Jobs Data Science

Cloudfresh ⛅️ is a Global Google Cloud Premier Partner, Zendesk Premier Partner, Asana Solutions Partner, GitLab Select Partner, Hubspot Platinum Partner, Okta Activate Partner, and Microsoft Partner.

Since 2017, we’ve been specializing in the implementation, migration, integration, audit, administration, support, and training for top-tier cloud solutions. Our products focus on cutting-edge cloud computing, advanced location and mapping, seamless collaboration from anywhere, unparalleled customer service, and innovative DevSecOps.

We’re currently looking for a Data Engineer/Analyst (GCP) to join our team. In this role, you’ll design, implement, and deliver robust data platforms and analytics solutions on Google Cloud - ensuring technical excellence and client success across the full project lifecycle, from pre-sales discovery to enterprise delivery and internal projects. We’re excited to meet someone who’s ready to make a real impact.

Requirements

- Fluency in Ukrainian
- At least B2 level in English
- GCP Native Data Stack: Deep practical knowledge of BigQuery, Cloud Storage, Cloud Functions, and BigQuery ML.
- 3+ years of experience in Data Engineering
- Strong experience with Dataflow, Dataproc, Pub/Sub, Cloud Composer, or Dataform for building and maintaining optimized ETL/ELT processes in GCP.
- Proficiency in Looker / Looker Studio.
- Advanced SQL (focus on optimization and performance tuning) and Python (for API integrations and pipeline scripting).
- Comfortable in client-facing and pre-sales scenarios (live demos, requirements gathering) with the ability to translate business needs into scalable technical designs.
- A proactive, self-driven approach with high responsibility for data platform reliability and accuracy.
- Familiarity with integrating data from APIs, relational databases, NoSQL systems, and SaaS platforms.
- Ability to work seamlessly with architects, DevOps, and ML engineers to deliver end-to-end cloud solutions.

Responsibilities

- Design and build modern data platforms for enterprise clients, working with both structured and unstructured datasets at scale.
- Lead the implementation of analytics solutions ensuring strict data quality, security, and rigorous performance optimization.
- Run workshops and deliver technical presentations to stakeholders to define and refine data architectures.
- Continuously monitor and tune BigQuery cost/scalability models and ETL performance.
- Collaborate with the team during discovery phases to provide technical expertise, live demos, and solution scoping.
- Identify and explore new GCP features and agentic AI tools to automate operations and improve process analytics.

Work conditions

- Flexible Work Format: Work remotely with flexible hours, allowing you to balance your professional and personal life efficiently.
- Training with Leading Cloud Products: Access in-depth training on cutting-edge cloud solutions, enhancing your expertise and equipping you with the tools to succeed in an ever-evolving industry.
- International Collaboration: Work alongside A-players and seasoned professionals in the cloud industry.
- Expand your expertise by engaging with international markets across the EMEA and CEE regions.
- Vibrant Team Environment: Be part of an innovative, dynamic team that fosters both personal and professional growth, creating opportunities for you to advance in your career.

When applying to this position, you consent to the processing of your personal data by CLOUDFRESH for the purposes necessary to conduct the recruitment process, in accordance with Regulation (EU) 2016/679 of the European Parliament and of the Council of April 27, 2016 (GDPR).
Additionally, you agree that CLOUDFRESH may process your personal data for future recruitment processes. You can withdraw this consent at any time.

Remote – Full-time – Flexible Hours - Shifted schedule to EST (New York) timezone

About TenViz

TenViz Predictive Analytics builds AI-powered solutions that help Fortune 100 investors anticipate market shifts before they become consensus. We combine financial expertise, mathematical rigor, and AI research to deliver predictive analytics for institutional investors.

Your Expertise

• Strong interest in quantitative and algorithmic approaches
• BSc/MSc in Math, Physics or related field with solid background in statistics, linear algebra and probability theory
• Advanced Linear Algebra, Advanced Statistics, Using Linear Algebra methods in Statistical analysis
• 2+ years in data research, quantitative modeling, ML prototyping, and backtesting
• Skilled in Python, SQL, Git
• Experience with  experiment tracking, pipeline orchestration and workflow automation

Nice to have:

• Bayesian inference, reinforcement learning, or ensemble methods
• Financial econometrics, time-series forecasting, or regime modeling

What You’ll Do

• Design predictive models using probabilistic, ML, and hybrid approaches
• Analyze large-scale financial datasets to detect hidden patterns and early regime shifts
• Prototype and evaluate algorithms, from deep learning to Bayesian models
• Write production-grade research code in Python/SQL that integrates into pipelines
• Translate results into insights and themes investors can act on — not just outputs

Example projects:

• Forecasting inflation via commodity price signals
• Constructing probabilistic forecasts of currency moves around macroeconomic announcements
• Designing a Bayesian model to estimate recession probabilities from yield curve dynamics
• Designing a regime-aware ML pipeline that switches models depending on market conditions
• Creating hybrid ensembles that outperform traditional benchmarks

What’s in It for You

• Remote-first, flexible hours
• Paid vacation & holidays (after 8 months)
• Continuous learning: advanced ML/DL workshops, probabilistic methods, optimization
• Supportive, collaborative team — no micromanagement, focus on trust and creativity
• Work on real financial problems where your models influence billion-dollar decisions

About AIstats

AIstats is transforming the football data market by moving from 2D to 3D, powered by a cutting-edge Computer Vision and Machine Learning ecosystem that unlocks deep insights for both fans and professionals.

What we build

Advanced Football Analytics — a mobile app for football fans an— a mobile app for football fans and bettors that reached 100k MAU in less than a year, featuring advanced statistics powered by our proprietary ML and Computer Vision technologies.

Advanced Analytics for Football Clubs — a next-generation analytics platform helping football clubs, scouts, and agents make smarter decisions both on the pitch and in the transfer market. Thanks to our early leadership in skeletal tracking data, AIstats secured several Tier-1 club partnerships within months of launching the platform.

Responsibilities

• Develop and improve statistical and machine learning models
• Analyze large-scale datasets using Python and SQL
• Conduct statistical analysis, hypothesis testing, and experiment evaluation
• Build interpretable ML solutions and explain model behavior
• Collaborate with cross-functional teams on data-driven initiatives

Requirements

• 3+ years of experience as a Data Scientist
• Strong Python skills with NumPy, Pandas, and scikit-learn
• Solid SQL knowledge; R is a plus
• Strong understanding of probability theory and mathematical statistics
• Experience with statistical testing: t-test, χ², ANOVA, Mann-Whitney, Wilcoxon, KS test, bootstrap/permutation tests, Bonferroni and FDR corrections
• Hands-on experience with XGBoost, LightGBM, and CatBoost
• Understanding of Bayesian methods
• Experience with longitudinal/panel data, mixed/fixed/random effects models, and repeated measures
• Experience with interpretable ML approaches: GAM, SHAP, partial dependence, counterfactual analysis

Nice to Have

• Experience with deep learning: Transformers, attention mechanisms, RNN/LSTM
• Knowledge of Graph Neural Networks
• Experience with Reinforcement Learning: PPO, MAPPO, MAT, multi-agent RL
• Familiarity with Google Research Football or similar environments
• Advanced Bayesian statistics: PyMC, Stan, numpyro, MCMC, hierarchical models
• Experience with causal inference: DAGs, instrumental variables, difference-in-differences
• Knowledge of probabilistic spatial models: Markov chains, HMM/DBN, MRF/CRF, Gaussian processes

What we offer

• Fully remote work format;
• Flexible start of the working day and convenient schedule;
• Stable competitive salary pegged to the USD;
• 24 days of paid vacation and 15 additional paid days off (including sick leave, corporate holidays, and national/religious holidays);
• Friendly communication culture, great product, and transparent processes;
• Dynamic work environment with a passionate team that loves sports and technology;
• Opportunities for professional growth and career development;
• No micromanagement — just trust, autonomy, and freedom to experiment.

Join our team and help drive innovation in the football industry!

Equals5 helps pharmaceutical companies run precisely targeted ad campaigns on social platforms by identifying healthcare professionals and the consumer signals that reach them. The Identity team builds the data, scoring, and targeting systems that make this possible.

We are looking for a Data Analyst  to join the Identity team at Equals5. This is not a standard analyst role. We are building a dynamic identity resolution system where AI is deeply integrated — both as a productivity multiplier and as a core component of our data processing logic for identity resolution and LLM-based scoring.

We run enrichment against 70+ TB of data, apply AI and LLM-based scoring on top, and process around 20 million enrichment operations per day across hundreds of thousands of records.

The analytical challenges ahead are substantial: how to measure the quality of AI-driven scoring, how to validate that a change makes the system better, how to know where our money and time deliver the most value, and how to turn the questions our internal teams bring in into fast data-backed answers. You will own this side of the business, working daily alongside engineers and the product lead to turn these challenges into decisions that ship.

The main part of the role is not building dashboards — it is the data work that makes a meaningful dashboard possible in the first place. Concretely, you will be:

• Looking at data from multiple vendors side by side and deciding what to trust.
• Reconciling entities across sources that have different shapes and quality.
• Proposing and validating the logic that matches and scores them.
• Defining metrics and ground truth for questions nobody has asked before.
• Tracing why numbers don't line up across partial and sometimes contradictory sources.

Dashboards and visualizations are part of how findings get communicated, but they come at the end — the surface of the work, not its core. Most of your time is spent on what sits underneath: methodology, judgment calls on data quality, and shaping how the domain understands its own numbers.

Responsibilities

• Measurement & Evaluation Foundations: Define how we measure what matters — data quality, the accuracy of our AI scoring, how many records actually get matched to usable signals, cost per outcome. Build the feedback loops that tell us whether a change makes the system better or worse.

• Scoring Intelligence (co-owned with engineers): Partner with the engineering team on the LLM-based scoring system. You will build the evaluation framework for AI scoring, run experiments comparing prompts, models, and approaches against a baseline, and make data-backed calls on where we invest next. Our AI scoring system is actively evolving — you will shape how we measure and improve it.

• Client-related Analysis: Own the analytical side of the work that internal sales and media-buying teams bring in from pharma clients — how reachable a target audience is, how good the data looks, why a campaign performed the way it did. You turn their questions into data-backed answers they can use. Identity is a product core; the analyst works with internal teams, not with clients directly.

• Cost and Value Analysis: Turn cost and value into product decisions. Which data sources and enrichment scenarios pay off, where we overspend, what to cache, what to cut. We operate a mix of free owned data and paid external providers — the decisions have real dollar impact.

• Active AI Usage: Use Claude Code and modern AI engineering tools to multiply your own output. Automate the repetitive parts of analysis so you can focus on the decisions. We provide Claude Code licenses and expect active daily use.

• Data Quality Response: When pipeline output drifts, a provider degrades, or upstream data shifts, you notice first, diagnose the root cause, and drive the fix. You treat data quality as a product, not a ticket queue.

Requirements

• 5+ years as a Data Analyst, Analytics Engineer, or Data Scientist in a product team (not consultancy, not BI-only roles).
• Advanced SQL at TB scale — BigQuery, Snowflake, or equivalent warehouse. You can read and write complex queries on tens of terabytes without someone pre-modelling the data for you.
• Python for analysis at a working level — pandas, Jupyter, or modern equivalents. Comfortable putting together a small script or helper when an existing tool doesn't cover your case.
• Built measurement or evaluation frameworks from scratch for systems that had no baseline. You are comfortable defining what and how to measure, not consuming someone else's KPI.
• Comfort with probabilistic systems. You understand that noise is inherent and 100% accuracy is not the goal — scoring, matching, ranking.
• AI-native workflow. You already use Claude Code, ChatGPT, Copilot, or similar AI tools daily and integrate them into your analytical process.
• English: B2+ (Upper-Intermediate) or higher.

Strong plus 

• LLM evaluation experience — A/B testing AI-powered features, output scoring, prompt iteration with measurable outcomes.
• Identity resolution / identity graph experience — adtech, martech, or customer data platform context (LiveIntent, LiveRamp, Epsilon, Experian Marketing, Acxiom, Merkle, or similar).
• Healthcare or pharma data — working with NPI, HCP-level data, healthcare taxonomies, pharma targeting.

Nice to have

• Experience running controlled experiments on data pipelines.
• Prompt engineering or structured LLM prompt design.
• Prior work in AI-first product companies.

What We Offer

• Fully remote with flexible hours, with working overlap in CET ±2 hours for syncs.
• AI-Native Environment: We provide licenses for Claude Code and encourage using the bleeding edge of AI tech — both for daily work and as part of product logic.
• High-Impact Role: You will directly shape how we identify, score, and reach healthcare professionals — the core business of our domain.
• Small team, direct impact: You will work directly with Product and Engineering leads — no layers between you and the people making decisions.
• No Bureaucracy: Fast decisions, no legacy processes, focus on results.

Role Overview

We are a systematic prop trading firm operating at the intersection of CeFi and DeFi, with infrastructure deployed across 50+ exchanges and 20+ chains.
 

We are looking for a Senior Quantitative Researcher & Algorithmic Strategist to join the trading team. In this role, you will fully own the lifecycle of trading strategies — from research and backtesting to production deployment and optimisation.
 

This is a high-autonomy, high-impact position with direct collaboration with the founding team.
 

Key ResponsibilitiesStrategy Development

• Develop proprietary alpha signals or adapt existing strategies
• Design, backtest, and forward-test systematic trading strategies
• Conduct statistical validation (walk-forward, out-of-sample, regime analysis)
• Analyse capacity, decay, and correlation within the portfolio
• Prepare research documentation and performance reports
   

Engineering & Deployment

• Translate research into production-grade code (Python / C++ / Rust)
• Build algorithms for CEX market-making programs (Binance, OKX, Bybit)
• Improve order book logic (spread, inventory, execution efficiency)
• Integrate strategies into low-latency infrastructure
• Monitor live performance and conduct post-trade analysis
   

Market Microstructure & Execution

• Optimize fill rates, slippage, and latency
• Work with order book dynamics and execution logic
• Improve rebate capture and maker program performance
   

RequirementsHard Requirements (Must-have)

• Proven live trading track record (real P&L, not only backtests)
• Strong knowledge of crypto market microstructure
• Production-level coding in Python
• Working knowledge of C++ or Rust (preferred)
• Strong quantitative background (statistics, probability, time-series)
• Experience with exchange APIs (REST/WebSocket)
• Ability to own strategies end-to-end independently
   

Nice to Have

• Experience in prop trading / HFT / crypto quant firms
• Market-making algorithm experience
• Experience with CEX maker programs
• Familiarity with DEX / on-chain trading / MEV
• Experience in arbitrage strategies (CeFi–DeFi / cross-venue)
• Risk management systems (PnL limits, volatility sizing)
   

Academic Background

• Degree in Mathematics, Statistics, Physics, Computer Science, Financial Engineering, or similar
• Practical track record is prioritized over formal education
   

What We’re Looking For

• Strong ownership mindset over strategies and results
• Ability to explain and defend your edge
• Deep understanding of why strategies work (and fail)
• Attention to execution details (fees, latency, fills)
• Intellectual honesty and data-driven thinking
• Collaborative, non-ego-driven approach
   

Compensation

• Base Salary: Competitive (USD / stablecoin)
• Performance Bonus: P&L-based allocation (high-water mark model)
• Capital Allocation: Up to $100M+ per strategy (based on performance)
• Infrastructure: Full access to trading stack (50+ venues, low latency)
• Format: Remote-first, high autonomy
   

What We Offer

• Ready-to-use trading infrastructure (no need to build from scratch)
• Direct access to decision-makers
• Fast capital deployment
• Exposure to cutting-edge crypto markets (CEX + DEX)
• Collaborative research environment
• Flat team structure
   

Hiring Process

1. Call with Recruiter (30 min) — background and strategy discussion
2. Team interview (60 min) — strategy walkthrough
3. Final C-level Interview (30 min) 

Position Overview

We are seeking an experienced Data Scientist/ML Engineer specializing in neural networks, with 3+ years experience in the applied field. The ideal candidate should be a great mentor and team player; python ninja with a strong background in various neural network architectures (recurrent, convolutional, feedforward, etc.) and a deep understanding of developing, tuning, and deploying different architectures. This role also requires excellent Python programming skills and a solid foundation in data analysis and statistics. If you strive in a fast pace environment, with tight deadlines but fast professional growth, you are welcome in our team!

Key Responsibilities:

• Create statistical summaries to support hypothesis testing and data-driven decisions during EDA
• Implement data preparation and feature engineering pipelines for the models
• Plan and implement algorithms for predictive modeling.
• Provide continuous improvement of models in production
• Lead the ML-driven python coding for end to end model deliveries, support our ML-related libraries/
• Collaborate with a cross-functional team of specialists, including senior and junior data scientists, Python developers and ML engineers, data analysts, and DevOps specialists.
• Provide expertise in tuning loss functions, metrics, sample weights adjustment, hyperparameter tuning and model improvement in general.
• Generate detailed evaluation reports, summarizing key findings and presenting actionable insights to stakeholders.
• Keep up with tight deadlines, agile environment of work with evolving objectives and KPIs, having the highest level of organisation and self management to provide a full work clarity, extensive tracking and documentation of your work.

Required Skills and Qualifications:

• Minimum of 3 years of professional experience as a Data Scientist or Machine Learning Engineer.
• Proficient in building custom neural networks using PyTorch.
• Strong experience with various neural network architectures specialised for time-series modelling and tabular data predictive modelling 
• Proficiency with neural network layers (like LSTM, GRU, Conv1D, Dense, Dropout, BatchNormalization) and activation functions (ReLU, Sigmoid, Tanh, Softmax).
• Experience with time series data, including loading, cleaning, and feature engineering using libraries like pandas, numpy. Knowledge of Darts library would be a large bonus.
• Expertise in model development, including selecting and developing models from the Darts library and building custom neural network architectures.
• In-depth knowledge of Python programming and relevant libraries (e.g., Scikit-learn, Pandas, PyTorch, Shap). We want to emphasize this requirement of excellency in python, including good working OOP knowledge. We do noе accept candidates with partial experience in python, as our projects require solid programming skills for writing and supporting data science related functionalities and libraries
• Solid understanding of statistics and data analysis.
• Skilled in using and tuning loss functions such as MSE, MAE, Cross-Entropy Loss, and custom loss functions relevant to time series forecasting.
• Experience with hyperparameter optimization using libraries like Optuna or Hyperopt.
• Capable of implementing and optimizing training loops in PyTorch, including data loaders and training validation steps.
• Knowledge of time series-specific cross-validation techniques and regularization methods to prevent overfitting.
• Strong skills in evaluating model performance using metrics like MSE, RMSE, MAE, MAPE, and R-squared.
• Familiarity with backtesting functionalities for validating model performance.
• Experience in model deployment, including serialization and deployment of models for real-time services.
• Strong communication skills for presenting findings and progress to stakeholders, using visualizations created with matplotlib, seaborn, or plotly.
• Proficiency in using Git for version control and collaboration.
• Major education in Data Science, Computer Science, or a related field IS A MUST. We do not accept people with other backgrounds.
• Certifications in deep learning are preferred.
• Excellent command of spoken and written English.
• Strong self-management, time management, and organizational skills.

• Ability to work under a structured management system while independently handling creative tasks.

   

What We Offer:

• Competitive remuneration package.
• Bonuses
• Professional mentorship and guidance from experienced team members.
• Opportunities for professional growth and continuous learning.
• A dynamic and collaborative work environment.
   

About DataObrii
DataObrii is a high-tech consulting firm specializing in data science, machine learning, and AI-augmented Internet of Things. Our team comprises experienced data scientists, python engineers, devops, hardware electrical engineers, and business analysts dedicated to delivering innovative, data-driven solutions that enhance business intelligence and efficiency. We emphasize continuous improvement, keeping up with current and emerging technologies, and delivering complete timely, effective solutions.

Our Values:

• Efficiency: We employ an agile approach to ensure timely delivery of high-quality solutions. This governs fast delivery cycles, quick and efficient solutions, iteratively going from PoC developments to fully enhanced production systems.
• Professionalism: Our commitment to excellence drives us to achieve success for our clients. This includes both technical and ethical proficiency required from all our employees.
• Creativity: We utilize design thinking to develop innovative solutions that address complex business challenges.
• Care: We invest time in understanding our clients' business models to provide tailored solutions that align with their objectives.
   

Duration: 3-6 months

Must Have Skills:

• Senior-level Data Science / ML ownership — 5+ years
• ETL / data pipelines for ML — 3+ years
• Python + production ML / MVP delivery — 4+ years
• Geospatial / positional / logistics-related data - any exp

About the Project
We are looking for a Senior Data Scientist / ML Engineer to support a Berlin-based logistics technology product company on a data science project focused on building a production-ready MVP.
The project is currently in an early research / proof-of-concept stage. Initial validation has shown that the concept is technically feasible, and the next step is to bring it from research into a usable MVP that can be integrated into the existing product infrastructure.
The solution will use positional / location-based data to identify specific locations and generate insights that help drivers and improve operational efficiency.
This is not a fully isolated greenfield project. The work will be connected to existing internal systems, data pipelines, and infrastructure. Therefore, the ideal candidate should be highly autonomous, senior enough to clarify requirements directly with internal stakeholders, and able to take ownership of the solution with limited day-to-day supervision.

Responsibilities

• Own the data science and ML development work from research validation to a usable MVP.
• Build and improve data pipelines and ETL processes that feed the model.
• Work with positional / geospatial / location-based data.
• Develop, validate, and iterate on ML models that identify relevant locations and generate useful outputs for the product.
• Prepare the model and pipelines to run in production on a scheduled basis.
• Work with internal data, product, and engineering teams to understand available data, infrastructure, constraints, and business requirements.
• Translate high-level product and business goals into technical implementation steps.
• Make technical decisions independently while keeping internal stakeholders aligned.
• Document the solution, assumptions, limitations, and handover materials for the internal team.
• Ensure that the MVP can be further scaled, improved, and integrated into the company’s existing product suite.

Required Experience

• 5+ years of experience in Data Science, Machine Learning, or ML Engineering.
• Strong hands-on experience building ML models from concept to production or MVP stage.
• Experience working with ETL processes, data pipelines, data preparation, and model input pipelines.
• Strong Python experience and common data science / ML libraries.
• Experience working with large datasets and imperfect real-world data.
• Ability to work with positional, geospatial, mobility, logistics, or location-based data.
• Experience deploying or preparing ML models for production usage.
• Strong understanding of model evaluation, iteration, and practical trade-offs between accuracy, speed, and business value.
• Ability to work independently with limited day-to-day guidance.
• Strong ownership mindset: ability to clarify requirements, identify blockers, communicate with stakeholders, and drive the work forward.

Nice to Have

• Experience in logistics, transportation, mobility, route optimization, delivery, or fleet-related products.
• Experience with cloud data infrastructure such as AWS, GCP, or similar.
• Experience with scheduled production pipelines, batch processing, or workflow orchestration tools.
• Experience handing over ML solutions to internal engineering or data teams.
• Previous experience in startup or product-company environments.

We are looking for a Senior Data Scientist to help build data-driven products that significantly improve customer experience on a large-scale betting platform. You will work on high-impact machine learning models, take full ownership from research to production, and directly influence product and business decisions. This role goes beyond modeling – you will deploy models into production, run experiments, monitor results, and partner closely with product and business teams.

You will work on models and systems such as:

• Churn, retention and LTV prediction;
• Risk scoring, fraud and responsible gambling models;
• Personalization and recommendation systems;
• Uplift and causal models to drive product decision;
• Real-time ML systems powering user experience;

• Experimentation frameworks and A/B testing.

   

What you’ll be doing:

• Design, train and productionize machine learning models for product, risk and personalization use cases;
• Own models end-to-end: data exploration, feature engineering, modeling, deployment and monitoring;
• Design and analyze A/B tests to measure the real business impact of models and product changes;
• Collaborate closely with product managers, engineers and analysts to translate business problems into ML solutions;
•  Monitor model performance, data quality, drift and key business metrics after deployment;

• Communicate insights and recommendations to business and product stakeholders.

   

What we’re looking for:

• 4+ years of experience as a Data Scientist or in a similar applied ML role;
• Strong hands-on experience with Python and SQL;
• Proven experience building and deploying ML models in production;
• Experience with churn, LTV, behavioral or risk-related models;
• Solid understanding of statistics, experimentation and A/B testing;
• Ability to clearly explain technical results to non-technical stakeholders.

Nice to have:

• Experience in gaming, fintech or other high-scale consumer products;
• Experience with recommendation systems or risk/fraud models;
• Familiarity with cloud environments and basic MLOps practices;
• Experience working with real-time or near real-time ML systems.

Who We Are:

We are DATA.BET, a product IT company that develops comprehensive technological solutions for sports, esports, and virtual sports betting in a single sportsbook.
Our key products - Odds Feed, AI-powered risk management system, and fast Single Page Application - help partners gain stable profits and a competitive advantage.
Our team consists of more than 250 professionals. 

BETER is a rapidly growing product company. We create competitive content supplemented with analytics. For more than 5 years, we have been organizing sports tournaments, broadcasts, and developing our own sports community. We hold 45,000+ events per month in esports and traditional sports, and our products are represented in 150+ countries worldwide.

We are looking for a hands-on Data Science Tech Lead to join our team and drive the development of sports analytics and prediction products. This role combines deep technical expertise in machine learning and mathematical modeling with team orchestration and delivery ownership.

You will work closely with the Engineering Manager and lead a team of 4 Data Science Engineers, helping transform business needs and production challenges into scalable data science solutions.

Responsibilities:

• Collaborate with the Engineering Manager to coordinate and support a team of 4 Data Science Engineers
• Collect and analyze business requirements, production issues, and product needs, transforming them into actionable data science tasks and backlog
• Analyze large volumes of sports, esports, and trading data
• Develop and improve sports prediction models using statistics and machine learning techniques
• Conduct, automate, and evaluate data-driven experiments
• Integrate mathematical and ML models into production workflows
• Participate in hands-on development and coding activities
• Perform code reviews and maintain high engineering and data science quality standards
• Ensure efficient execution and delivery of data science initiatives within the team

Requirements:

• Previous experience in a Tech Lead or team leadership role
• Strong hands-on programming skills in Python
• Advanced knowledge of databases and data processing
• Strong expertise in machine learning, statistics, and probability theory
• Experience building and deploying mathematical or predictive models
• Upper-Intermediate+ level of English
• Ability to balance technical leadership with hands-on engineering work

What we’re looking for:

This is primarily a hands-on engineering role. The main focus is building mathematical models, writing production-level code, and delivering real business impact through data science solutions.

At the same time, the role requires strong coordination skills — the ability to distribute work effectively within the team, support engineers, and ensure successful delivery together with the Engineering Manager.

Successful probation expectations:

During the probation period, we expect the successful delivery of a sports analytics or modeling product through:

• effective team orchestration,
• hands-on contribution to development,
• and high-quality execution of data science workflows.

Ideal candidate profile:

We would love to meet someone who is:

• proactive in communication and information gathering,
• focused on data-driven decision making,
• detail-oriented and demanding when it comes to production quality and best practices.

Also relevant:

• High-load real-time data environments, such as fintech, gaming, marketing analytics, computer vision, or similar domains, working with large-scale data streams.

We offer:

• Ability to work remotely
• Competitive and timely payment for services
• Flexible work schedule
• Personal mentoring and coaching
• Team building and corporate events
• 100% paid sick leave
• 20 working days of paid vacation
• Paid child care leave

We are looking for a full-time Study-Level Biostatistician to support clinical trial projects in a CRO/FSP environment. The role is hands-on and focused on the development and review of study-level statistical documents, including Statistical Analysis Plans, statistical input to study documents, review of mock shells, and QC/review of Tables, Listings, and Figures.

The ideal candidate should have strong clinical trials biostatistics experience, excellent written and spoken English, and the ability to work independently with project teams, statistical programmers, and client stakeholders. Experience in ophthalmology and/or oncology therapeutic areas is strongly preferred.

Key Responsibilities

• Develop, review, and provide input into Statistical Analysis Plans.
• Provide statistical input into protocols, study-level documents, mock shells, and analysis specifications.
• Review Tables, Listings, and Figures for consistency with the protocol, SAP, mock shells, and statistical methodology.
• Perform statistical QC/review of outputs and provide clear comments to statistical programmers.
• Support definition of endpoints, analysis populations, visits/timepoints, subgroup analyses, and statistical methods.
• Work closely with statistical programmers to ensure correct implementation of planned analyses.
• Review and respond to client/sponsor comments related to statistical documents and outputs.
• Participate in study team meetings and communicate statistical topics clearly in English.
• Ensure study-level statistical deliverables are accurate, consistent, and compliant with applicable regulatory and project requirements.

Requirements

• Master’s degree in Biostatistics or Statistics is required.
• 3+ years of experience as a Biostatistician in clinical trials, preferably 5+ years.
• Hands-on experience developing and reviewing Statistical Analysis Plans.
• Strong experience with study-level statistical documents and clinical trial deliverables.
• Experience reviewing and performing QC of Tables, Listings, and Figures.
• Good understanding of clinical trial methodology, endpoints, analysis populations, and common statistical methods.
• Ability to work independently in a CRO, pharma, biotech, or FSP environment.
• Strong written and spoken English.
• Good communication skills and ability to interact with project teams and clients.
• Preferred therapeutic area experience: ophthalmology and/or oncology.
• Understanding of CDISC/ADaM and statistical programming deliverables is a plus, but programming is not the primary responsibility.

Nice to Have

• PhD in Biostatistics or Statistics.
• Ophthalmology and/or oncology experience.
• Experience in regulatory submissions.
• Experience working directly with sponsors/clients.

Fozzy Group is one of the largest trade industrial groups in Ukraine and one of the leading Ukrainian retailers, with over 700 outlets all around the country. It is also engaged in e-commerce, food processing & production, agricultural business, parcel delivery, logistics and banking.
 

The Foodtech team is looking for a Data Scientist to develop the Operational Analytics function for a fast growing food delivery business. In this role, you will focus on time series forecasting, regression modeling, simulation modeling, and end-to-end machine learning pipelines to support resource planning and operational decision-making.

You will be responsible for developing simulation-based models that serve as a foundation for a digital twin of operational processes, enabling scenario analysis, stress testing, and what-if simulations for capacity planning and operational optimization.

You will work closely with product, engineering, and operations teams to transform data into measurable business impact through production-ready ML and simulation solutions.
 

Job Responsibilities:

• Develop and implement time series forecasting models for resource planning (demand, capacity, couriers, delivery slots, operational load);
• Build regression and machine learning models to explain key drivers and support operational decisions;
• Apply a wide range of time series approaches from classical models (SARIMA, ETS, Prophet) and ML models (GB) to modern Deep Learning models (LSTM, Temporal CNNs, Transformers for TS);
• Design, build, and maintain end-to-end automated ML pipelines, deploy and operate models in production using AWS SageMaker;
• Orchestrate training and inference workflows with Apache Airflow;
• Analyze large-scale operational datasets and convert results into insights, forecasts, and actionable recommendations;
• Collaborate with product managers, engineers, and operations teams to define business problems and validate analytical solutions;
• Monitor model performance, forecast stability, and business impact over time.
   

Requirements:

• Bachelor’s Degree in Mathematics / Engineering / Computer Sciences / Quantitative Economics / Econometrics;
• Strong mathematical background in Linear algebra, Probability, Statistics & Optimization Techniques;
• At least 2 years working experience on Data Science;
• Experience of the full cycle of model implementation (data collection, model training and evaluation, model deployment and monitoring);
• Ability to work independently, proactively, and to decompose complex problems into actionable tasks.
   

Skills:

• Strong proficiency in Python with solid application of object-oriented programming (OOP) principles (modular design, reusable components, maintainable code);
• Solid experience in time series forecasting and regression modeling;
• Practical knowledge of:o Classical and ML forecasting techniques;
  o Statistical methods (hypothesis testing, confidence intervals, A/B testing);
• Advanced SQL skills (window functions, complex queries);
• Experience building automated ML pipelines;
• Understanding of MLOps principles (model versioning, monitoring, CI/CD for ML).
   

Nice to have:

• Hands-on experience with AWS SageMaker (training jobs, endpoints, model registry);
• Experience with Apache Airflow for data and ML workflow orchestration;
• Knowledge of Reporting and Business Intelligence Software (Power BI, Tableau);
• Experience working with large-scale production data systems.
   

What We Offer:

• Competitive salary;
• Professional & personal development opportunities;
• Being part of dynamic team of young & ambitious professionals;
• Corporate discounts for sport clubs and language courses;
• Medical insurance package.

About the Company

We are a specialized consulting company focused on Statistics, Data Science, and AI. We work across multiple industries, including the public sector, insurance, and other data-intensive domains.

For this role, we are specifically looking for a Senior Data Engineer to join projects in the public sector. These projects involve building technically advanced solutions that also create a meaningful positive impact on society.

What You'll Be Doing

• Co-design, manage, and lead consulting projects in Data Science and AI
• Independently advise stakeholders on the client side
• Perform data management, data preparation, analysis, and interpretation
• Prepare and present results, and derive actionable recommendations
• Provide technical leadership and mentor team members
• Contribute to business growth through client acquisition and sales activities
• Identify new business opportunities and maintain existing client relationships
• Represent the company externally
• Contribute to the strategic development of the company

Requirements

• Degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Business Administration, or a related quantitative field
• At least 5 years of experience in Data Science, AI, Advanced Analytics, or Consulting
• Strong expertise in at least one core area, with solid knowledge across related domains
• Experience working with databases and large-scale (Big Data) systems
• Strong programming skills in Python and R
• Excellent communication and consulting skills
• Experience interacting with stakeholders across different departments and seniority levels
• Leadership experience (ideally managing teams of 3–8 people)
• Proven project management and project leadership experience
• Strong sense of responsibility, teamwork, and problem-solving mindset
• Fast learner, adaptable, and easy to work with
• English — B2
• Ukrainian — Native
• Regular business trips within Germany are required

What We Offer

• Dynamic environment with flat hierarchies and high ownership
• Flexible working hours and remote work options
• Individual learning and development opportunities
• Diverse and interdisciplinary team

We are looking for a Data Scientist to join a risk modeling team within one of the leading banks in Ukraine. The team has been a benchmark in risk modeling for over 10 years, applying advanced Machine Learning techniques and global best practices.

We are open to considering candidates from Junior to Senior level, depending on experience and expertise.

In this role, you will develop ML solutions to minimize financial risks and optimize strategies for working with millions of customers.

Why Join the Team:

• Technology Stack: We use cloud solutions and modern development tools such as Amazon SageMaker, Amazon S3, Git, Confluence, etc.
• Scale: Working with large-scale datasets to support critical business decisions.
• Expertise: Risk models aligned with international standards and regulatory requirements.
• Professional Community: Strong Data Science community, regular internal workshops, participation in conferences, and sponsored learning.
• Automation: Proprietary system for managing the full ML lifecycle (from development to monitoring).

Key Requirements:

• 2+ year of experience in Data Science (experience in FinTech/Banking will be a strong advantage);
• Strong proficiency in Python for data analysis (NumPy, Pandas, SciPy, etc.) and experience optimizing SQL queries on large datasets;
• Hands-on experience with ML frameworks such as Scikit-learn and gradient boosting libraries (LightGBM, XGBoost, etc.);
• Experience with time series analysis and forecasting tools (Statsmodels, Prophet, etc.);
• Solid understanding of mathematics behind models (from linear regression to gradient boosting) and model evaluation techniques;
• Understanding of Big Data concepts and experience with version control tools (Git).

Nice to Have:

• Experience with Docker and/or MLflow;
• Knowledge of financial regulations (e.g., Basel III, IFRS 9).

Key Responsibilities:

• Credit Risk Modeling: Develop portfolio risk models (PD, LGD, EAD) in line with IFRS 9 requirements;
• Income & Solvency Forecasting: Build ML models to assess customer financial condition based on transactional activity;
• Credit & Behavioral Scoring: Develop classical and advanced scoring systems for automated decision-making across the customer credit lifecycle;
• Market Risk Analysis: Apply time series analysis and ML techniques to assess liquidity risk, forecast FX volatility, and model customer fund behavior;
• Fraud & Anomaly Detection: Build and maintain systems for detecting unusual transaction patterns in real time;
• ML Lifecycle: Participate in the full lifecycle of models — from idea and data collection to automated monitoring and retraining.

What We Offer:

• Opportunity to work in one of the leading banks in Ukraine;
• Official employment and 24+4 calendar days of vacation;
• Sick leave compensation;
• Competitive salary;
• Bonuses and performance-based incentives;
• Medical insurance;
• Corporate training and development programs;
• Remote work options;
• Financial support in critical situations.

QIT Software is looking for a Data Scientist for Luxury Real Estate Website. ( PART TIME)


Requirements:

- Strong experience in data analysis, research, or journalism — ideally within real estate, property, or luxury consumer markets
- Strong source-evaluation instincts: you can tell a credible, citable figure from an unverified claim and know when a second source is required
- Proficiency working inside CMS platforms (WordPress experience a plus) and comfort with AI-assisted content tools
- Excellent written English — able to turn raw data into clean, concise market commentary for a professional readership
- High attention to detail: you catch a figure that's been rounded differently across two sources, and you follow it up
- Self-directed and organised — capable of managing a rolling workload without close supervision, meeting regular weekly and quarterly deadlines
- Familiarity with the branded residences sector, luxury hospitality, or international property markets is a strong advantage
- Experience working with structured data from multiple formats (PDFs, web articles, press releases) preferred

Responsibilities:

- Review and audit existing market pages to ensure every data point is accurate, current, and properly sourced
- Cross-reference figures against primary sources — market reports, press releases, and news articles — flagging any that lack credible attribution
- Update market data using the internal AI builder and WordPress CMS on a rolling basis as new information becomes available
- Monitor inbound press releases and scraped news content, extracting relevant market figures and tagging them for ingestion
- Produce weekly market insight summaries highlighting notable data changes, emerging trends, and pipeline activity in the branded residences sector
- Compile quarterly reports synthesising market performance across key geographies, brands, and project types
- Maintain a source log that documents the origin, date, and confidence level of every figure on the platform
- Proactively identify gaps in coverage and surface recommendations for new data categories or geographies to track

🚀We are looking for an experienced Data Scientist for a full-time job to join our team.


🎯 Requirements:
-2+ years of experience as a data scientist
-BSc in Mathematics, Statistics, Computer Science, Economics, or another related field.
-Experience in using Python & SQL
-Experience with Airflow and GCP
-Experience with Git & CI/CD
-Upper Intermediate English — written and spoken
-Ability to design creative solutions for complex requirements
-Ability to learn and lead projects independently, and to work with minimal supervision with customers (tech & business)

🧩 Responsibilities:
-Conduct independent research, including defining research problems, creating research plans, designing experiments, developing algorithms, implementing code, and performing comprehensive comparisons against existing benchmarks;
-Clearly communicate your research findings to both technical and non-technical audiences
-Work on various data sources and apply sophisticated feature engineering capabilities
-Bring and use business knowledge
-Build and manage technical relationships with customers and partners.

🎯 We offer:

• Paid vacation and sick leave
• We provide the equipment you need to work comfortably (if required)
• We cover 50% of courses if you want to grow professionally
• Regular salary reviews based on your performance
• Competitive salary, depending on your experience
• Always on-time payments — no delays
• A normal, supportive team without unnecessary bureaucracy