Jobs Data Science

Milionyx is a full-cycle iGaming company. We bring together in-house technology development, performance marketing, analytics, and operational expertise.

We are looking for a Senior Data Scientist to become a key contributor to our Marketing Analytics team. The primary focus of this role is building predictive models that optimize user acquisition, evaluate traffic quality, forecast user value, and maximize marketing ROI.
Our product operates on a white-label solution, so the ideal candidate should be comfortable handling heterogeneous datasets, and developing scalable analytical and ML-solutions.

Responsibilities:
Develop and maintain predictive models for:

• LTV, Early LTV Prediction
• ROAS
• Marketing Payback Period
• FTD Prediction
• Deposit behavior forecasting
• Churn Prediction;
• Analyze traffic quality across acquisition channels and identify optimization opportunities;
• Build user value prediction models using early lifecycle behavioral signals;
• Build ML-solutions to optimize marketing budget allocation;
• Collaborate closely with Marketing, Retention, Product, and Tech Teams;
• Monitor model performance, retrain models when necessary, and support production ML-pipelines.

Required Experience:

• 3+ years of experience as a Data Scientist;
• Strong background in Marketing Analytics, User Acquisition, or Growth Analytics;
• Hands-on experience building predictive models for user behavior and business value forecasting;
• Experience delivering ML-solutions from research through production;
• Experience working with large-scale datasets;
• Ability to work with incomplete and heterogeneous data.

Required Technical Skills:

• Python (Pandas, NumPy, Scikit-learn);
• Advanced SQL;
• Git/GitLab;
• Gradient Boosting (XGBoost, LightGBM, CatBoost);
• Random Forest;
• Logistic Regression;
• Customer Segmentation & Clustering;
• Time Series Forecasting (preferred);
• Uplift Modeling (preferred).

Nice to have:

• AWS ecosystem;
• Amazon Athena;
• Apache Kafka;
• GTA (internal analytical data platform);
• Large-scale event-based datasets.

We offer

• Flexible work format — remote, hybrid, or office in Kyiv, whichever suits you best
• Monday–Friday schedule, 10:00 AM–6:00 PM
• 18 days of paid vacation, 10 sick days
• Direct communication with leadership and real influence on marketing and business decisions, without unnecessary bureaucracy

Automat-it is an all-in AWS Premier Partner and Managed Services Provider specializing in the startup ecosystem. With over 800 customers and 500+ AWS certifications, Automat-it brings hands-on expertise in AI, DevOps, and FinOps to empower fast-paced startups to grow, deliver & win. Our customers save significant time-to-market and optimize their cloud performance and costs.

We work across EMEA and the US, fueling innovation and solving complex challenges daily. Join us to grow your skills, shape bold ideas, and help build the future of tech.

We’re looking for a Senior Data Scientist to join our Data Science practice and work directly with customers on complex AI and Machine Learning projects on AWS.

This is not a traditional analytics or research-focused Data Science role. You’ll work across classical ML, fine-tuning, GenAI, multimodal systems, and production ML on AWS, helping customers decide which technical approach actually fits their problem.

You’ll own projects end-to-end: understand the customer’s domain and data, evaluate different solution paths, define the architecture, implement the critical parts, and guide delivery through production.

📍 Work location: remote from Ukraine

If you are interested in this opportunity, please submit your CV in English.

Key Responsibilities

• Own Data Science projects end-to-end: Take customer problems from technical discovery through data analysis, solution design, experimentation, implementation, deployment, and production validation.
• Choose the right technical approach: Evaluate whether a problem should be solved with prompt engineering, RAG, GenAI, agentic workflows, fine-tuning, smaller language models, classical ML, Computer Vision, recommendation systems, or custom model training.
• Work deeply with data: Analyze and prepare customer datasets, identify data quality issues, create representative validation and golden datasets, and ensure the available data can support the chosen modelling approach.
• Build and fine-tune ML models: Train, fine-tune, optimize, evaluate, and deploy models using Python, PyTorch/TensorFlow, SageMaker, and modern ML tooling.
• Work with Generative AI: Build and evaluate GenAI solutions using Amazon Bedrock, RAG, prompt engineering, model selection, agentic workflows, and related AWS-native AI services.
• Use SageMaker in production: Work with SageMaker Studio, training jobs, endpoints, pipelines, model registry, batch inference, monitoring, and other production ML capabilities.
• Design production-ready solutions: Make architecture decisions across quality, latency, cost, scalability, maintainability, observability, and operational complexity.
• Work directly with customers: Participate in technical discovery, workshops, architecture discussions, and delivery conversations with founders, CTOs, engineering teams, and technical stakeholders.
• Challenge technical assumptions: Help customers avoid unnecessary complexity, explain trade-offs, costs and recommend simpler or more effective approaches when appropriate.
• Lead through technical ownership: Independently own projects with minimal supervision and mentor less experienced Data Scientists and engineers when needed.

• Collaborate across teams: Work closely with AI Engineers, MLOps, Data Engineering, DevOps, and Solution Architecture teams on customer solutions that cross multiple technical domains.

   

Requirements

• 5+ years of experience in Data Science, Machine Learning, Applied Science, or a closely related role.
• Strong classical Machine Learning fundamentals and hands-on experience building production ML solutions.
• Deep practical experience working with data, including data preparation, validation, feature engineering, dataset construction, and model evaluation.
• Strong Python skills and hands-on experience with PyTorch and/or TensorFlow.
• Strong practical experience with AWS SageMaker beyond notebook-level usage, including model training, deployment, inference, pipelines, or production operations.
• Hands-on experience with Amazon Bedrock and modern Generative AI approaches.
• Practical experience with fine-tuning models and understanding when fine-tuning is preferable to prompting, RAG, or other approaches.
• Experience in at least one strong ML domain such as NLP, Computer Vision, recommendation systems, forecasting, structured ML, multimodal ML, or similar.
• Understanding of RAG, embeddings, prompt engineering, foundation models, and agentic workflows.
• Strong understanding of MLOps and production ML practices, including model deployment, monitoring, reproducibility, lifecycle management, and CI/CD.
• Experience designing and owning solutions independently rather than working only from predefined technical specifications.
• Strong customer-facing communication skills and ability to explain technical trade-offs clearly.
• Ability to work with ambiguity, messy real-world data, and changing customer requirements.

• Strong technical judgment and a pragmatic approach to balancing model quality with delivery speed, cost, and business value.

   

Nice to have

• Experience with AgentCore, Bedrock Agents, LangGraph, Strands Agents, or other agentic frameworks.
• Experience with Small Language Models or domain-specific model adaptation.
• Experience with recommendation systems, audio ML, signal processing, or multimodal systems.
• Experience in technical consulting, pre-sales, or customer discovery.
• AWS Machine Learning certifications.
• Master’s degree or PhD in Computer Science, Machine Learning, Data Science, Mathematics, Statistics, or a related field.

What Success Looks Like in Year One

• Independently owns assigned data projects end-to-end, from understanding the problem and making technical decisions to implementation, troubleshooting, testing and successful delivery, with minimal supervision.
• Has built strong practical expertise across the team’s core data stack and can confidently work across different customer environments, including Spark, Python/SQL, data lakes/lakehouses, analytical and NoSQL databases, streaming and other technologies used by the team.
• Can investigate unfamiliar data problems independently, identify performance or data-modeling issues, understand upstream/downstream dependencies, and recommend pragmatic solutions to customers.
• Continuously expands technical depth, keeps up with modern Data technologies and patterns, and can quickly learn new technologies as customer projects and the Data practice evolve.
   

Automat-it is committed to fostering a workplace that promotes equal opportunities for all. We firmly believe that cultivating a diverse workforce is crucial to our success. Our recruitment decisions are grounded in your experience and skills, recognizing the value you bring to our team.

About Digis
Digis is a European IT company with 250+ specialists delivering complex products for 70+ clients across the U.S., Europe, and Canada. We operate in a remote-first environment and focus on long-term collaboration, stable projects, and transparent processes.

About the Project
An AI-driven real-time market intelligence platform for the global fresh produce industry. The platform helps growers, retailers, distributors, and investors make faster, data-driven decisions.

Requirements

• 5+ years of professional experience in Data Science.
• Strong Python skills and experience with ML libraries.
• Strong SQL skills and hands-on experience with BigQuery.
• Strong experience with GCP.
• Experience with advanced machine learning frameworks and libraries such as XGBoost, LightGBM, PyTorch, or TensorFlow.
• Upper-Intermediate+ English level

Responsibilities

• Analyze complex multivariable datasets and identify key patterns and drivers.
• Build forecasting and classification models using statistical, ML, and deep learning approaches.
• Engineer features based on seasonality, weather, trade flows, and regional dynamics.
• Design and maintain model evaluation and backtesting frameworks.
• Assess model uncertainty, explainability, and performance.
• Monitor model drift and continuously improve model quality.
• Prototype LLM and AI-agent use cases.
• Collaborate with engineering teams to deploy models into production.

Advantages

• High ownership of the scientific and modeling layer.
• Work with unique global weather, market, trade, and supply-chain datasets.
• Build models that directly impact customer-facing products.
• Direct collaboration with CTO, founders, and senior leadership.
• Freedom to experiment with new approaches and technologies.
• Remote-first working environment.

We Offer

• 20 paid vacation days per year
• 5 paid sick days
• Compensation for professional courses
• English learning support
• Work equipment
• Transparent communication and processes
• Opportunity to switch projects within Digis
• Corporate psychologist

About the client

Our client is a leading European online car marketplace, connecting car buyers and sellers across the continent. The platform serves over 30 million monthly users and operates across 18 countries, making it one of the largest and most trusted digital marketplaces for vehicles in Europe.

About the role

As a Senior Data Scientist, you will be a key contributor within the client's global Data Science organization, shaping the AI and machine learning systems that sit at the core of marketplace platforms across Europe and Canada. You will design, build, and operate machine learning and GenAI systems that run in production and matter to the business — working on complex, ambiguous problems, exercising strong technical judgment, and taking responsibility for outcomes, not just models.

While this is a hands-on role, you will also influence technical direction, raise the bar for engineering practices, and enable other data scientists and engineers to succeed.

Responsibilities:

• Own ML and GenAI systems end-to-end in production, including problem framing, system design, deployment, monitoring, and continuous improvement.
• Make sound architectural and methodological decisions for ML systems on AWS, balancing robustness, observability, cost, and long-term maintainability.
• Partner closely with Product, Engineering, and Business leaders to turn ambiguous problems into ML-enabled product solutions with clear outcomes.
• Operate and evolve ML systems deployed across multiple countries and markets, accounting for differences in data distributions, regulations, and constraints.
• Mentor other data scientists through hands-on technical guidance, design reviews, and shared ownership of systems.
• Raise the bar for engineering practices across the Data Science organization and influence technical direction beyond your own projects.

Requirements:

• 5+ years of experience building and owning production ML systems end-to-end in a real-world environment.
• Advanced degree in Computer Science, Engineering, Mathematics, or a comparable field.
• Strong hands-on expertise in Python and modern ML frameworks, with the ability to ship, operate, and evolve ML systems in production.
• Solid experience with AWS and MLOps practices, including deployment, monitoring, and operating ML systems at scale.
• Practical experience applying GenAI / LLMs in applied or production contexts, with a clear understanding of trade-offs and limitations.
• Ability to influence technical decisions through clear judgment and collaboration, not formal authority.
• Experience supporting and guiding more junior data scientists through hands-on collaboration, code reviews, and technical feedback.
• Strong sense of ownership and accountability for outcomes, with a pragmatic approach to trade-offs and delivery.
• Excellent verbal and written English skills.

ORIL is looking for a Data Integration Specialist to lead and manage the data integration architecture of our AI-driven facilities management platform. The ideal candidate will possess strong expertise in optimizing data pipelines, resolving intricate integration issues, and ensuring uncompromising data quality.

About the Project

A platform shaping the future of facilities management by connecting buildings, occupants, service providers, and operatives. It streamlines soft FM services so property managers and service providers can plan, source, and manage their portfolios more efficiently and at lower cost, delivering real-time transparency across every site. The ecosystem spans FM service workflows, IoT-connected building sensors, third-party FM platforms, mobile and web clients, and event-driven microservices.
 

What You'll Do

• Design, build, and maintain scalable data pipelines and integrations across internal microservices, third-party FM platforms, IoT sensors, and external APIs.
• Design and implement ETL/ELT pipelines on Azure Databricks, building on Delta Lake and Lakehouse architecture to ingest, model, transform, and serve structured and semi-structured data.
• Define and own the data modelling layer - schemas and domain models that keep data structured and validated before ingestion, avoiding raw data sprawl.
• Work with event-driven architectures (message queues, pub/sub, event streams) to enable real-time data propagation and workflow automation.
• Define and enforce data mapping, transformation rules, and validation standards across all data flows.
• Collaborate with backend engineers, product managers, and data stakeholders to translate integration requirements into robust solutions.
• Monitor integration health, set up alerting, and diagnose pipeline failures to maintain high availability and data fidelity.
• Document integration patterns, data contracts, and API specifications for the wider engineering team.
• Contribute to platform-wide data quality, observability, and reliability standards.

What We're Looking For

• 4+ years of professional experience in a data integration, data engineering, or backend integration role.
• Hands-on experience with Azure Databricks for data engineering, pipeline orchestration, and large-scale transformations.
• Strong proficiency in Python for data engineering, pipeline development, and transformation logic.
• Strong understanding of data modelling, schema design, and data transformation patterns.
• Experience with relational and/or NoSQL databases (PostgreSQL, Cosmos DB, or similar).
• Understanding of distributed systems, microservices architectures, and cloud-native environments.
• High attention to detail and a strong sense of ownership over data quality and integration reliability.
• Comfort working across engineering, product, and business teams.
• English: Upper-Intermediate+

Nice to Have

• Experience with Azure Service Bus or similar event-driven messaging platforms (pub/sub, message queues, async workflows).
• Exposure to real-time streaming data and sensor telemetry workflows.
• Knowledge of data governance, lineage tracking, or observability tooling.

We offer:

• Competitive compensation;
• Accounting support services;
• Flexibility to work from anywhere (fully remotely or in our office in Lviv);
• Comprehensive day-off policy, including public holidays and special events;
• Education & certification budget + corporate education events;
• Wellness budget;
• An agile environment and culture that promotes personal and team development.

Sii Ukraine is a subsidiary of Sii Poland — the leader in IT, engineering, and consultancy services with over 7500 IT experts in Poland and more than 250 prestigious customers. We are looking for ambitious and top-quality professionals to join our project teams.

We are looking for a Senior Big Data Developer to join a project focused on building data-driven solutions and integrations using the Databricks platform.

You will work on backend logic, API integrations and application components within Databricks Apps, contributing to scalable and maintainable solutions that integrate with enterprise systems and procurement processes.

Responsibilities:

• Develop and maintain applications and data solutions using Databricks
• Build and configure Databricks Apps, Workflows, Delta Lake and Unity Catalog
• Develop backend logic using Python, including routing, validation and business logic
• Design and implement integrations with external systems through REST APIs
• Work with JSON, OAuth 2.0 and API-based content routing
• Develop application interfaces using Streamlit or React within Databricks Apps
• Implement and maintain data and API integration flows
• Write unit and integration tests to ensure solution quality and reliability
• Use Git and CI/CD practices for development and deployment
• Implement logging, monitoring and troubleshooting mechanisms
• Collaborate with other developers, architects and business stakeholders

• Contribute to technical decisions and continuous improvement of the solution

   

Requirements:

• Strong commercial experience as a Big Data / Data / Software Developer
• Hands-on experience with Databricks
• Practical knowledge of:
  • Databricks Apps
  • Databricks Workflows
  • Delta Lake
  • Unity Catalog
• Strong Python skills, particularly for backend development
• Experience with REST APIs, JSON and OAuth 2.0
• Understanding of API-based content routing
• Experience with Streamlit or React
• Good understanding of software engineering practices
• Experience with Git and CI/CD
• Experience writing unit and integration tests
• Experience with logging, monitoring and troubleshooting
• Ability to work independently and communicate effectively with technical and business stakeholders
• English at a level sufficient for working in an international environment
• Tax residence in Ukraine required

Nice to Have:

• Experience with E-Invoicing solutions
• Knowledge of SAP Ariba
• Experience integrating with ERP or procurement systems
• Knowledge of UBL / PEPPOL
• Experience working with enterprise integration platforms

ZentixSoft is looking for a Senior Data Solutions Consultant / Solution Architect! 🚀

Format: Project-Based / Hourly Engagement (Remote). 

💡 Why ZentixSoft?

💸 Fair Compensation: Transparent hourly/project-based rates for expert consulting hours.

⚖️ Work-Life Balance: Flexible scheduling tailored around your primary availability.

🦾 Trust & Freedom: Pure professional autonomy with zero micromanagement and no time-trackers.

🎁 Culture & Community: A collaborative environment where your architectural decisions directly drive business outcomes.

🧩 Project & Role Overview:

You will support our technical and presales teams during the discovery and solution design phase of a complex product data management initiative (handling legacy data sources, supplier onboarding, taxonomy, attribute mapping, and multi-system synchronization).

 Discovery & Workshops: Participate in client-facing discovery sessions and technical workshops to unpack legacy architecture and business constraints.

 Architecture Design: Analyze product-data flows, identify systems of record, and distinguish between integration/ETL challenges versus semantic data-mapping issues.

 Roadmap & Strategy: Define realistic target architecture options, phased implementation roadmaps, and potential pilot use cases.

 PIM / MDM & AI Integration: Evaluate approaches for supplier data onboarding, taxonomy harmonization, and assess where AI/LLM automation adds real value vs. deterministic rules/governance.

 Proposal Support: Help formulate solution proposals, architecture diagrams, and technical recommendations for business and technical stakeholders.

🎓 Our Perfect Match (Requirements):

 Domain Experience: 5+ years of hands-on experience in data architecture, system integrations, and solution design.

 PIM / MDM / Product Data Expertise: Strong background in Product Information Management (PIM), Master Data Management (MDM), product taxonomy, attribute mapping, and supplier onboarding.

 Platform Exposure: Experience with platforms such as Akeneo, Salsify, Stibo Systems, Informatica, Contentserv, Pimcore, or inriver (or similar product catalog ecosystems).

 Technical Integration & Data Quality: Familiarity with ERP/PIM/e-commerce integrations, product data standards (e.g., BMEcat), data deduplication, golden-record concepts, and legacy SQL/Access environments.

 Modern Tech Evaluation: Understanding of AI/LLM-assisted data classification and automated mapping techniques.

 Consulting & Communication: Proven experience leading technical workshops with clients, with Fluent / Confident English for client-facing communication.

📍 Engagement Details:

⏳ Format: Project-based / Hourly consulting that can be easily combined with your full time occupation.

📍 Location: 100% Remote.

We look forward to reviewing your profile:

We are looking for a Computer Vision Engineer to design and implement visual intelligence systems that power personalized digital experiences across connected devices and mobile applications.

This is a hands-on role focused on applied computer vision - from model design and training to on-device deployment. You will work at the intersection of deep learning, image and video processing, and product integration, collaborating closely with backend engineers, ML engineers, and product managers to bring vision-driven features into production and continuously optimize them for real-world usage and resource-constrained environments.

Responsibilities:

• Design, train, and deploy computer vision models for use cases such as object detection, classification, segmentation, and visual analysis;
• Build and maintain data pipelines for image and video processing, including annotation, augmentation, and dataset versioning workflows;
• Optimize models for inference on edge and mobile devices (quantization, pruning, distillation, hardware-accelerated runtimes);
• Collaborate with product and engineering teams to translate product ideas into vision-powered features;
• Implement model observability, monitoring, and retraining workflows for models running in production;
• Contribute to the architecture of a modern ML platform within a cloud-native microservices environment;
• Work with backend and mobile teams to expose model outputs via APIs and integrate them into customer-facing experiences;
• Evaluate emerging CV/AI research and assess promising methods for future implementation.

Requirements:

• 3+ years of experience in applied computer vision or machine learning with a strong CV focus;
• Strong knowledge of:
  • Deep learning for vision: CNNs, object detection and segmentation architectures (e.g. YOLO, Faster R-CNN, U-Net), and modern vision transformers;
  • Python and ML libraries such as PyTorch, TensorFlow, OpenCV;
  • Building, deploying, and maintaining CV models in production.
• Familiarity with:
  • Model optimization and on-device inference (ONNX, TensorRT, TensorFlow Lite, Core ML, OpenVINO);
  • MLOps tools (e.g. MLflow, TFX, Airflow, Vertex AI);
  • Data and video pipelines (e.g. Kafka, BigQuery, FFmpeg, GStreamer);
  • Model serving frameworks (e.g. FastAPI, TensorFlow Serving, TorchServe).
• Experience with cloud platforms (GCP, AWS, or Azure) and container-based deployments;
• Comfortable collaborating with product, engineering, and analytics teams;
• Bonus: Experience with edge AI, mobile ML, multi-modal models, or real-time video processing at scale.

We Offer:

• Remote work and flexible working hours.
• Legal support.
• Paid sick leaves.
• Paid vacations.
• Medical insurance. 
• Free English classes.

We are looking for a Game Mathematician to work on slot games within an iGaming product. This role is for someone who can turn mathematics into an engaging and well-balanced gaming experience, works confidently with probabilities, RTP, and simulations, and collaborates effectively with developers and QA.

Responsibilities
• Design and calculate mathematical models for slot games (RTP, volatility, hit rate, max win).
• Build and balance core game elements: paytables, reels / weights, wild & scatter logic, free spins, respins, multipliers, bonus features.
• Prepare complete mathematical documentation for implementation: formulas, rules, tables, edge cases, configurations.
• Run and analyze Monte Carlo simulations to validate RTP, payout distribution, and volatility.
• Prepare math validation reports.
• Support developers and QA during integration and testing phases.

Optional / Additional Responsibilities
• Research iGaming trends and competitor mechanics.
• Participate in the full game lifecycle (from concept to release).
• Analyze player behavior using mathematical and statistical data.

Requirements
• Commercial experience in slot math / casino game mathematics.
• Strong understanding of probability theory, combinatorics, and statistics.
• Clear understanding of RTP structure (base vs bonus contribution), volatility management, and feature triggers.
• Ability to produce clear and well-structured technical documentation for engineering teams.
• Strong analytical thinking, attention to detail, and ownership of results.

Nice to Have
• Confident knowledge of Python.
• Experience building simulation scripts / tools (config-driven, reproducible simulations, result export).
• Experience with jackpot / progressive models.
• Understanding of compliance and regulatory requirements in iGaming.

Skylum empowers millions of photographers to create incredible images. Our award-winning photo editing software combines AI-powered automation with full creative control. We make editing enjoyable, easy, and accessible for everyone.
You’ll join an environment where growth, learning, and creativity are encouraged. Flexible schedules, trust-based workflows, and a supportive team give you everything you need to focus on your best work.
🇺🇦 Proudly Ukrainian, Skylum stands with Ukraine through action, regularly supporting organizations that help accelerate our victory.

 

We’re looking for a Data Scientist to understand user behavior and develop machine learning models that improve user experience, product adoption, retention, and purchasing conversion.

You will join our Core Analytics team and collaborate with Data Engineers, Analytics Engineers, and Product and Marketing analysts. You will identify meaningful behavioral patterns, turn them into predictive features, and train models that support personalization and better product decisions.

Our data environment: 

Python, SQL, Apache Airflow, MySQL, ClickHouse, Parquet, AWS, and Tableau.

Why join us: 

You will help shape applied data science within Core Analytics and influence how our products respond to user needs. Your work will turn behavioral data into models and experiences that help users discover value, adopt relevant features, and make repeat purchases.

 

Required experience:
 

• Experience delivering data science or machine learning solutions with measurable product or business outcomes.
• Strong Python skills for data preparation, feature engineering, model training, and evaluation using pandas, NumPy, scikit-learn, and related libraries.
• Strong SQL skills for analyzing event-level data and building reliable modeling datasets.
• Practical experience with classification, regression, clustering, and gradient boosting methods such as XGBoost, LightGBM, or CatBoost.
• Experience identifying behavioral patterns and creating predictive features from user activity, event sequences, purchase history, and lifecycle data.
• Knowledge of the full model lifecycle: target definition, dataset preparation, hyperparameter tuning, validation, deployment, monitoring, and retraining.
• Strong understanding of statistics and experimentation, including data leakage, class imbalance, selection bias, temporal validation, and prediction calibration.
• Familiarity with deep learning concepts and a clear understanding of when neural networks offer value over simpler approaches.
• Experience with Git, reproducible workflows, and collaboration with engineers to productionize models.
• Ability to translate business goals into modeling problems and explain findings, limitations, and recommendations clearly.

Key Responsibilities: 
 

• Explore user journeys, habits, and behavioral patterns to identify signals associated with adoption, engagement, upgrades, repeat purchases, and churn.
• Define prediction targets and develop features, labels, and training datasets from product events, transactions, and customer data.
• Train, tune, and evaluate machine learning models for purchase propensity, customer lifetime value, churn risk, and next-best actions.
• Develop segmentation, recommendations, and personalization models; apply deep learning where the problem and available data justify it.
• Partner with engineers to deploy models and integrate predictions into product experiences and business workflows, including monitoring and retraining.
• Validate business impact through experiments, distinguishing predictive relationships from causal effects and balancing conversion with user satisfaction and long-term retention.

Nice to have:
 

• Hands-on experience with PyTorch or TensorFlow.
• Experience with recommendation systems, ranking, embeddings, or sequential models for user behavior.
• Knowledge of uplift modeling and causal inference to identify which interventions improve outcomes for different users.
• Experience with subscription, mobile, or marketplace products, including upgrade propensity and repeat-purchase prediction.
• Familiarity with Airflow, ClickHouse, AWS, experiment tracking, feature stores, and model monitoring.

What we offer
For personal growth:
 

• A chance to work with a strong team and a unique opportunity to make substantial contributions to our award-winning photo editing tools;
• An educational allowance to ensure that your skills stay sharp;
• English, German and Polish classes to strengthen your capabilities and widen your knowledge.

For comfort:
 

• A great environment where you’ll work with true professionals and amazing colleagues whom you’ll call friends quickly;
• The choice of working remotely or in our office space located on Podil, equipped with everything you might need for productive and comfortable work.

For health:
 

• Medical insurance;
• Twenty-one days of paid sick leave per year;

For leisure:
 

• Twenty-one days of paid vacation per year;
• Fun times at our frequent team-building activities.

What to expect when you apply
 

• An interview with our Talent Acquisition Manager
• Professional  interview
• Technical interview 
• Interview with CEO
• And finally, your job offer!

Type: Full-time, remote
Location: EU + CIS + Balkans (citizenship doesn't matter — what matters is current location and the jurisdiction from which access to client servers would occur)
Contract: 12 months, long-term, with potential for extension
Start date: Early September 2026 (ASAP)
Time zone: CET
English: B2 minimum, C1 preferred

About the Role

We're looking for a senior Data Architect to design and modernize an Azure data platform, including the data architecture underpinning GenAI/RAG/Agentic applications. This is an architecture role, not hands-on RAG/ML development — the focus is on how enterprise data and knowledge are ingested, prepared, stored, governed, and made available for retrieval and AI consumption.

Must-Haves

• Azure Cosmos DB — hands-on experience with data modeling, partitioning, indexing, RU/throughput and cost optimization, consistency, Change Feed
• Proven experience designing data platforms supporting GenAI/RAG use cases
• AI-ready data architecture experience
• A real project where the candidate designed or contributed to the data architecture supporting a RAG, GenAI, or Agentic AI application
• Azure Synapse Analytics — depth across workload patterns (Pipelines, Spark, Serverless SQL, Dedicated SQL Pools)
• Azure Databricks
• Azure SQL MI / SQL Server / T-SQL
• Azure Data Lake / Lakehouse architecture
• Azure Functions
• Experience with structured and genuinely unstructured data (documents, PDFs, scans, images, email, free text)
• Metadata/configuration-driven ingestion — reusable, scalable design for onboarding new data sources
• Demonstrated architecture ownership — real decisions made, not just pipeline implementation
• At least 4 years of recent (not dated) hands-on Azure experience
• Must be an Architect by role, not a Data Engineer

What You'll Do

• Take over an existing, problematic data platform, quickly assess it, and deliver both tactical fixes and a longer-term target architecture
• Design a reusable ingestion architecture for heterogeneous sources (databases, APIs, files)
• Make technology-neutral calls on when Databricks/Synapse/ADF are actually needed — and when they're not
• Produce detailed, implementation-ready architecture documentation for engineering teams
• Own data governance, data quality, and incremental processing
• Communicate effectively with both technical and business stakeholders

A Note on GenAI/RAG

You won't be building RAG solutions or AI agents yourself. What's required is experience designing the data architecture around such applications — how enterprise data and knowledge get ingested, processed, indexed, and made available for retrieval and AI consumption (document processing, OCR, chunking, embeddings, vector search, etc.), including where Cosmos DB fits into that picture.

Interview Process

1. Intro call
2. Technical interview (~1 hour)

Be ready to walk through 2–3 concrete project examples using: problem → options considered → your decision → rationale → implementation → result. The focus is less on definitions and more on how you think as an architect and how quickly you can turn a messy existing situation into practical direction for an engineering team.

We are looking for a Middle AI/ML 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.

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.
• Practical experience with RAG architectures, vector databases, and retrieval strategies
• 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).

We offer*:

• Flexible working format - remote, office-based or flexible
• A competitive salary and good compensation package
• Personalized career growth
• Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more)
• Active tech communities with regular knowledge sharing
• Education reimbursement
• Memorable anniversary presents
• Corporate events and team buildings
• Other location-specific benefits

*not applicable for freelancers

We are looking for a Senior AI/ML Engineer to design, develop, and enhance AI-driven solutions within the Palantir Foundry and AIP ecosystem.

In this role, you will take ownership of developing production-ready machine learning and LLM-based solutions, from understanding business needs and exploring data to designing technical approaches, implementing solutions, and integrating them into Foundry workflows. You will work closely with data engineers, business analysts, and domain experts, contribute to technical decisions, and help establish best practices for building scalable and reliable AI solutions.

Key Responsibilities:

• Design and develop AI/ML solutions for predictive analytics, classification, forecasting, automation, and AI-assisted workflows within Palantir Foundry, leveraging Python, Ontology objects, pipelines, and workflows.
• Design, implement, evaluate, and optimize LLM/NLP and RAG solutions using Palantir AIP, including prompt engineering, embeddings, retrieval strategies, vector search, and text processing.
• Own the end-to-end lifecycle of AI solutions, from experimentation and model development to deployment, monitoring, and continuous improvement.
• Collaborate with data engineers to understand data sources, ensure data quality, and design efficient and scalable data processing approaches.
• Integrate AI solutions into production workflows and optimize them for accuracy, reliability, latency, scalability, and cost-efficiency.
• Contribute to technical architecture and design decisions, including model, framework, and tool selection; proactively identify technical risks, improvement opportunities and proactively propose solutions.
• Work with business and domain stakeholders to translate business needs into practical, scalable, and production-ready AI solutions, clearly communicating technical decisions and model limitations.
• Identify technical risks, limitations, and opportunities for improvement and proactively propose solutions.

Requirements:

• 4+ years of experience in machine learning, AI engineering, applied data science, or related fields.
• Strong Python skills and hands-on experience with ML frameworks such as scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Strong practical experience with LLMs, NLP, or GenAI applications, including prompt engineering, embeddings, text processing, summarization, or similar use cases.
• Solid hands-on experience designing and implementing RAG architectures, vector databases, and retrieval strategies.
• Strong understanding of the ML lifecycle, including data preparation, feature engineering, model development, evaluation, deployment, and monitoring.
• Experience working with both structured and unstructured data, including tabular data, time series, text, and documents.
• Ability to independently analyze ambiguous problems, propose technical approaches, and drive solutions from concept to implementation.
• Strong communication skills, with the ability to explain AI concepts, technical decisions, and model 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)

We offer*:

• Flexible working format - remote, office-based or flexible
• A competitive salary and good compensation package
• Personalized career growth
• Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more)
• Active tech communities with regular knowledge sharing
• Education reimbursement
• Memorable anniversary presents
• Corporate events and team buildings
• Other location-specific benefits

*not applicable for freelancers

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

Responsibilities:
 

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

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

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

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

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

- Implement model output decoding and post-processing;

- Develop camera-related software;

- Write efficient multi-threaded code.

Requirements:
 

- Strong C++ skills;

- Solid background in computer vision;

- Practical experience with deep learning for CV and PyTorch;

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

-Experience with multi-threaded programming and camera systems.

We are looking for a Senior Data Scientist

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.

Duration: long-term