Jobs Data Scientist

We are seeking a Data Scientist with a strong foundation in physics, control theory, and mathematical modeling to join our team working on cutting-edge autonomous systems. The ideal candidate combines analytical rigor with practical experience in modeling, simulation, and algorithm development for autonomous platforms.

Levels: Middle and Senior (responsibilities and scope will be adjusted accordingly).

Key Responsibilities

• Develop and validate mathematical models for autonomous systems and dynamic environments.
• Apply data-driven approaches for system identification, optimization, and predictive control.
• Analyze large datasets from sensors and simulations to extract insights and improve system performance.
• Design and implement algorithms for control, navigation, and decision-making.
• Collaborate with cross-functional teams to integrate models into real-world autonomous platforms.

Required Qualifications

• 3+ years in R&D or applied data science/software development.
• Strong background in mathematical modeling, system identification, and control theory.
• Proficiency in Matlab/Simulink for modeling and simulation.
• Experience in signal processing and data analysis.
• Programming skills in Python and C++.
• Ability to quickly research and apply recent trends in control theory, autonomous systems, and data-driven modeling.
• Relevant work experience or education in STEM field

Nice to Have

• Knowledge of aerodynamics fundamentals.
• Experience with Machine Learning (e.g., reinforcement learning, predictive modeling).
• Familiarity with simulation tools such as Gazebo, AirSim.
• Hands-on experience with SITL/HITL testing.
• Exposure to flight control stacks like PX4, Betaflight, ArduPilot.

About us
 

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. 

Since its inception in 1997, Fozzy Group has focused on making innovative business improvements, creating

new opportunities for the market and further developing the industry as a whole.
 

Job Description:
 

The Foodtech team is looking for a Data Scientist to develop the Operational Analytics function for a fast[1]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

Must Have
 

• 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).
 

Preferred
 

• 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

Drones kill and injure over 5,000 Ukrainians every month. Munin exists to stop that. We are building hand-launched micro-missiles to intercept small drones at short range. Small enough to carry three in a vest and cheap enough for wide use, this will be the smallest, most cost-effective guided missile ever deployed.

We work from a test site in Oslo, UK and in Kyiv, directly with Ukrainian brigades. Our goal is to turn the tide of war and give NATO a proven soldier-level counter-drone solution.

We are hiring a Computer Vision Engineer to join our founding team.

You will lead the design and development of the missile’s electronics system, from ideas to field tests and production. You will own the architecture, guide component selection, and drive integration while staying hands-on through bring-up, testing, and iteration.

Responsibilities

• Design system architecture and lead electronics development

• Select and integrate sensors, processors, power systems, and actuators

• Design PCBs

• Develop test plans for live environments and build test rigs

• Collaborate with mechanical and software engineers to ensure reliable, testable systems

Requirements

• Experience with embedded or mechatronic system design

• Strong PCB design and debugging skills

• Skilled in sensor integration and power management

• Able to balance high-level design with hands-on execution

• Willing to work from Oslo, UK or Kyiv

Bonus

• Aerospace or computer vision experience
• Knowledge of control systems or autonomy
• FPGA programming experience
   

Why Munin

• Impact: Build tech that saves lives

• Ownership: Lead seeker development for a new missile category

• Team: Join a mission-driven group from Stanford, Imperial, Rheinmetall, and special forces

• Benefits: Salary, 0–3% equity, housing, travel, and training support

This is not a research role. We are flying real hardware fast. If you are ready to build something that matters, apply.

WE ARE

SoftServe is a global digital solutions company, headquartered in Austin, Texas, and founded in 1993.

With 2,000+ active projects across the USA, Europe, APAC, and LATAM, we deliver meaningful outcomes through bold thinking and deep expertise. Our people create impactful solutions, drive innovation, and genuinely enjoy what they do.

The AI and Data Science Center of Excellence (CoE) is SoftServe’s premier AI/ML hub, primarily based in Europe. With 130+ experts—including data scientists, research analysts, MLOps engineers, ML and LLM architects — we cover the full AI lifecycle, from problem framing to deployment.

In 2024, we delivered 150+ AI projects, including over 100 focused on Generative AI, combining scale with measurable impact.

We are a 2024 NVIDIA Service Delivery Partner and maintain strong collaborations with Google Cloud, Amazon, and Microsoft, ensuring our teams always work with cutting-edge tools and technologies.

We also lead Gen AI Lab — our internal innovation engine focused on applied research and cross-functional collaboration in Generative AI.

In 2025, a key area of innovation is Agentic AI — where we design and deploy autonomous, collaborative agent systems capable of addressing complex, real-world challenges at scale for our clients and internally.

IF YOU ARE

• Experienced in Generative AI and natural language processing (NLP), working with large-scale transformer models and generative pre-trained LLMs like GPT-4, Claude, and Gemini
• Knowledgeable about the latest advancements in diffusion models and other generative frameworks for text and image generation
• Adept at applying advanced deep learning techniques to practical use cases
• Well-versed in emerging trends and breakthroughs in machine learning, deep learning, and NLP, with a strong focus on their real-world applications
• Proficient in working with state-of-the-art pre-trained language models like GPT-4 and BERT, including fine-tuning for specialized tasks
• Aware of the software development lifecycle for AI projects and the operationalization of machine learning models
• Experienced in deploying AI solutions on major cloud platforms
• Hands-on with Python and deep learning frameworks such as TensorFlow or PyTorch
• Skilled in interpersonal communication, analytical reasoning, and complex problem-solving
• Capable of translating technical concepts into clear, concise insights that non-technical audiences can easily grasp
• Proficient in business communication in English at an upper-intermediate level
   

AND YOU WANT TO

• Work with the full stack of data analysis, deep learning, and machine learning model pipeline that includes deep analysis of customer data, modeling, and deployment in production
• Choose relevant computational tools for study, experiment, or trial research objectives
• Drive the development of innovative solutions for language generation, text synthesis, and creative content generation using the latest state-of-the-art techniques
• Develop and implement advanced Generative AI solutions such as intelligent assistants, Retrieval-Augmented Generation (RAG) systems, and other innovative applications
• Produce clear, concise, well-organized, and error-free computer programs with the appropriate technological stack
• Present results directly to stakeholders and gather business requirements
• Develop expertise in state-of-the-art Generative AI techniques and methodologies
• Grow your skill set within a dynamic and supportive environment
• Work with Big Data solutions and advanced data tools in cloud platforms
• Build and operationalize ML models, including data manipulation, experiment design, developing analysis plans, and generating insights
• Lead teams of data scientists and software engineers to successful project execution

TOGETHER WE WILL

• Be part of a team that's shaping the future of AI and data science through innovation and shared growth.
• Advance the frontier of Agentic AI by shaping intelligent multi-agent ecosystems that drive autonomy, scalability, and measurable business value.
• Have access to world-class training, cutting-edge research, and collaborate with top industry partners.
• Maintain a synergy of Data Scientists, DevOps team, and ML Engineers to build infrastructure, set up processes, productize machine learning pipelines, and integrate them into existing business environments
• Communicate with the world-leading companies from our logos portfolio
• Enjoy the opportunity to work with the latest modern tools and technologies on various projects
• Participate in international events and get certifications in cutting-edge technologies
• Have access to powerful educational and mentorship programs
• Revolutionize the software industry and drive innovation in adaptive self-learning technologies by leveraging multidisciplinary expertise

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

Join us at Provectus to be a part of a team that is dedicated to building cutting-edge technology solutions that have a positive impact on society. Our company specializes in AI and ML technologies, cloud services, and data engineering, and we take pride in our ability to innovate and push the boundaries of what's possible.

As an ML Engineer, you’ll be provided with all opportunities for development and growth.

Let's work together to build a better future for everyone!
 

Requirements:

• Comfortable with standard ML algorithms and underlying math.
• Strong hands-on experience with LLMs in production, RAG architecture, and agentic systems
• AWS Bedrock experience strongly preferred
• Practical experience with solving classification and regression tasks in general, feature engineering.
• Practical experience with ML models in production.
• Practical experience with one or more use cases from the following: NLP, LLMs, and Recommendation engines.
• Solid software engineering skills (i.e., ability to produce well-structured modules, not only notebook scripts).
• Python expertise, Docker.
• English level - strong Intermediate.
• Excellent communication and problem-solving skills.
   

Will be a plus:

• Practical experience with cloud platforms (AWS stack is preferred, e.g. Amazon SageMaker, ECR, EMR, S3, AWS Lambda).
• Practical experience with deep learning models.
• Experience with taxonomies or ontologies.
• Practical experience with machine learning pipelines to orchestrate complicated workflows.
• Practical experience with Spark/Dask, Great Expectations.
   

Responsibilities:

• Create ML models from scratch or improve existing models. 
• Collaborate with the engineering team, data scientists, and product managers on production models.
• Develop experimentation roadmap. 
• Set up a reproducible experimentation environment and maintain experimentation pipelines.
• Monitor and maintain ML models in production to ensure optimal performance.
• Write clear and comprehensive documentation for ML models, processes, and pipelines.
• Stay updated with the latest developments in ML and AI and propose innovative solutions.

Join us at Provectus to be a part of a team that is dedicated to building cutting-edge technology solutions that have a positive impact on society. Our company specializes in AI and ML technologies, cloud services, and data engineering, and we take pride in our ability to innovate and push the boundaries of what's possible.

As an ML Engineer, you’ll be provided with all opportunities for development and growth.

Let's work together to build a better future for everyone!
 

Requirements:

• Comfortable with standard ML algorithms and underlying math.
• Strong hands-on experience with LLMs in production, RAG architecture, and agentic systems
• AWS Bedrock experience strongly preferred
• Practical experience with solving classification and regression tasks in general, feature engineering.
• Practical experience with ML models in production.
• Practical experience with one or more use cases from the following: NLP, LLMs, and Recommendation engines.
• Solid software engineering skills (i.e., ability to produce well-structured modules, not only notebook scripts).
• Python expertise, Docker.
• English level - strong Intermediate.
• Excellent communication and problem-solving skills.
   

Will be a plus:

• Practical experience with cloud platforms (AWS stack is preferred, e.g. Amazon SageMaker, ECR, EMR, S3, AWS Lambda).
• Practical experience with deep learning models.
• Experience with taxonomies or ontologies.
• Practical experience with machine learning pipelines to orchestrate complicated workflows.
• Practical experience with Spark/Dask, Great Expectations.
   

Responsibilities:

• Create ML models from scratch or improve existing models. 
• Collaborate with the engineering team, data scientists, and product managers on production models.
• Develop experimentation roadmap. 
• Set up a reproducible experimentation environment and maintain experimentation pipelines.
• Monitor and maintain ML models in production to ensure optimal performance.
• Write clear and comprehensive documentation for ML models, processes, and pipelines.
• Stay updated with the latest developments in ML and AI and propose innovative solutions.

Join us at Provectus to be a part of a team that is dedicated to building cutting-edge technology solutions that have a positive impact on society. Our company specializes in AI and ML technologies, cloud services, and data engineering, and we take pride in our ability to innovate and push the boundaries of what's possible.

As an ML Engineer, you’ll be provided with all opportunities for development and growth.

Let's work together to build a better future for everyone!
 

Requirements:

• Comfortable with standard ML algorithms and underlying math.
• Strong hands-on experience with LLMs in production, RAG architecture, and agentic systems
• AWS Bedrock experience strongly preferred
• Practical experience with solving classification and regression tasks in general, feature engineering.
• Practical experience with ML models in production.
• Practical experience with one or more use cases from the following: NLP, LLMs, and Recommendation engines.
• Solid software engineering skills (i.e., ability to produce well-structured modules, not only notebook scripts).
• Python expertise, Docker.
• English level - strong Intermediate.
• Excellent communication and problem-solving skills.
   

Will be a plus:

• Practical experience with cloud platforms (AWS stack is preferred, e.g. Amazon SageMaker, ECR, EMR, S3, AWS Lambda).
• Practical experience with deep learning models.
• Experience with taxonomies or ontologies.
• Practical experience with machine learning pipelines to orchestrate complicated workflows.
• Practical experience with Spark/Dask, Great Expectations.
   

Responsibilities:

• Create ML models from scratch or improve existing models. 
• Collaborate with the engineering team, data scientists, and product managers on production models.
• Develop experimentation roadmap. 
• Set up a reproducible experimentation environment and maintain experimentation pipelines.
• Monitor and maintain ML models in production to ensure optimal performance.
• Write clear and comprehensive documentation for ML models, processes, and pipelines.
• Stay updated with the latest developments in ML and AI and propose innovative solutions.

We are seeking a highly skilled Machine Learning (ML) Tech Lead with a strong background in Large Language Models (LLMs) and AWS Cloud services. The ideal candidate will oversee the development and deployment of cutting-edge AI solutions while managing a team of 5-10 engineers. This leadership role demands hands-on technical expertise, strategic planning, and team management capabilities to deliver innovative products at scale.
 

Responsibilities:

• Leadership & Management
  -Lead and manage a team of 5-10 engineers, providing mentorship and fostering a collaborative team environment;
  -Drive the roadmap for machine learning projects aligned with business goals;
  -Coordinate cross-functional efforts with product, data, and engineering teams to ensure seamless delivery.
• Machine Learning & LLM Expertise
  -Design, develop, and fine-tune LLMs and other machine learning models to solve business problems;
  -Evaluate and implement state-of-the-art LLM techniques for NLP tasks such as text generation, summarization, and entity extraction;
  -Stay ahead of advancements in LLMs and apply emerging technologies;
  -Expertise in multiple main fields of ML: NLP, Computer Vision, RL, deep learning and classical ML.
• AWS Cloud Expertise
  -Architect and manage scalable ML solutions using AWS services (e.g., SageMaker, Lambda, Bedrock, S3, ECS, ECR, etc.);
  -Optimize models and data pipelines for performance, scalability, and cost-efficiency in AWS;
  -Ensure best practices in security, monitoring, and compliance within the cloud infrastructure.
• Technical Execution
  -Oversee the entire ML lifecycle, from research and experimentation to production and maintenance;
  -Implement MLOps and LLMOps practices to streamline model deployment and CI/CD workflows;
  -Debug, troubleshoot, and optimize production ML models for performance.
• Team Development & Communication
  -Conduct regular code reviews and ensure engineering standards are upheld;
  -Facilitate professional growth and learning for the team through continuous feedback and guidance;
  -Communicate progress, challenges, and solutions to stakeholders and senior leadership.
  
  Qualifications:
• Proven experience with LLMs and NLP frameworks (e.g., Hugging Face, OpenAI, or Anthropic models);
• Strong expertise in AWS Cloud Services;
• Strong experience in ML/AI, including at least 2 years in a leadership role;
• Hands-on experience with Python, TensorFlow/PyTorch, and model optimization;
• Familiarity with MLOps tools and best practices;
• Excellent problem-solving and decision-making abilities;
• Strong communication skills and the ability to lead cross-functional teams;
• Passion for mentoring and developing engineers.

We are seeking a highly skilled Machine Learning (ML) Tech Lead with a strong background in Large Language Models (LLMs) and AWS Cloud services. The ideal candidate will oversee the development and deployment of cutting-edge AI solutions while managing a team of 5-10 engineers. This leadership role demands hands-on technical expertise, strategic planning, and team management capabilities to deliver innovative products at scale.
 

Responsibilities:

• Leadership & Management
  -Lead and manage a team of 5-10 engineers, providing mentorship and fostering a collaborative team environment;
  -Drive the roadmap for machine learning projects aligned with business goals;
  -Coordinate cross-functional efforts with product, data, and engineering teams to ensure seamless delivery.
• Machine Learning & LLM Expertise
  -Design, develop, and fine-tune LLMs and other machine learning models to solve business problems;
  -Evaluate and implement state-of-the-art LLM techniques for NLP tasks such as text generation, summarization, and entity extraction;
  -Stay ahead of advancements in LLMs and apply emerging technologies;
  -Expertise in multiple main fields of ML: NLP, Computer Vision, RL, deep learning and classical ML.
• AWS Cloud Expertise
  -Architect and manage scalable ML solutions using AWS services (e.g., SageMaker, Lambda, Bedrock, S3, ECS, ECR, etc.);
  -Optimize models and data pipelines for performance, scalability, and cost-efficiency in AWS;
  -Ensure best practices in security, monitoring, and compliance within the cloud infrastructure.
• Technical Execution
  -Oversee the entire ML lifecycle, from research and experimentation to production and maintenance;
  -Implement MLOps and LLMOps practices to streamline model deployment and CI/CD workflows;
  -Debug, troubleshoot, and optimize production ML models for performance.
• Team Development & Communication
  -Conduct regular code reviews and ensure engineering standards are upheld;
  -Facilitate professional growth and learning for the team through continuous feedback and guidance;
  -Communicate progress, challenges, and solutions to stakeholders and senior leadership.
  
  Qualifications:
• Proven experience with LLMs and NLP frameworks (e.g., Hugging Face, OpenAI, or Anthropic models);
• Strong expertise in AWS Cloud Services;
• Strong experience in ML/AI, including at least 2 years in a leadership role;
• Hands-on experience with Python, TensorFlow/PyTorch, and model optimization;
• Familiarity with MLOps tools and best practices;
• Excellent problem-solving and decision-making abilities;
• Strong communication skills and the ability to lead cross-functional teams;
• Passion for mentoring and developing engineers.

Join us at Provectus to be a part of a team that is dedicated to building cutting-edge technology solutions that have a positive impact on society. Our company specializes in AI and ML technologies, cloud services, and data engineering, and we take pride in our ability to innovate and push the boundaries of what's possible.

As an ML Engineer, you’ll be provided with all opportunities for development and growth.

Let's work together to build a better future for everyone!
 

Requirements:

• Comfortable with standard ML algorithms and underlying math.
• Strong hands-on experience with LLMs in production, RAG architecture, and agentic systems
• AWS Bedrock experience strongly preferred
• Practical experience with solving classification and regression tasks in general, feature engineering.
• Practical experience with ML models in production.
• Practical experience with one or more use cases from the following: NLP, LLMs, and Recommendation engines.
• Solid software engineering skills (i.e., ability to produce well-structured modules, not only notebook scripts).
• Python expertise, Docker.
• English level - strong Intermediate.
• Excellent communication and problem-solving skills.
   

Will be a plus:

• Practical experience with cloud platforms (AWS stack is preferred, e.g. Amazon SageMaker, ECR, EMR, S3, AWS Lambda).
• Practical experience with deep learning models.
• Experience with taxonomies or ontologies.
• Practical experience with machine learning pipelines to orchestrate complicated workflows.
• Practical experience with Spark/Dask, Great Expectations.
   

Responsibilities:

• Create ML models from scratch or improve existing models. 
• Collaborate with the engineering team, data scientists, and product managers on production models.
• Develop experimentation roadmap. 
• Set up a reproducible experimentation environment and maintain experimentation pipelines.
• Monitor and maintain ML models in production to ensure optimal performance.
• Write clear and comprehensive documentation for ML models, processes, and pipelines.
• Stay updated with the latest developments in ML and AI and propose innovative solutions.

We are looking for a Computer Vision / C++ Engineer to join our team and work on real-world computer vision systems.

Responsibilities:
 

- Develop and maintain computer vision solutions in C++;

- Work with geometric computer vision (projective and epipolar geometry);

- Implement classical CV algorithms (demosaicing, tracking, optical flow, color calibration, image enhancement);

- Fine-tune existing deep learning models (e.g. YOLO) and train custom models using PyTorch;

- Deploy DL models in C++ using inference runtimes (ONNX Runtime, OpenCV DNN or similar);

- Implement model output decoding and post-processing;

- Develop camera-related software;

- Write efficient multi-threaded code.

Requirements:
 

- Strong C++ skills;

- Solid background in computer vision;

- Practical experience with deep learning for CV and PyTorch;

- Experience with deploying models to C++ applications and inference runtimes;

-Experience with multi-threaded programming and camera systems.

Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with the organization of the first Data Science UA conference, setting the foundation for our growth. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.

About the role:
We are looking for a Computer Vision / Machine Learning Engineer to develop offline CV models for industrial visual inspection.

Your main task will be to design, train, and evaluate models on inspection data in order to:

• Improve discrimination between good vs. defect samples
• Provide insights into key defect categories (e.g., terminal electrode irregularities, surface chipping)
• Significantly reduce false-positive rates, optimizing for either precision, or recall
• Prepare the solution for future deployment, scaling, and maintenance
•  

Key Responsibilities:
Data Analysis & Preparation
- Conduct dataset audits, including class balance checks and sample quality reviews
- Identify low-frequency defect classes and outliers
- Design and implement augmentation strategies for rare defects and edge cases
Model Development & Evaluation
- Train deep-learning models on inspection images for defect detection
- Use modern computer vision / deep learning frameworks (e.g., PyTorch, TensorFlow)
- Evaluate models using confusion matrices, ROC curves, precision–recall curves, F1 scores and other relevant metrics
- Analyze false positives/false negatives and propose thresholds or model improvements
Reporting & Communication
- Prepare clear offline performance reports and model evaluation summaries
- Explain classifier decisions, limitations, and reliability in simple, non-technical language when needed
- Provide recommendations for scalable deployment in later phases (e.g., edge / on-prem inference, integration patterns)

Candidate Requirements:
Must-have:
- 1-2 years of hands-on experience with computer vision and deep learning (classification, detection, or segmentation)
- Strong proficiency in Python and at least one major DL framework (PyTorch or TensorFlow/Keras)
- Solid understanding of:

• Image preprocessing and augmentation techniques
• Classification metrics: accuracy, precision, recall, F1, confusion matrix, ROC, PR curves
• Handling imbalanced datasets and low-frequency classes

- Experience training and evaluating offline models on real production or near-production datasets
- Ability to structure and document experiments, compare baselines, and justify design decisions
- Strong analytical and problem-solving skills; attention to detail in data quality and labelling
- Good communication skills in English (written and spoken) to interact with internal and client stakeholders

Nice-to-have:
- Experience with industrial / manufacturing computer vision (AOI, quality inspection, defect detection, etc.)
- Familiarity with ML Ops/deployment concepts (ONNX, TensorRT, Docker, REST APIs, edge devices)
- Experience working with time-critical or high-throughput inspection systems
- Background in electronics, semiconductors, or similar domains is an advantage
- Experience preparing client-facing reports and presenting technical results to non-ML audiences

We offer:
- Free English classes with a native speaker and external courses compensation;
- PE support by professional accountants;
- 40 days of PTO;
- Medical insurance;
- Team-building events, conferences, meetups, and other activities;
- There are many other benefits you’ll find out at the interview.

Hello!

We are E-Com, a team of Foodtech and Ukrainian product lovers.

And we also break stereotypes that retail is only about tomatoes. Believe me, the technical part of our projects provides a whole field for creativity and brainstorming.

What we are currently working on:

• we are upgrading the existing delivery of a wide range of products from Silpo stores;
• we are developing super-fast delivery of products and dishes under the new LOKO brand.

We are developing a next-generation Decision Support Platform that connects demand  planning, operational orchestration, and in-store execution optimization into one unified Analytics and  Machine Learning Ecosystem. 

The project focuses on three major streams: 

•  Demand & Forecasting Intelligence: building short-term demand forecasting models, generating  granular demand signals for operational planning, identifying anomalies, and supporting commercial  decision logic across virtual warehouse clusters. 

•  Operational Orchestration & Task Optimization: designing predictive models for workload  estimation, task duration (ETA), and prioritization. Developing algorithms that automatically map  operational needs into structured tasks and optimize their sequencing and allocation across teams. 

•  In-Store Execution & Routing Optimization: developing models that optimize picker movement,  predict in-store congestion, and recommend optimal routes and execution flows. Integrating store  layout geometry, product characteristics, and operational constraints to enhance dark-store  efficiency. 

You will join a cross-functional team to design and implement data-driven decision module that directly  influence commercial and operational decisions. 

Responsibilities:

•  develop and maintain ML models for forecasting short-term demand signals and detecting anomalies  across virtual warehouse clusters;

•  build predictive models to estimate task workload, execution times (ETA), and expected operational  performance;

•  design algorithms to optimize task distribution, sequencing, and prioritization across operational  teams;

•  develop routing and path-optimization models to improve picker movement efficiency within dark  stores; 

•  construct data-driven decision modules that integrate commercial rules, operational constraints, and  geometric layouts;

•  translate business requirements into ML-supported decision flows and automate key parts of  operational logic; 

•  build SQL pipelines and data transformations for commercial, operations, and logistics datasets;

•  work closely with supply chain, dark store operations, category management, and IT to deliver  measurable improvements;

•  conduct A/B testing, validate model impact, and ensure high-quality model monitoring. 

Requirements:

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

•  at least 2 years working experience on Data Science; 

•  strong mathematical background in Linear algebra, Probability, Statistics & Optimization Techniques; 

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

•  expertise in Machine Learning, Time Series Analysis and application of Statistical Concepts  (Hypothesis testing, A/B tests, PCA); 

•  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;
• opportunity to work on flagship projects impacting millions of users;
• flexible remote or office-based work (with backup power and reliable connectivity at SilverBreeze Business Center);
• flexible working schedule;
• medical and Life insurance packages;
• support for GIG contract or private entrepreneurship arrangements;
• discounts at Fozzy Group stores and restaurants;
• psychological support services;
• Caring corporate culture;
• a team where you can implement your ideas, experiment, and feel like you are among friends.

Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with the organization of the first Data Science UA conference, setting the foundation for our growth. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.

About the role:
We are looking for a Computer Vision / Machine Learning Engineer to develop offline CV models for industrial visual inspection.

Your main task will be to design, train, and evaluate models on inspection data in order to:

• Improve discrimination between good vs. defect samples
• Provide insights into key defect categories (e.g., terminal electrode irregularities, surface chipping)
• Significantly reduce false-positive rates, optimizing for either precision, or recall
• Prepare the solution for future deployment, scaling, and maintenance
•  

Key Responsibilities:
Data Analysis & Preparation
- Conduct dataset audits, including class balance checks and sample quality reviews
- Identify low-frequency defect classes and outliers
- Design and implement augmentation strategies for rare defects and edge cases
Model Development & Evaluation
- Train deep-learning models on inspection images for defect detection
- Use modern computer vision / deep learning frameworks (e.g., PyTorch, TensorFlow)
- Evaluate models using confusion matrices, ROC curves, precision–recall curves, F1 scores and other relevant metrics
- Analyze false positives/false negatives and propose thresholds or model improvements
Reporting & Communication
- Prepare clear offline performance reports and model evaluation summaries
- Explain classifier decisions, limitations, and reliability in simple, non-technical language when needed
- Provide recommendations for scalable deployment in later phases (e.g., edge / on-prem inference, integration patterns)

Candidate Requirements:
Must-have:
- 1-2 years of hands-on experience with computer vision and deep learning (classification, detection, or segmentation)
- Strong proficiency in Python and at least one major DL framework (PyTorch or TensorFlow/Keras)
- Solid understanding of:

• Image preprocessing and augmentation techniques
• Classification metrics: accuracy, precision, recall, F1, confusion matrix, ROC, PR curves
• Handling imbalanced datasets and low-frequency classes

- Experience training and evaluating offline models on real production or near-production datasets
- Ability to structure and document experiments, compare baselines, and justify design decisions
- Strong analytical and problem-solving skills; attention to detail in data quality and labelling
- Good communication skills in English (written and spoken) to interact with internal and client stakeholders

Nice-to-have:
- Experience with industrial / manufacturing computer vision (AOI, quality inspection, defect detection, etc.)
- Familiarity with ML Ops/deployment concepts (ONNX, TensorRT, Docker, REST APIs, edge devices)
- Experience working with time-critical or high-throughput inspection systems
- Background in electronics, semiconductors, or similar domains is an advantage
- Experience preparing client-facing reports and presenting technical results to non-ML audiences

We offer:
- Free English classes with a native speaker and external courses compensation;
- PE support by professional accountants;
- 40 days of PTO;
- Medical insurance;
- Team-building events, conferences, meetups, and other activities;
- There are many other benefits you’ll find out at the interview.