of project performance using ML: guest profile structure and dynamics, technology presence, assortment, distance to competitors – so the business can assess project potential per store, forecast effects, and scale more effectively. 

• Self-Service Validation & Monitoring: build reusable, automated frameworks for effect validation and monitoring that let Finance and business owners get verified answers on demand, with full source transparency and reproducibility.
You will join a cross-functional team to design and implement data-driven decision module that directly influence financial outcomes. 

Job Responsibilities 

• Design and run experiments (A/B tests) and quasi-experiments (difference-in-differences, synthetic control, propensity score matching), and apply causal ML to estimate the incremental and heterogeneous effects of operational, commercial, and investment initiatives. 

• Build uplift / heterogeneous-treatment-effect models to learn which guests, segments, and stores respond most, and quantify effects on key metrics – purchase frequency, share-of-wallet / guest spend, average check, CLV, and retention (via survival models) – for pilot versus control groups. 

• Develop interpretable driver models (gradient boosting with SHAP, causal forests) that explain project performance, and rank the factors with the highest impact on results. 

• Build store-level potential-scoring and effect-forecasting models – probabilistic and hierarchical forecasting, transfer learning and store embeddings to borrow signal across the network – to guide and prioritize scaling decisions. 

• Design adaptive early-stop logic using Bayesian sequential testing, multi-armed bandits, and change point detection, to reallocate or halt rollouts as soon as the economic signal turns marginal. 

• Translate financial and commercial questions into statistically rigorous measurement designs, and connect statistical results to economic value (incrementality, ROI, payback). 

• Build SQL and Python pipelines, reusable feature sets / a feature store, and a self-service experimentation framework. 

• Productionize and monitor models (MLOps) – drift detection, automated validation, reproducibility, and source transparency. 

• Partner with Finance, Investment, Commercial, and Operations teams to deliver verified, decision ready insights. 
 

Requirements 

• Bachelor’s Degree in Mathematics / Quantitative Economics / Econometrics / Statistics / Computer Sciences / Finance; 

• At least 2 years working experience on Data Science; 

• Strong foundation in Statistics, Causal Inference, and Experimental Design – A/B testing, Hypothesis Testing, Power Analysis; 

• Proven experience with SQL (Window functions, CTEs, joins) and Python; 

• Expertise in Machine Learning, Time Series Analysis; 

• Ability to work independently and decompose complex problems. 
 

Preferred 

• Experience with Airflow, Docker, or Kubernetes for Data Orchestration; 

• Practical experience with Amazon SageMaker: training, deploying, and monitoring ML models in a production environment; 

• Knowledge of Reporting and Business Intelligence Software (Power BI, Tableau, Looker); 

• Ability to design and deliver packaged analytical/ML solutions. 
 

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.

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.

Our team is seeking a Data Solution Architect to further strengthen our capabilities.

Responsibilities

• Work closely with business in identifying solution requirements and key case-studies/scenarios to architect Data solution
• Participate in Data and Big Data initiatives on a company level
• Design Data analytics solutions by utilizing the Data technology stack with Big Data techniques
• Conduct solution architecture review/audit, calculate and present ROI
• Create and present solution architecture documents with deep technical details to customer and implementation teams
• Participate in specific “pre-sale” activities related to available experience and practice in order to prepare technical proposals on customer requests
• Lead implementation of the solutions from establishing project requirements and goals to solution "go-live"
• Maintain a strong understanding of technical solutions and architecture design trends and best practices, stay on cutting edge of Data technologies
• Constantly grow expertise by gathering and monitoring available EPAM project experience and ongoing projects with clients from different business domains to drive further EPAM business in Data field
• Create and follow personal education plan in the technology stack and solution architecture
• Share experience, knowledge and vision with EPAM, colleagues and customer teams. Participate in seminars, meet ups, mentoring, training programs. Prepare white papers and presentations

Requirements

• Strong ‘hands-on’ experience as a Data Architect with design/development background with Java, Scala, or Python
• Architecture experience and practice in Data Management, Data Storage, Data Visualization, Disaster Recovery, Integration, Operation and Security
• Experience with building traditional Cloud Data Warehouses, Data Lakes
• Knowledge of high load and IoT Data Platform architectures and infrastructures
• Wide experience in analysis, design, implementation, deployment as well as troubleshooting and rebuilding distributed Linux based platforms and Big Data solutions on premises and in Cloud
• Strong Cloud experience in at least one of the Cloud providers (AWS, Azure, GCP)
• Solid experience in continuous delivery tools and technologies
• Broad experience with Containers and Resource Management systems: Docker, Mesos, Kubernetes/OpenShift, Yarn
• Able to deliver Data Analytics projects and architecture guidelines
• Strong in research, comparison, and selection of tools / technologies / approaches to be used
• Practical experience in performance tuning and optimization, bottleneck problem analysis
• Strong communication skills, experienced in team coordination skills and solution implementation supervision
• Good in Agile development methodology, Scrum in particular
• Solid skills in business analysis, network/stack architecture, troubleshooting, support
• Fluent English

🚀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:

• Paid vacation and sick leave
• We provide the equipment you need to work comfortably (if required)
• We cover 50% of courses if you want to grow professionally
• Regular salary reviews based on your performance
• Competitive salary, depending on your experience
• Always on-time payments — no delays
• A normal, supportive team without unnecessary bureaucracy

Welcome to Drawer.AI!

- What we do: AI-driven workflows that let estimators pull quantities and costs from complex PDF drawing sets.  
- Stage: Seed-funded, revenue-generating, aiming for a Series A in 2026.  
- Why join now: Real customers are pushing our roadmap faster than our current front-end team can build.

We are looking for an AI/ML Engineer who will work at the intersection of LLMs, classical algorithms, and applied machine learning, focusing on designing and improving intelligent systems that solve complex real-world problems.

Key Responsibilities:

• Research and solve complex AI challenges.
• Improve existing algorithms using ML and LLMs.
• Design and build new AI solutions.
• Collaborate with the engineering team to bring ideas into production.
   

Requirements:

• 4+ years of experience in AI/ML.
• Commercial experience with Computer Vision and classical Machine Learning (at least 1 year).
• Strong knowledge of Machine Learning and Deep Learning.
• Experience with PyTorch, TensorFlow, or similar.
• Strong algorithmic and problem-solving skills.
• Experience deploying ML models to production.
• Experience with LLMs and modern AI frameworks.

Nice to have:

• AWS experience.
• LangChain.
• FastAPI or Django.
• Experience with PDF/SVG processing.
• Experience with Graph Neural Networks (GNNs).
• AI research, Kaggle, hackathons, or publications.

Join our stellar team at Drawer.AI, where excellence meets opportunity! Experience the exceptional; enjoy a competitive salary and career advancement and the outstanding work environment we take pride in. Elevate your professional journey with us – because we believe in creating success together. Build a better future with us!

We are looking for a Senior Data Scientist to lead the design and implementation of next-generation AI solutions for Lifecycle Marketing.

As a senior member of our team, you will shape methodology, guide product direction under uncertainty, and represent us in high-stakes conversations with customers. You’ll combine deep expertise in causal inference, reinforcement learning, and experimentation with strong communication skills to drive impact across the company and with our clients.


Responsibilities:
 

• Own the design and methodology of data science solutions for lifecycle marketing, with emphasis on causal inference, uplift modeling, and reinforcement learning.
• Lead the development of experimentation frameworks (A/B/n tests, variance reduction, policy evaluation).
• Research, prototype, and productionize multi-armed bandits and contextual RL approaches for personalization and timing optimization.
• Take a strong, opinionated role in brainstorming sessions, guiding decision-making in areas of high uncertainty.
• Represent us in front of customers, translating complex modeling decisions into clear business value.
• Mentor junior and mid-level team members, raising the technical bar across the team.
• Collaborate closely with product managers, engineers, and analysts to align data science strategy with business goals.
  
   

Requirements:
 

• M.Sc. or Ph.D. in Computer Science, Statistics, Mathematics, or related field.
• 6+ years of hands-on data science experience, including direct work with lifecycle marketing, personalization, or customer analytics.
• Advanced expertise in statistics, causal inference, and experimental design.
• Proven track record of implementing reinforcement learning / multi-armed bandit models in production.
• Strong software engineering skills in Python, SQL, and ML frameworks
• Exceptional communication skills — able to influence internal teams and present to executive-level customers.
• Entrepreneurial mindset: thrives in uncertainty, challenges assumptions, and pushes for impactful solutions.

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. 

About the Role
We are seeking an AI & Data Science Consultant to drive the discovery, strategy, and end-to-end delivery of advanced AI initiatives for EPAM’s global clients. This role is a unique hybrid, blending strategic business consulting with deep practical engineering. You will collaborate directly with client executives and EPAM’s delivery teams to transform complex business challenges into scalable, production-ready AI, Machine Learning, and Agentic solutions.
 

Key Responsibilities

• Consult & Strategize: Lead client-facing discovery sessions and workshops to identify high-value AI opportunities, define MVP scopes, and map out strategic delivery roadmaps.
• Design & Architect: Shape scalable AI products incorporating Generative AI, RAG, multi-agent frameworks, semantic layers, and advanced analytics.
• Bridge the Collaboration: Serve as the central link connecting business stakeholders, data scientists, ML engineers, DevOps, and product teams.
• Drive Delivery & MLOps: Apply a strong engineering mindset to ensure solutions are secure, production-ready, and smoothly integrated into cloud infrastructures.
• Support Business Growth: Partner on pre-sales activities, solution estimation, and proposal development while contributing to EPAM’s internal AI and MLOps offerings.
   

Requirements

• Professional Experience: 3+ years of hands-on experience in Data Science, ML, or Applied AI, with a proven track record of bringing models from concept to production.
• Consulting & Communication: Strong active listening, presentation, and storytelling skills; ability to confidently translate complex AI concepts for C-level stakeholders.
• AI & Agentic Tech Stack: Experience with RAG, LLM applications, multi-agent orchestration, and tools like LangChain, LangGraph, CrewAI, LlamaIndex, or Vector DBs.
• Data & Architecture: Understanding of Semantic Layers, metadata, knowledge graphs, and data platforms (such as Databricks or Snowflake).
• Engineering & MLOps: Familiarity with the production model lifecycle, MLOps/LLMOps pipelines, and cloud platform integrations.
   

Nice to Have

• Experience mentoring cross-functional AI teams or driving enterprise-scale GenAI adoption.
• Practical knowledge of cloud infrastructure and DevOps tooling.
• Insights into AI governance and ethical AI frameworks.

Quantum is a global technology partner delivering high-end software products that address real-world problems. 

We advance emerging technologies for outside-the-box solutions. We focus on Machine Learning, Computer Vision, Deep Learning, GIS, MLOps, Blockchain, and more.

Here at Quantum, we are dedicated to creating state-of-the-art solutions that effectively address the pressing issues faced by businesses and the world. To date, our team of exceptional people has already helped many organisations globally attain technological leadership.

We constantly discover new ways to solve never-ending business challenges by adopting new technologies, even when there isn't yet a best practice. If you share our passion for problem-solving and making an impact, join us and enjoy getting to know our wealth of experience!

About the position

Quantum is expanding its team in Central Europe and is looking for talented Data Science Engineers to join us. Our projects span multiple domains, including data analysis, fintech, computer vision, image processing, edge-device solutions, Large Language Models (LLMs), and classical Machine Learning applications. We build custom Data Science solutions for clients across various industries, combining strong engineering practices with a research-driven mindset.

Joining Quantum means becoming part of a team that values solid technical fundamentals, continuous learning, and practical problem-solving. You'll collaborate on impactful products, work with cutting-edge technologies, and contribute to projects that turn complex ideas into real-world solutions.

Must have skills:

• 3+ years of commercial DS/ML experience
• Solid foundation in linear algebra, statistics, and probability
• Strong Python, including NumPy, Pandas, and scikit-learn
• Hands-on experience with PyTorch (or TensorFlow)
• Deep experience in at least one of: 
  • Computer Vision (modern DL: detection, segmentation, classification - ViT, YOLO, SAM); 
  • LLMs / NLP (fine-tuning, RAG, prompt engineering, embeddings, evaluation); 
  • Classical ML at scale (tabular, time series, recommender systems)
• Working knowledge of Transformers and attention; familiarity with CNNs
• Experience deploying models to production (Docker, REST/gRPC serving, basic monitoring)
• Experience with at least one cloud platform (AWS, GCP, or Azure)
• Clean code and Git workflow
• Experience with SQL
• Upper-Intermediate English (B2+) - you'll communicate directly with clients

Nice to have skills:

• Cross-domain exposure: if your main strength is CV, some LLM exposure is a plus (and vice versa)
• Edge deployment: TensorRT, OpenVINO, ONNX, quantisation, model distillation, mobile/embedded inference
• LLM tooling: LangChain/LlamaIndex, vector databases (pgvector, Pinecone, Weaviate, Qdrant), LoRA/QLoRA fine-tuning, agentic frameworks
• MLOps & model monitoring: MLflow, DVC, Weights & Biases, CI/CD for ML
• Classical CV with OpenCV (still relevant for hybrid pipelines and edge work)
• Ability to read recent papers and implement methods in new domains
• Experience working with external clients
• Participation in Kaggle competitions

Your tasks will include:

• Own full-cycle DS projects: scoping with the client, data prep, modelling, evaluation, deployment
• Build solutions across CV, LLM/NLP, and classical ML depending on the project pipeline
• Deploy models to production - cloud, edge, or hybrid, depending on client needs
• Read recent research and adapt methods to client problems
• Communicate technical decisions and trade-offs directly to clients

We offer:

• Delivering high-end software projects that address real-world problems
• Surrounding experts who are ready to move forward professionally
• Professional growth plan and team leader support
• Taking ownership of R&D and socially significant projects
• Participation in worldwide tech conferences and competitions
• Taking part in regular educational activities
• Being a part of a multicultural company with a fun and lighthearted atmosphere
• Working from anywhere with flexible working hours
• Paid vacation and sick leave days

Join Quantum and take a step toward your data-driven future.

Продуктова компанія, що створює інноваційні AI-рішення для будівельної індустрії, шукає Data Tagger для роботи з розміткою даних. Ця роль буде ключовою для тренування моделей машинного навчання, що автоматизують аналіз архітектурних планів.
 

Платформа компанії зчитує креслення кухонь та ванних кімнат, визначає на них потрібні елементи (шафи, стільниці тощо) та генерує готові кошториси на основі каталогів матеріалів. Ціль — прискорити процес комерційної пропозиції з кількох днів до кількох хвилин.
 

Обов’язки:

• Тегування текстів та зображень згідно з інструкціями проєкту
• Розмітка об’єктів на кресленнях: сегментація, виділення ключових точок тощо
• Підготовка якісних навчальних даних для потреб AI-моделей
• Взаємодія з аналітиками та ML-командою для вдосконалення процесів розмітки

Вимоги:

• Досвід роботи з анотацією/тегуванням даних (зображення або тексти)
• Розуміння принципів Computer Vision буде перевагою
• Уважність до деталей, здатність працювати з великою кількістю даних
• Середній або вищий рівень володіння англійською (для розуміння інструкцій та комунікації)
   

Умови:

• Формат: повністю віддалено
• Відпустка: 10 днів + державні свята
• Лікарняні: оплачувані
• Медстрахування

ПРО ПОЗИЦІЮ

Вимоги:

• Досвід DS/MLE від 1 року
• Математичний бекграунд: вища математична освіта (КПІ ІПСА, МехМат Шевченко тощо), бажано олімпіадний досвід
• Глибокі знання алгоритмів і структур даних, глибоке розуміння ООП, глибокі теоретичні та практичні знання машинного та глибокого навчання
• Практичний досвід вирішення NLP та Tabular Data ML задач
• Високий рівень володіння Python, включаючи досвід роботи з бібліотеками машинного навчання і володіння принаймні одним із фреймворків глибокого навчання (Tensorflow, Keras, PyTorch)
• Вміння писати чистий, підтримуваний та ефективний код, готовий до production
• Вміння трансформувати бізнес-вимоги у технічні завдання для ML-рішень
• Досвід feature engineering
• Досвід роботи з Git та Docker
• Досвід роботи з базами даних (SQL/NoSQL) для отримання та підготовки даних для моделей

Буде плюсом:

• Досвід вирішення CV завдань
• Досвід успішних виступів на хакатонах/Kaggle
• Досвід роботи з Airflow на рівні запуску та моніторингу завдань
• Розуміння принципів паралелізму в Python та досвід роботи з відповідними бібліотеками

Приклади задач:

• Підвищення точності поточних CV, NLP та інших моделей
• Розробка нових моделей з нуля: від R&D та feature engineering до валідації
• Аналіз великих обсягів даних для пошуку інсайтів та формулювання гіпотез для моделювання