Jobs Data Scientist

The CHI Software team is not standing still. We love our job and give it one hundred percent of us! Every new project is a challenge that we face successfully. The only thing that can stop us is… Wait, it’s nothing! The number of projects is growing, and with them, our team too. And now we need a Data Architect.

Must-Have Skills

• Data Architecture (8+ years, 3+ as architect)
• Enterprise data architecture — conceptual, logical, physical modeling
• Lakehouse architecture patterns — medallion architecture, data mesh principles
• Hybrid data architecture — on-prem ↔ cloud data movement, consistency, governance
• Data modeling — dimensional modeling, Data Vault 2.0, or similar for analytical workloads

• Cloudera / Hadoop Ecosystem (Critical)
• Cloudera Data Platform (CDP) — architecture, administration, security
• Apache Hive / Iceberg — table formats, partitioning strategies, compaction
• Apache Spark on Cloudera — performance tuning, resource management
• HDFS / Ozone — storage architecture, replication, tiering
• Apache Ranger / Knox — security, access control, audit
• Query & Analytics Layer
• Starburst / Trino — federated query architecture, connector management, caching strategies
• Tableau — data source architecture, extract vs. live connections, semantic layer design
• SQL optimization for analytical workloads — join strategies, materialized views, caching
• Streaming & Integration
• Apache Kafka — topic design, schema registry (Avro/Protobuf), exactly-once semantics
• CDC (Change Data Capture) patterns — Debezium, Kafka Connect
• ETL/ELT pipeline design — batch and streaming patterns
• Apache Airflow — DAG design for complex data pipelines
• Governance & Quality
• Informatica — data catalog, data quality, metadata management
• Data governance frameworks — data ownership, stewardship, lineage
• ABAC implementation for data access (attribute-based access control)
• Data quality metrics and monitoring
• Business glossary management

• Cloud Data Architecture (AWS)
• S3 — data lake storage patterns, lifecycle policies, storage classes
• AWS Glue — catalog, ETL jobs
• Lake Formation — fine-grained access control
• Hybrid integration — AWS Direct Connect, data replication strategies
   

Nice-to-Have

• Telco data models (CDR, network events, subscriber data, billing)
• Experience migrating from traditional Hadoop to modern lakehouse
• Data Mesh implementation experience
• Cost optimization for large-scale data platforms
• DAMA-DMBOK or equivalent data management certifications
   

Engagement Model

• Full-time staff augmentation (embedded in team)
• Business trips to Baku every 2 month
• Must overlap with Baku working hours (GMT+4)
• English fluency required

Data Science UA is a service company with deep expertise in AI and Data Science. Our story started 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 client and role:
We are looking for a Computer Vision/3D Vision Engineer for our client, who is the creator of the world’s leading patented mobile body scanning technology, driving retail innovation by providing personalized fit and size recommendations and virtual try-on solutions for shoppers. The company helps brands reduce returns while simultaneously increasing conversion rates and average order value (AOV).

Responsibilities:
- Design, train, and deploy computer vision systems from initial dataset preparation to production-ready models.
- Solve complex problems in object detection, segmentation, and pose estimation using custom-built solutions.
- Develop and optimize pipelines for 3D reconstruction, depth estimation, and point cloud processing.
- Evaluate and debug model performance, ensuring high accuracy and technical depth in every solution.
- Manage dataset annotation, curation, and model evaluation metrics to ensure robust performance.

Requirements:
- Hands-on experience with object detection, segmentation, tracking, and camera calibration. You should have a deep understanding of image geometry.
- Proficiency in at least one of the following: 3D reconstruction, Multi-view geometry, Photogrammetry, NeRF/Neural rendering, or SLAM.
- Expert-level knowledge of Python and frameworks like PyTorch or TensorFlow.
- Proven ability to build and explain a CV system end-to-end, including architecture choice and deployment logic.
- Critical thinking and the ability to understand why a model works (or doesn't), with a focus on experimentation and debugging.

Hello everyone 👋

At Intobi, we're a software and product development company passionate about driving innovation and progress.

We help our clients succeed by delivering custom-built tech solutions designed to meet their unique needs.

Our expertise lies in developing cutting-edge Web and Mobile applications.

We are seeking an experienced Mid/Mid+ Data Scientist with expertise in Large Language Models (LLMs) such as GPT, Claude, and related technologies. The ideal candidate will have a strong background in natural language processing (NLP), machine learning, and deep learning models. They will play a critical role in developing and deploying cutting-edge LLM applications to drive innovation across our product lines.

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

— 2-3+ 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

— Experience with classical ML models - XGBoost, LightGBM

— 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

Please send your CV here or via email

Should the first stage be successfully completed, you’ll be invited to a personal interview.

About Cyberette
Cyberette is an AI-native cybersecurity company developing forensic-grade technology to detect digital manipulation across video, audio, and images. Our mission is to restore trust in digital communication and protect people, organizations, and institutions from deception in the AI era.

About the Role
We are looking for a Computer Vision Data Scientist with strong initiative and technical excellence to join our core development team. You’ll be responsible for designing, training, and optimizing computer vision models and architectures used in our deepfake detection and forensic analysis platform.

This role requires someone who thrives in fast-paced environments, loves solving complex problems, and brings both creativity and rigor to their work. You’ll collaborate closely with our founders and engineers to push the limits of multimodal AI for real-world impact.

Key Responsibilities

• Develop, train, and deploy computer vision models for image and video manipulation detection.
• Research and experiment with new architectures, model ensembles, and multimodal fusion techniques.
• Preprocess, clean, and manage large-scale datasets for training and evaluation.
• Optimize model accuracy, latency, and scalability for enterprise-grade performance.
• Contribute to the design of the model evaluation framework and internal benchmarking tools.
• Collaborate with other engineers and researchers to integrate models into production pipelines.
• Document methodologies, experiments, and findings clearly and systematically.

Requirements

• MSc/PhD in Computer Science, Machine Learning, Artificial Intelligence, or related field.
• Strong experience in computer vision (CNNs, Transformers, multimodal models).
• Proficiency in Python and deep learning frameworks (PyTorch, TensorFlow).
• Solid understanding of ML ops, dataset curation, and model evaluation.
• Proven experience building and training models from scratch.
• Excellent analytical and problem-solving skills.
• Self-driven, disciplined, and collaborative mindset.

What We Value
At Cyberette, we believe technology should serve people. We are a team of builders united by a shared sense of purpose — creating tools that make the digital world safer and more truthful. If you care deeply about impactful innovation and want your work to matter, you’ll feel at home here.

We value ownership, fast responses, and a goal-oriented mindset. Our environment is collaborative and fast-moving, and we appreciate people who communicate proactively, take initiative, and focus on delivering meaningful results.

 

Bonus Skills

• Experience with audio or multimodal analysis.
• Knowledge of adversarial attacks or deepfake generation methods.
• Experience in real-time inference or edge deployment.

We are looking for a Data Scientist to join the analytics team of a leading Life Sciences / Pharma insights agency. The role focuses on advanced data modelling, NLP analysis, and transforming complex datasets into meaningful insights for pharmaceutical clients.

This is a hands-on role for someone who enjoys working with real-world datasets, building models, and communicating insights to stakeholders in the healthcare and scientific space.

The position is remote, full-time, and initially offered as a 6-month contract with strong potential extension.

ROLE OVERVIEW

You will work closely with analysts, researchers, and client teams to develop data science solutions that support insight generation for pharmaceutical and healthcare projects.

The role combines advanced modelling, NLP analysis, and data analytics, with an emphasis on interpreting complex data and presenting results clearly to non-technical stakeholders.

KEY RESPONSIBILITIES

Data Science & Modelling

• Develop machine learning and statistical models to analyse structured and unstructured data
• Apply NLP techniques such as topic modelling, clustering, text classification, and sentiment analysis
• Work with large datasets from surveys, research, and digital sources

Advanced Data Analysis

• Perform exploratory data analysis and statistical investigations
• Identify patterns, trends, and meaningful signals in complex datasets
• Translate modelling results into actionable insights

Data Processing & Pipelines

• Build and maintain data processing workflows
• Work with ETL pipelines and data transformations
• Ensure data quality, validation, and consistency

Insight Communication

• Present analytical findings clearly to internal teams and clients
• Build visualisations and reports that communicate complex results effectively
• Collaborate with analysts and consultants to shape research outputs

AI & LLM Applications

• Experiment with LLMs and AI tools for text analysis, summarisation, and insight generation
• Use modern AI-assisted development tools to accelerate workflows

REQUIRED SKILLS

• Strong Python skills for data science and analytics
• Experience with machine learning and statistical modelling
• Practical experience with NLP techniques (topic modelling, embeddings, classification, clustering)
• Experience with data analysis libraries such as pandas, NumPy, scikit-learn
• Ability to work with messy real-world datasets
• Strong analytical thinking and problem-solving ability

• Ability to clearly explain complex analytical results

   

NICE TO HAVE

• Experience working in Pharma, Life Sciences, or Healthcare analytics
• Experience working in agencies or consulting environments
• Familiarity with LLMs or AI-assisted data analysis tools
• Experience with data visualisation tools (e.g. Tableau, Power BI, Python visualisation libraries)

IDEAL CANDIDATE

You are someone who:

• enjoys building models and analysing complex datasets
• understands how to turn modelling results into insights
• can communicate findings clearly to non-technical audiences
• is comfortable working in a fast-paced consulting environment

Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.

About the client:
The company is a trailblazer in the world of data-driven advertising, known for its innovative approach to optimizing ad placements and campaign effectiveness through advanced analytics and machine learning techniques. Our mission is to revolutionize the advertising sector by enabling brands to reach their audiences more effectively.

About the role:
We are seeking an experienced and motivated Senior Data Scientist to join our dynamic team. The ideal candidate will have deep expertise in supervised learning, reinforcement learning, and optimization techniques. You will play a pivotal role in developing and implementing advanced machine learning models, driving actionable insights, and optimizing our advertising solutions.
This position is based in Ukraine. The team primarily works remotely, with occasional in-person meetings in the Kyiv or Lviv office.

Responsibilities:
- Develop and implement advanced supervised and reinforcement learning models to improve ad targeting and campaign performance.
- Collaborate with cross-functional teams to identify opportunities for leveraging machine learning and optimization techniques to solve business problems.
- Conduct extensive data analysis and feature engineering to prepare datasets for machine learning tasks.
- Apply optimization algorithms to enhance the effectiveness and efficiency of advertising campaigns.
- Evaluate and refine existing models to enhance their accuracy, efficiency, and scalability.
- Utilize statistical techniques and machine learning algorithms to analyze large and complex datasets.
- Communicate findings and recommendations effectively to both technical and non-technical stakeholders.
- Stay updated with the latest advancements in machine learning, reinforcement learning, and optimization techniques.
- Work with engineering teams to integrate models into production systems.
- Monitor, troubleshoot, and improve the performance of deployed models.
- Mentor junior data scientists and contribute to the continuous improvement of the data science practice within the company.

Requirements:
- 5+ years of experience in data science or machine learning roles, with a strong focus on supervised learning, reinforcement learning, and optimization techniques.
- Technical Skills:
- Proficiency in Python.
- Strong understanding of working with relational databases and SQL.
- Experience with machine learning libraries such as scikit-learn, TensorFlow, PyTorch, or similar.
- Deep understanding of statistical modeling and supervised learning algorithms (e.g., linear regression, logistic regression, decision trees, random forests, SVMs, gradient boosting, neural networks).
- Hands-on experience with reinforcement learning algorithms and frameworks like OpenAI Gym.
- Practical experience with optimization algorithms (linear, non-linear, combinatorial, etc.).
- Hands-on experience with data manipulation tools and libraries (e.g., pandas, NumPy).
- Familiarity with cloud services, specifically AWS, is a plus.
- Practical experience building and managing cloud-based ML pipelines using AWS services (e.g. SageMaker, Bedrock) is a plus.
- Education:
- Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Engineering, or a related field. A PhD is a plus.

Other Skills:
- Strong analytical and problem-solving skills.
- Excellent communication skills, with the ability to clearly articulate complex concepts to diverse audiences.
- Ability to work in a fast-paced environment and manage multiple priorities.
- Strong organizational skills and attention to detail.
- Ability to mentor and guide junior data scientists.
- Must be able to communicate with U.S.-based teams

The company offers:
- An opportunity to be at the forefront of advertising technology, impacting major marketing decisions.
- A collaborative, innovative environment where your contributions make a difference.
- The chance to work with a passionate team of data scientists, engineers, product managers, and designers.
- A culture that values learning, growth, and the pursuit of excellence.

Data Science UA is a service company with deep expertise in AI and Data Science. Our story started 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 hands-on Sr. Machine Learning / Computer Vision Engineer with strong practical experience in designing, training, evaluating, and improving vision systems for real-world applications. The ideal candidate combines solid theoretical ML/CV fundamentals with an engineering mindset and is comfortable taking ownership of the full pipeline, from data preparation and experimentation to model optimization and deployment.

Responsibilities:
- Design, train, evaluate, and improve machine learning and computer vision models for real-world production use cases
- Build and maintain end-to-end ML pipelines, including data collection support, preprocessing, feature engineering, training, validation, and inference
- Develop solutions across both computer vision and machine learning tasks depending on business needs
- Perform error analysis, diagnose model weaknesses, and drive improvements in accuracy, robustness, and reliability
- Select and justify appropriate methods and model architectures based on dataset characteristics, product constraints, and deployment requirements
- Work with challenging real-world data, including noisy, limited, imbalanced, or weakly labeled datasets
- Collaborate with engineering and product teams to translate business problems into practical ML solutions
- Contribute to production-ready development with a focus on maintainability, reproducibility, and performance

Requirements:
- 3–5+ years of professional experience in Machine Learning / Computer Vision
- Strong senior-level practical expertise in computer vision, machine learning, and applied model development
- Hands-on experience with tasks such as object detection, image classification, anomaly detection, feature-based approaches, or similar
- Solid understanding of classical ML methods, including tree-based models, linear models, feature engineering, model selection, and evaluation
- Strong Python skills and practical experience with PyTorch, OpenCV, and common ML tooling
- Experience building full ML workflows including data preparation, augmentation, preprocessing, training, validation, testing, and deployment handoff
- Strong understanding of model evaluation, debugging, overfitting, generalization, optimization, and experiment-driven improvement
- Ability to write clean, modular, maintainable code and independently own technical solutions
- Strong analytical thinking, engineering maturity, and problem-solving skills

Nice to Have:
- Experience in adjacent ML domains such as time-series, audio, or NLP
- Experience with embedding-based methods, metric learning, retrieval systems, or memory-bank approaches
- Practical experience with model optimization techniques such as quantization, pruning, distillation, or related efficiency-focused approaches
- Experience working with real-world industrial datasets and ambiguous problem formulations
- Knowledge of software engineering best practices, reproducible experimentation, and pipeline design

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

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.

We are looking for a Middle Machine Learning Engineer to develop and enhance AI-driven solutions within the Palantir Foundry and AIP ecosystem.

In this role, you will focus on building and iterating on machine learning and LLM-based solutions, integrating them into Foundry workflows to support analytics, automation, and decision-making. You will collaborate closely with data engineers, business analysts, and domain experts to deliver practical, production-ready AI solutions.

Key Responsibilities:

• Develop and enhance machine learning and AI models to support predictive analytics, classification, forecasting, and AI-assisted workflows.
• Build AI and ML solutions within Palantir Foundry, using Python and existing Foundry pipelines, Ontology objects, and workflows.
• Apply LLMs and NLP techniques (e.g. prompt engineering, fine-tuning, embeddings, retrieval-augmented workflows) using Palantir AIP for enterprise use cases.
• Collaborate with data engineers to understand data sources, ensure data quality, and prepare datasets for model training and inference.
• Conduct experiments, evaluate model performance, and iterate on features and model approaches.
• Integrate AI models into Foundry workflows to surface insights and support business processes.
• Support model deployment and monitoring by following established team standards and best practices.
• Work closely with business and domain stakeholders to translate requirements into practical AI-driven solutions.
• Document model behavior, assumptions, and limitations to support transparency and compliance.
• Stay up to date with applied AI and GenAI trends and contribute ideas under guidance from senior team members.

Requirements:

• 3+ years of experience in machine learning, AI engineering, or applied data science.
• Strong Python skills; experience with ML libraries such as scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Hands-on experience with LLMs, NLP, or GenAI use cases (e.g. prompt design, embeddings, text classification, summarization).
• Practical understanding of the ML lifecycle: data preparation, feature engineering, model training, evaluation, and iteration.
• Experience working with structured data (tabular, time series); exposure to text or unstructured data is a plus.
• Familiarity with enterprise data environments and collaborative development workflows.
• Ability to clearly explain model results and AI behavior to non-technical stakeholders.
• Upper-Intermediate English or higher.

Nice to have: 

• Proficiency in Foundry Ontology, Object Builders, and Code Repositories.
• Experience in big pharma or highly regulated industries.
• Knowledge of data privacy, compliance, and security best practices in AI applications.
• Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).

As part of the AI transformation strategy, Kyivstar invests resources into development of products and services that uses cutting edge ML and Speech technologies to significantly improve internal processes and day-to-day tasks, but also uses gained experience to help partners and clients to receive maximum benefits from these technologies too.

Necessary skills:

•4+ years of experience in Machine Learning, Deep Learning and Speech Processing

•Strong Python skills with experience building maintainable and production-ready ML code

•Fundamental knowledge of  ML/DL theoretical concepts

•Hands-on experience with training and deploying models for various speech processing tasks including data preparation (e.g., speech classification, speech-to-text, text-to-speech, speaker diarization, etc.)

•Practical experience developing and deploying end-to-end production-ready ML pipelines, covering data gathering, preparation, training, validation, and inference

•Proficient in version control (Git) and containerization tools (Docker)

Would be a plus:

•Experience with real-time / low-latency speech systems

•Experience with distributed data processing and training in multi-node, multi-GPU environments

•Familiarity with PySpark for large-scale data processing

•Experience with Azure/AWS

•Exposure to Data Science in the Telecom industry

Responsibilities:

•Design, train, and optimize ASR and TTS models for production use

•Design, train, and evaluate classical ML models

•Perform feature engineering and data analysis for both speech and non-speech data

•Collaborate with DE/DevOps team to deploy the solution

•Plan and manage ML projects, articulate methodologies, and communicate results and business impact to stakeholders and customers

Proposed:

•Working with huge amounts of data at the largest operator in Ukraine

•Hands on experience with Cloud technologies and tools

•Great diversity of tasks and projects to utilize your knowledge and skills; flexibility in tools and approaches

•Opportunity to grow within the team of talented data scientists

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.

We are looking for a Autonomy Engineer to work on autonomous systems with a focus on computer vision, control theory, and data-driven modeling. This role is ideal for someone with strong analytical skills and experience applying data science to real-world autonomy or robotics problems.

Key Responsibilities

• Develop vision-based algorithms for perception and navigation.
• Work on data analysis and sensor fusion for multi-sensor systems.
• Build models and simulations to support system development and testing.
• Process and analyze datasets from cameras, IMUs, and other sensors to improve autonomy performance.
• Collaborate with robotics and software engineers on perception and navigation pipelines.
• Stay up to date with recent research in computer vision, autonomy, and data science.

Required Qualifications

• 2+ years of experience in data science, robotics, computer vision, or a related field.
• Knowledge of control theory, PID controllers, signal processing, and data-driven modeling.
• Understanding of geometric computer vision principles.
• Strong programming skills in Python and good working knowledge of C++.
• Experience working with Linux-based systems.
• Degree in Computer Science, Robotics, Engineering, Mathematics, or another STEM field (or equivalent practical experience).

Nice to Have

• Experience with SLAM or Visual-Inertial Odometry (VIO).
• Familiarity with OpenCV, NumPy, and ML frameworks such as PyTorch or TensorFlow.
• Experience with ROS2, Gazebo, or AirSim.
• Experience with PX4, Betaflight, or ArduPilot.
• Understanding of neural networks for computer vision.
• Interest in reinforcement learning or predictive modeling.

Data Science UA is a service company with deep expertise in AI and Data Science. Our story started 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 hands-on Sr. Machine Learning / Computer Vision Engineer with strong practical experience in designing, training, evaluating, and improving vision systems for real-world applications. The ideal candidate combines solid theoretical ML/CV fundamentals with an engineering mindset and is comfortable taking ownership of the full pipeline, from data preparation and experimentation to model optimization and deployment.

Responsibilities:
- Design, train, evaluate, and improve machine learning and computer vision models for real-world production use cases
- Build and maintain end-to-end ML pipelines, including data collection support, preprocessing, feature engineering, training, validation, and inference
- Develop solutions across both computer vision and machine learning tasks depending on business needs
- Perform error analysis, diagnose model weaknesses, and drive improvements in accuracy, robustness, and reliability
- Select and justify appropriate methods and model architectures based on dataset characteristics, product constraints, and deployment requirements
- Work with challenging real-world data, including noisy, limited, imbalanced, or weakly labeled datasets
- Collaborate with engineering and product teams to translate business problems into practical ML solutions
- Contribute to production-ready development with a focus on maintainability, reproducibility, and performance

Requirements:
- 3–5+ years of professional experience in Machine Learning / Computer Vision
- Strong senior-level practical expertise in computer vision, machine learning, and applied model development
- Hands-on experience with tasks such as object detection, image classification, anomaly detection, feature-based approaches, or similar
- Solid understanding of classical ML methods, including tree-based models, linear models, feature engineering, model selection, and evaluation
- Strong Python skills and practical experience with PyTorch, OpenCV, and common ML tooling
- Experience building full ML workflows including data preparation, augmentation, preprocessing, training, validation, testing, and deployment handoff
- Strong understanding of model evaluation, debugging, overfitting, generalization, optimization, and experiment-driven improvement
- Ability to write clean, modular, maintainable code and independently own technical solutions
- Strong analytical thinking, engineering maturity, and problem-solving skills

Nice to Have:
- Experience in adjacent ML domains such as time-series, audio, or NLP
- Experience with embedding-based methods, metric learning, retrieval systems, or memory-bank approaches
- Practical experience with model optimization techniques such as quantization, pruning, distillation, or related efficiency-focused approaches
- Experience working with real-world industrial datasets and ambiguous problem formulations
- Knowledge of software engineering best practices, reproducible experimentation, and pipeline design

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

If you’re a talented Data Scientist looking to be part of a fast-growing, innovative R&D team, shape the future of decision-making in business, work on cuttingedge technologies and vast amounts of global real-time data, affect billions of mobile users, and become an industry leader - then let’s talk! Start.io is a mobile data platform that enables organizations to uncover insights and make datadriven decisions that enhance strategies and drive growth. We are seeking a Senior Data Scientist to join our Data Science & Engineering team.

Responsibilities:

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

Requirements:

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

Will be a plus:

• Knowledge in the programmatic advertising landscape – DSPs, SSPs, exchanges – significant advantage;
• Experience with relational (e.g. Vertica, VoltDB) and non-relational (e.g. MongoDB) databases;
• Experience with PMML.

What we offer:

• Help and support from our caring HR team;
• 20 days of vacation;
• The exchange of experience and work with talented colleagues;
• Last but not least - valuable compensation for your efforts.

We are looking for a Senior Data Scientist to build the core intelligence layer of our Session Analyzer product: scoring, audience segmentation, and prescriptive recommendations for pharmaceutical marketing teams. You will operate as a “full-stack” data scientist across data validation, data engineering (prod pipelines), ML modeling, causal measurement, and AI-assisted workflows (agents for insights and data diagnostics). You will work directly with the Product Lead and Tech Lead.

Responsibilities

- Design and ship scoring, segmentation, and recommendation features based on pixel session/event data, NPI-enriched profiles, and 3rd-party datasets.

- Build a data quality & validation framework from scratch: tests, monitors, anomaly detection, drift tracking, and root-cause workflows across the data chain.

- Deliver batch PoC (days latency) and evolve to near-real-time MVP (hours latency) pipelines and feature tables.

- Develop and evaluate ML models for engagement prediction and audience targeting; ensure calibration, stability, and business-relevant lift.

- Define and implement measurement/attribution approaches to support ROI uplift and explain “what worked / why”.

- Build AI agents for (1) automated data issue diagnostics and (2) insight/presentation generation, with guardrails and traceability.

- Produce outputs as score tables, top insights, aggregates/features, dashboards/reports, and APIs for audience activation and CDP/CRM integrations.

Requirements

- 5+ years in Data Science / Analytics roles with production ownership.

- Strong Python + SQL; hands-on building production pipelines and data products.

- Experience with event/session analytics, aggregation modeling, and working with noisy web/pixel data.

- Proven ability to build propensity/engagement models, segmentation, and recommendation logic; strong evaluation practices.

- Working knowledge of causal inference / attribution methods for marketing effectiveness.

- Experience with GCP / BigQuery in production; familiarity with ClickHouse/Postgres is a plus.

- Ability to operate end-to-end: problem framing → data validation → modeling → deployment → monitoring → stakeholder comms.

- Hands-on experience building and operating data-driven marketing/advertising analytics products, including event-level measurement, audience segmentation, activation workflows, and performance attribution (AdTech/MarTech environments).

Nice-to-have: 

- Experience with agents (LLM-based workflows), automated insights, and safe deployment patterns.

- Familiarity with data quality tooling (e.g., Great Expectations/dbt tests) and observability.

What We Offer

- Fully remote with flexible hours (aligned with EU timezones for syncs).
- Influence on quality strategy across the entire engineering organization.
- A cross-team role with visibility into every part of the product.
- AI-first tooling.
- Claude Code licenses and cutting-edge AI development workflows. 
- A team with no bureaucracy, decisions are made fast.