Jobs Data Science

As a member of Computer Vision Team, you will contribute to data preparation, research, prototyping, and the deployment of machine learning models and computer vision algorithms in edge and cloud environments. Your responsibilities will also include monitoring and evaluating solution performance in production and adapting the system to rapidly changing operational conditions.
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
• Knowledge of C++

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.

We are looking for a Senior AI/Machine Learning Engineer to join a healthcare project from the Netherlands focused on simplifying preparation for orthopedic surgeries. The product uses artificial intelligence to automatically analyze 3D images of the skeletal system and support surgical planning. In this role, you will focus on continuing and improving the X3D pipeline (X-ray to 3D bone reconstruction) and integrating it into the web application.
 

Requirements:
— 5+ years of commercial experience in Machine Learning/AI engineering.
— Strong experience with PyTorch and deep learning model training, evaluation, and optimization.
— Solid background in medical image analysis (CT, X-ray) and/or 3D computer vision.
— Strong understanding of image processing fundamentals, including registration, segmentation, and data augmentation techniques.
— Familiarity with GAN architectures (Pix2Pix, CycleGAN) and encoder-decoder networks.
— Experience working with medical imaging file formats (NIfTI, DICOM).
— Ability to read and implement techniques from research papers.
— Proficiency in Python and scientific computing libraries (NumPy, SciPy, SimpleITK or similar).
— Self-directed and independent working style with the ability to effectively collaborate under technical guidance.
— Upper-Intermediate or higher level of English (spoken and written).

Nice to have:
— Experience with 3D shape reconstruction or 3D mesh processing.
— Familiarity with DRR generation from CT volumes.
— Experience with Azure ML or similar cloud ML platforms.
— Background in orthopedic or musculoskeletal imaging.
— Experience in a medical device or regulated environment (FDA, CE).

Responsibilities:
— Improve and extend the existing X3D pipeline for X-ray to 3D bone reconstruction.
— Design, train, evaluate, and optimize deep learning models for medical image analysis and 3D reconstruction tasks.
— Work with medical imaging data (CT, X-ray, DICOM, NIfTI) and implement preprocessing, augmentation, segmentation, and registration techniques.
— Research, prototype, and implement new approaches from academic papers and industry best practices.
— Integrate machine learning solutions into the web application and support deployment workflows.
— Collaborate closely with the Head of Engineering to further develop and productionize the existing AI pipeline.
— Maintain code quality through testing, documentation, and continuous improvement of ML infrastructure and development processes.

Interview stages:
— HR interview (30 minutes).
— Technical interview (1 hour).
— Interview with the customer (1 hour).
— Job offer 🎉🎉🎉

We offer:
— Competitive salary.
— Challenging tasks and projects.
— Opportunities for professional development and growth.
— Flexible working hours.
— New hardware.
— Free English lessons.
— Table tennis and yoga classes.
— Availability of generators and Starlinks in the office.
— Small gym in the office.
— Adjustable height tables.
— Regular office fruit delivery and other benefits.

Let’s create value together!

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.

About Logivations

Adidas, DHL, IKEA, Heineken, Mercedes, Swarovski — and 30,000+ other users — work with software built by Logivations. We develop cloud-native solutions using cutting-edge technologies to transform logistics and operations worldwide. Learn more at www.logivations.com.

The Role

We are looking for a detail-oriented Junior Data Annotator to join our AI/ML team in Lviv. Your work will directly contribute to the quality of machine learning models used by leading global companies. No programming background is required — what matters is sharp analytical thinking, precision, and the ability to follow structured guidelines consistently.

This is an excellent entry-level opportunity for someone eager to step into the IT world and grow within an international tech environment.

What You’ll Do

• Annotate and label datasets according to project-specific guidelines
• Review, validate, and correct annotations to ensure quality and consistency
• Identify edge cases and inconsistencies in data
• Collaborate with the data team to resolve ambiguities and refine processes
• Maintain productivity and quality metrics
• Adapt to evolving annotation techniques, tools, and project requirements

What You Bring

• English reading comprehension at B1 level or above
• High school diploma or equivalent or a student
• Basic to intermediate computer proficiency
• Strong attention to detail and analytical mindset
• Ability to work independently, manage time effectively, and meet deadlines
• Comfort with structured, rule-based, and repetitive tasks
• Clear communication skills for cross-functional collaboration

Nice to Have

• Prior experience in data annotation, data review, or a related field
• Ability to identify patterns and edge cases in data

What We Offer

• Decent compensation paid in EUR
• Hybrid schedule (office and remote)
• Direct impact on international AI/ML projects
• Professional growth through training and cross-functional collaboration

How to Apply

Please send your CV in English.

Join us to develop SLAM, Visual-Inertial Odometry (VIO), and state estimation solutions for real-world robotics and navigation systems.

We're looking for an engineer with a strong mathematical background and extensive experience in classical and geometric computer vision. You'll independently design, prototype, and optimize computer vision algorithms, quickly turn research ideas into working solutions, and contribute to technical decisions across the development process.

Responsibilities:

• Design, develop, and optimize classical and geometric computer vision algorithms
• Work with feature tracking, matching, and geometric processing
• Develop and improve SLAM / VIO and state estimation pipeline components
• Integrate and test multi-sensor fusion systems (camera + IMU)
• Design and run experiments, evaluate algorithm performance
• Translate research papers into practical implementations
• Collaborate with cross-functional engineering teams

Required Qualifications:

• 5+ years of professional experience in computer vision, robotics, or a related field
• Deep understanding of linear algebra, statistics, and optimization methods
• Strong hands-on experience with classical and geometric computer vision
• Strong understanding of camera models, feature detection and tracking, geometry, and state estimation
• Proven experience developing SLAM, Visual-Inertial Odometry (VIO), or related localization systems
• Strong C++ development skills and experience with Linux
• Experience designing, implementing, and optimizing production-ready computer vision algorithms
• Ability to independently investigate research papers and implement state-of-the-art methods
• Experience working on R&D projects
• Degree in Computer Science, Mathematics, Physics, Engineering, or related 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
• Robotics or navigation background

We are looking for a Computer Vision Engineer with strong experience in deep learning and production computer vision systems.

The role involves developing and bringing computer vision models into production, covering the full lifecycle from training and evaluation to deployment and system integration.

Responsibilities:

• Develop and improve Computer Vision and Machine Learning models
• Design and implement solutions for object detection, classification, segmentation, and tracking
• Work with large-scale image, video, and telemetry datasets
• Prepare, process, and maintain datasets for model training and evaluation
• Manage model training, validation, and testing workflows
• Optimize models for production deployment and real-time performance
• Integrate ML models into software services, applications, and APIs
• Analyze model performance and identify opportunities for quality improvement
• Participate in the design and architecture of AI components within the product ecosystem
• Collaborate closely with Backend, Infrastructure, Data, and Product Engineering teams
• Contribute to the development of MLOps practices and the end-to-end machine learning lifecycle.

Requirements:

• 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
• Experience working with thermal imaging data (thermal cameras, infrared imagery) would be an advantage
• 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++

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.