Jobs Data Science

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.

About Tie

Tie is building the next generation of identity resolution and marketing intelligence. Our platform connects hundreds of millions of consumers across devices, browsers, and channels—without relying on cookies—to power higher deliverability, smarter targeting, and measurable revenue lift for modern marketing teams.

At Tie, AI is not a feature—it is a core execution advantage. We operate large-scale identity graphs, real-time scoring systems, and production ML pipelines that directly impact revenue, deliverability, and customer growth.

The Role

We are looking for a Senior AI / Machine Learning Engineer to design, build, and deploy production ML systems that sit at the heart of our identity graph and scoring platform. You will work at the intersection of machine learning, graph data, and real-time systems, owning models end to end—from feature engineering and training through deployment, monitoring, and iteration.

This role is highly hands-on and impact-driven. You will help define Tie’s ML architecture, ship models that operate at sub-second latency, and partner closely with platform engineering to ensure our AI systems scale reliably.

What You’ll Do

• Design and deploy production-grade ML models for identity resolution, propensity scoring, deliverability, and personalization
• Build and maintain feature pipelines across batch and real-time systems (BigQuery, streaming events, graph-derived features)
• Develop and optimize classification models (e.g., XGBoost, logistic regression) with strong handling of class imbalance and noisy labels
• Integrate ML models directly with graph databases to support real-time inference and identity scoring
• Own model lifecycle concerns: evaluation, monitoring, drift detection, retraining, and performance reporting
• Partner with engineering to expose models via low-latency APIs and scalable services
• Contribute to GPU-accelerated and large-scale data processing efforts as we push graph computation from hours to minutes
• Help shape ML best practices, tooling, and standards across the team

What You’ll Bring

Required Qualifications

• 5+ years of experience building and deploying machine learning systems in production
• Strong proficiency in Python for ML, data processing, and model serving
• Hands-on experience with feature engineering, model training, and evaluation for real-world datasets
• Ability to travel outside of Ukraine is a must
• Experience deploying ML models via APIs or services (e.g., FastAPI, containers, Kubernetes)
• Solid understanding of data modeling, SQL, and analytical workflows
• Experience working in a cloud environment (GCP, AWS, or equivalent)
• Experience with graph data, graph databases, or graph-based ML

• Familiarity with Neo4j, Cypher, or graph algorithms (community detection, entity resolution)

  Preferred / Bonus Experience

• Experience with XGBoost, tree-based models, or similar classical ML approaches
• Exposure to real-time or streaming systems (Kafka, Pub/Sub, event-driven architectures)
• Experience with MLOps tooling and practices (CI/CD for ML, monitoring, retraining pipelines)
• GPU or large-scale data processing experience (e.g., RAPIDS, CUDA, Spark, or similar)
• Domain experience in identity resolution, marketing technology, or email deliverability

Our Technology Stack

• ML & Data: Python, Pandas, Scikit-learn, XGBoost
• Graphs: Neo4j (Enterprise, GDS)
• Cloud: Google Cloud Platform (BigQuery, Vertex AI, Cloud Run, Pub/Sub)
• Infrastructure: Docker, Kubernetes, GitHub Actions
• APIs: FastAPI, REST-based inference services

What We Offer

• Competitive compensation, including salary, equity, and performance incentives
• Opportunity to work on core AI systems that directly impact revenue and product differentiation
• High ownership and autonomy in a senior, hands-on role
• Remote-first culture with a strong engineering and data focus
• Exposure to cutting-edge problems in identity resolution, graph ML, and real-time AI systems

• Clear growth path toward Staff / Principal IC roles

  What else:

• 4 weeks of paid vacation per year (flexible scheduling)
• Unlimited sick leave — we trust your judgment and care about your health
• US Bank Holidays off (American calendar)
• Remote-first culture and flexible working hours
• Flat structure, no micromanagement, and full ownership

• Opportunity to make a real impact during a critical growth phase

  Interview Process

• Recruitment Screening Call
• Initial call with Head of Data Science & AI and CTO  (30 min) in English
• Technical deep dive Interview (1,5h)  in English

• Optional test-task (paid)

  Why Join Us?

• High-impact delivery leadership role during a critical period
• Real ownership and autonomy
• Opportunity to shape delivery across the entire engineering organization
• Exposure to SaaS, data, integrations, automation, and platform work
• Collaboration with global teams and vendors
• A strong product with real scale and momentum

Why This Role Matters

At Tie, your work will not live in notebooks or experiments—it will power production systems used by real customers at scale. You will help define how AI is embedded into the company’s core platform and play a key role in making machine learning a durable competitive advantage.

Project

The project focuses on a deep refactoring and optimization of the reporting ecosystem combining Salesforce CRM and Tableau. The main goal is to redesign current data flows, replacing the bulky and slow architecture with a modular system of reusable components. You will work on a process redesign to ensure speed, data accuracy, and ease of future system maintenance.

Responsibilities

• Development and Redesign: designing and supporting complex interactive visualizations in Tableau Desktop with a focus on modularity.
• Architecture Optimization: transforming bulky data flows into a fast and stable reporting system.
• Integration and Setup: working with data streams from Salesforce CRM and ERP systems (SAP, Odoo, MS Business Central).
• Technical Optimization: performance tuning (Data Extracts, calculated fields, SQL query optimization).
• Business Collaboration: gathering requirements from stakeholders and translating them into clear technical specifications for visualization.
• Data Governance: ensuring data accuracy, integrity, and security (including Row-Level Security).

Requirements

• Must-have:
  • 1.5-2+ years of commercial experience with Tableau (Desktop, Server/Cloud) or other BI tools.
  • Strong SQL skills (writing complex queries, joins, CTEs).
  • Understanding of CRM and ERP system architecture (how data is stored and interconnected).
  • Intermediate+ English level (for technical communication and documentation).
• Nice-to-have:
  • Experience with Tableau and Salesforce integration (a significant plus).
  • Experience with Tableau Prep or other ETL tools (Alteryx, KNIME).
  • Tableau Desktop Specialist or Certified Data Analyst certification.
  • Experience with cloud data warehouses (Snowflake, BigQuery, AWS Redshift).

Conditions

• Collaboration Type: Part-time (Freelancer / Contract).
• Location: Remote.
• Schedule: Flexible (details to be discussed based on your availability).

Selection Process

1. Recruiter screening.
2. Technical interview with the Tech Lead.
3. Offer.

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.

Role Overview

Nucleus AI is seeking an AI / ML Data Scientist to help build and scale an AI-backed Learning Management System (LMS) that delivers personalized, adaptive learning experiences.

This is a hands-on, product-focused role at the intersection of machine learning, data science, and learning technology. You will design and deploy intelligent systems that directly impact learner engagement, personalization, assessment, and outcomes.

If you enjoy turning real-world data into models that are used by real users—and iterating on them in production—this role is for you.

Key Responsibilities

Machine Learning & Modeling

• Design, develop, and deploy machine learning models that power:
  • Personalized learning paths
  • Content and course recommendations
  • Learner analytics and insights
  • Adaptive assessments and feedback
     
• Build models for:
  • Learner skill inference and knowledge tracing
  • Engagement, completion, and drop-off prediction
  • Automated assessment scoring and feedback
     

Data & Analytics

• Analyze large-scale learner behavior data to extract actionable insights
• Develop and maintain data pipelines, feature engineering workflows, and model evaluation frameworks
• Apply statistical analysis and experimentation (including A/B testing) to validate model performance and impact
   

Collaboration & Product Integration

• Work closely with product managers, engineers, and instructional designers to translate learning objectives into AI-driven solutions
• Integrate models into production systems via APIs, batch pipelines, or real-time inference
   

LLMs & Advanced Techniques

• Experiment with and integrate LLMs and NLP techniques for:
  • Content generation
  • Learner feedback
  • Intelligent learner support
     
• Monitor models in production for performance, bias, and drift, and continuously improve them
   

Documentation & Governance

• Document models, assumptions, experiments, and results to ensure transparency, reproducibility, and maintainability
   

Required Qualifications

• Bachelor’s or Master’s degree in Data Science, Computer Science, AI/ML, Statistics, or a related field
• Strong proficiency in Python and common ML libraries (e.g., scikit-learn, PyTorch)
• Solid understanding of:
  • Supervised and unsupervised learning
  • Feature engineering and model evaluation
  • Statistical analysis and experimentation
     
• Experience working with both structured and unstructured data
• Proficiency in SQL and working with large datasets
• Ability to clearly communicate complex technical concepts to non-technical stakeholders
   

Nice-to-Have Qualifications

• Experience building ML systems in ed-tech, LMS platforms, or learning analytics
• Familiarity with
  • Large Language Models (LLMs)
  • NLP
  • Recommendation systems
  • Knowledge graphs
• Experience deploying models to production environments
• Exposure to cloud platforms such as AWS, GCP, or Azure
• Understanding of learning science, instructional design, or assessment theory
• Experience with MLOps tools (model versioning, monitoring, CI/CD for ML)
   

What We Offer

• Opportunity to work on mission-driven AI that improves how people learn
• Ownership of ML systems used by real learners at scale
• A collaborative, cross-functional team culture
• Competitive compensation and benefits
• Flexible work location and schedule
• Continuous learning and professional growth opportunities

About Keymakr

• Keymakr specializes in end-to-end dataset preparation, including video and image annotation and labeling for AI projects.
  We take a personalized approach, delivering customized solutions tailored to each client’s needs.
   
• Our strong in-house R&D team develops and adapts annotation tools for every client, allowing us to successfully handle even the most complex requirements.

About the Role:

• We are looking for a Data Management Specialist to manage, process, and maintain large volumes of data and media assets used in AI and technology projects.
   
• This role focuses on ensuring data integrity, consistency, and availability while optimizing workflows through automation and close collaboration with cross-functional teams.
   

Key Responsibilities:

1. Manage and manipulate data using Linux file systems and native OS tools;
2. Work with media files, including video formats, codecs, resolution, and frame rate (FPS);
3. Verify data integrity using checksum and verification tools;
4. Pack and unpack large data archives efficiently;
5. Automate data-related workflows using scripting languages;
6. Prepare, structure, and maintain datasets in structured formats;
7. Maintain cloud-based data storage and perform data migration;
8. Collaborate with Project, QA, and Technical teams to ensure smooth data operations;
9. Clearly and consistently document workflows, processes, and task updates.

Technical Skills:

1. Strong knowledge of Linux file systems and administration (Ubuntu/Debian, 2+ years);
2. Experience with Windows OS administration;
3. Proficiency in Python and Bash / Shell scripting;
4. Experience with archive management tools for large datasets;
5. Knowledge of checksum verification tools (md5sum, sha256sum);
6. Tools for checking video properties, codecs, and formats;
7. Solid knowledge of JSON and CSV formats;
8. Experience with cloud storage platforms: AWS S3, Google Cloud Storage, or Azure;
9. Familiarity with JIRA, Slack, Confluence, and Google Docs.

Nice to Have:

1. Experience with point clouds / 3D data;
2. Strong understanding of complex technical specifications;
3. Experience working with large-scale datasets;
4. English & Communication;
5. English level B2 (Upper-Intermediate) or higher;
6. Ability to read and understand technical documentation and project requirements;
7. Clear written communication in English (reports, updates, documentation);
8. Participation in English-language meetings when required. 

If you want, I can also:

• tailor it for remote / hybrid / office format,
• shorten it to fit DOU’s compact job posting style,
• or adjust it to a more technical / more business-oriented tone.

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.

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.

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

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.