Jobs Data Science

We are looking for a Marketing / Commercial Data Scientist to build the intelligence layer of our Session Analyzer product — including scoring systems, segmentation, recommendation models, and causal measurement frameworks for pharmaceutical marketing teams.

This role sits at the intersection of behavioral data, machine learning, and marketing analytics. You will work end-to-end: from raw event data → feature engineering → modeling → production pipelines → business insights.
 

You’ll collaborate closely with Product and Tech Leads to turn complex behavioral signals into actionable commercial intelligence.

🛠 Requirements

• 5+ years in Data Science / Analytics
• Experience building production ML systems
• Strong ownership of end-to-end data science workflows
   

Technical Skills

• Python, SQL, PostgreSQL
• GCP / BigQuery /ClickHouse
• Data pipelines & feature engineering
• Experience with LLM-based agents / AI workflows
• Event-level / session analytics
• Aggregation modeling
• Propensity / engagement modeling
• Segmentation
• Recommendation systems
• Model evaluation & monitoring
• Experience in AdTech / MarTech / Marketing Analytics
• Marketing attribution & measurement
• Behavioral / clickstream / pixel data
   

🧠 What You’ll Work On

🔹 Behavioral & Scoring Models

• Build propensity and engagement models (e.g., prescribing likelihood, rep response, content affinity)
• Develop switch / churn-style models and brand adoption predictions
• Design segmentation frameworks based on behavioral and profile data
   

🔹 Recommendation & Personalization

• Build recommendation systems using session and event-level data
• Model multi-channel engagement and content affinity
   

🔹 Causal Inference & Attribution

• Design and implement marketing attribution models
• Apply causal inference techniques (uplift modeling, incrementality testing, treatment/control analysis)
• Connect marketing exposure → downstream outcomes
   

🔹 Data & Feature Engineering

• Build feature pipelines from event/session data (clicks, scroll depth, dwell time, downloads, video plays)
• Create aggregated feature tables and scoring datasets
• Work with high-volume data systems (e.g., ClickHouse, BigQuery)
   

🔹 Production & Data Quality

• Deploy and maintain production ML pipelines
• Implement data validation, monitoring, anomaly detection, and drift tracking
   

🔹 AI & Insight Automation

• Build LLM-powered workflows for:
  • Automated insight generation
  • Data diagnostics
  • Structured analytical outputs (e.g., JSON)
     

🎯 What Success Looks Like

• You build reliable scoring systems used in production
• Your models drive measurable marketing impact
• You translate behavioral data into clear, actionable insights
• You help evolve the system toward real-time intelligence & automation
   

🧪 Interview Process

• Screening Interview
• Technical Interview
• Bar Raiser

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.

Computer Vision engineer

DeepX is looking for a Computer Vision engineer

Core requirements:
- Python development experience
- Knowledge of popular data processing libraries (numpy, pandas, scipy etc)
- GIT, Linux, bash

Nice to have (or able to learn quickly):
- Hands-on experience with popular Machine Learning frameworks (OpenCV, Pytorch, Tensorflow, Keras, Yolo)
- Understanding of Computer Vision problem space
- Machine Learning and Artificial Neural Networks fundamentals
- Linux, Docker, gcc/make etc experience
- C++ and C, creating Python C extensions

A bit about DeepX company:
DeepX is an R&D intensive and innovation-driven consortium that provides Artificial Intelligence-powered Computer Vision solutions for businesses.
Please, take a look to find out more about us https://deepxhub.com/
We are a group of enthusiastic adherents who are passionate about the idea of improving this world’s safety by making Computer Vision technologies more advanced. Lifetime learning is our motto, we are admire ambition hard workers who are ready to face new challenges. If you feel like we might be on the same page, please, apply!

Position Overview

As a Game Mathematician, you will own the mathematical design of our gaming systems — from core probability models to long-term behavioral dynamics of the game.

You will shape RTP, volatility curves, hit frequency, payout distributions, risk balance, and overall economic sustainability of each title.

This role requires deep theoretical knowledge, practical simulation skills, and the ability to collaborate closely with producers, designers, engineers, and compliance teams.

Responsibilities

• Develop and maintain detailed mathematical models for Slots, Crash Games, and Fast Games.

• Design and balance RTP, volatility, hit frequency, paytables, game curves.

• Model stochastic processes and RNG logic, ensuring correctness and predictability.

• Calculate probabilities and payout distributions across long-run simulations.

• Conduct large-scale simulations and statistical analysis of game behavior.

• Balance risk, retention, and economic KPIs of the product.

• Collaborate with producers to create optimal player experience.

• Work closely with engineers to validate correct technical implementation.

• Contribute to game logic design and innovative mechanics.

• Conduct competitive analysis of game mathematics and market trends.

• Participate in playtests and analyze real in-game behavior post-launch.

• Prepare complete mathematical documentation for certification and regulators.

Requirements

• Strong knowledge of probability theory, combinatorics, and mathematical statistics.

• Experience in slot or gambling game mathematics (3+ years).

• Practical experience designing and balancing RTP, volatility, and payout structures.

• Experience with stochastic modeling and simulations.

• Programming skills (Python, R, C++, MATLAB, or similar).

• Ability to clearly communicate mathematical decisions to cross-functional teams.

• English — Intermediate or higher.

Would Be a Plus

• Experience with crash or fast game mechanics.

• Experience with competitive math research.

• Understanding of player behavior and retention impact.

• Experience interacting with certification bodies and regulators.

What We Offer

• Direct influence on product economics and player experience.

• Real ownership of game mathematics and long-term performance.

• Competitive compensation.

• Professional growth inside a product-driven gambling studio.

• Flexible working format.

• Strong, collaborative team environment.

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 looking for a Computer Vision Engineer with a solid foundation in classical computer vision and hands-on experience implementing low-level CV algorithms. The ideal candidate will have exposure to SLAM, Visual-Inertial Odometry (VIO), and sensor fusion, with responsibilities adjusted according to experience level.

Responsibilities: 

• Design, implement, and optimize classical CV algorithms independently
• Develop and maintain components of SLAM/VIO pipelines
• Contribute to sensor fusion modules involving visual and inertial data
• Lead experiments, evaluate model performance, and propose improvements
• Review research papers and translate findings into practical solutions
• Collaborate across teams to ensure smooth integration of CV components.

Required Qualifications:

• Strong practical experience with classical CV and geometric vision
• Hands-on experience developing components of SLAM and/or VIO systems
• Proficiency in C++ and confident working in Linux environments
• Solid understanding of multi-sensor systems and data fusion
• Proven ability to prototype, benchmark, and optimize CV algorithms
• Ability to analyze research papers and implement state-of-the-art methods.

Nice to Have:

• Experience with Python
• Knowledge of neural networks and CV libraries/frameworks (OpenCV, NumPy, PyTorch, ONNX, Eigen, etc.)
• Experience with sensor fusion techniques
• Familiarity with embedded or real-time systems

Role Summary

We are looking for a Senior Data Scientist to lead the data intelligence layer of an AI-driven platform. This role focuses on extracting insights from structured and unstructured documents, building document processing and OCR pipelines, improving semantic retrieval systems, identifying information gaps in datasets, and maintaining high standards of data quality across end-to-end data workflows.
 

Key Responsibilities

• Design and improve OCR and document intelligence pipelines that convert complex PDFs and supporting files into structured, machine-readable data.
• Develop semantic retrieval workflows that identify relevant historical records and support recommendation systems based on similarity and contextual matching.
• Create data-driven methods to detect missing, incomplete, or inconsistent information within document-based datasets.
• Define and monitor data quality standards, validation rules, completeness checks, and anomaly detection mechanisms across data pipelines.
• Analyze structured and unstructured datasets to uncover patterns affecting operational performance, classification quality, and decision outcomes.
• Collaborate with machine learning engineers to prepare high-quality datasets for model training, validation, and iterative improvement.
• Develop labeling strategies, feature definitions, and dataset enhancement workflows for early-stage and production use cases.
• Evaluate the performance of retrieval systems, document extraction pipelines, and information coverage.
• Work closely with engineering and product teams to operationalize insights through dashboards, tools, and data-driven workflows.
• Communicate analytical findings, experiment results, and actionable insights clearly to technical and non-technical stakeholders.
   

Qualifications

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Artificial Intelligence, or a related technical field.
• 5+ years of experience in data science, applied NLP, document intelligence, or analytics-focused roles. 
• Strong proficiency in Python and practical experience with data science libraries and NLP tooling. 
• Experience building document processing or OCR pipelines and working with semi-structured or noisy datasets.
• Strong understanding of embeddings, semantic search, retrieval evaluation, and similarity-based recommendation systems.
• Experience with data cleaning, validation, feature engineering, and dataset quality management.
• Ability to design experiments and translate analytical findings into product or model improvements.
• Strong SQL skills and solid analytical problem-solving abilities.
   

Nice to Have

• Experience working with AI systems in regulated or compliance-sensitive environments.
• Familiarity with multilingual NLP applications or systems that support multiple languages.
• Experience with vector databases or retrieval-augmented architectures.
• Exposure to annotation workflows and human-in-the-loop review processes.
• Experience building dashboards, defining operational KPIs, or supporting data-driven reporting.
  
  We offer:
• Flexible working schedule
• Fully remote work
• Compensation for medical, educational and sports activities
• Professional growth opportunities
• Internal knowledge-sharing talks
• Awesome team events and activities
• Paid vacation and sick leave

We are looking for a Middle/Senior 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).

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.

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.

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