Jobs Data Science

COMFY — провідний омніканальний ритейлер побутової техніки та електроніки в Україні з 110 магазинами, який входить до трійки найкращих платформ електронної комерції.

Ми в пошуку Lead Data Scientist, якому вже замало просто будувати моделі.
Ця роль про можливість володіти повним циклом перетворення даних у рішення — від постановки задачі до впливу на фінансовий результат компанії.

Моделі тут не «для презентацій», а вшиті в реальні бізнес-процеси: ціни, промо, запаси, асортимент, персоналізація.

Що робить цю роль особливо цікавою:

• реальний масштаб — мільйони транзакцій, складні сезонні патерни, тисячі SKU;
• чіткий бізнес-ownership — відповідальність не за точність, а за маржу, ефективність промо і ROI;
• production-first підхід — моделі одразу проєктуються для продакшну, автоматизації та масштабування;
• простір для архітектури рішень — побудова decision-layer, а не окремих моделей.

Ця роль дозволяє перейти від «я роблю ML» до «я керую тим, як бізнес приймає рішення на базі ML».

Основні задачі:

• Побудова ML-напрямку: розробка та запуск ML-моделей у продакшн (прогнозування попиту, розрахунку ефекту промо, моделі еластичності попиту, оптимізація асортименту). Побудова MLOps-процесів, CI/CD для моделей.
• Відповідальність за бізнес-результат ML-моделей: спільна робота з усіма дирекціями компанії і трансформація моделей в управлінські та автоматизовані рішення.
• MLOps та production-підхід: побудова пайплайнів навчання моделей, CI/CD для ML, моніторингу якості та дрейфу. Моніторингу якості для стабільних, масштабованих і відтворюваних моделей.
• Формування команди Data Scientists, менторинг middle/senior та встановлення стандартів для фіч, валідацій і інтерпретованості моделей.
• Взаємодія з Data Engineering та BI: забезпечення якості даних, стабільних датасетів та інтеграції результатів моделей у BI та операційні системи.

Ми очікуємо:

• 6+ років досвіду у Data Science / Machine Learning
• Обов’язковий практичний досвід запуску ML-моделей у продакшні
• Впевнене володіння Python та SQL; ML: sklearn, xgboost, lightgbm, deep learning (бажано); Time series forecasting; Recommender systems; MLOps: MLflow, Docker, Kubernetes, CI/CD
• Бізнес-орієнтоване мислення, вміння «продавати» ML бізнесу, лідерство та менторство, відповідальність за результат
• Бажантй досвід у ритейлі / FMCG / e-commerce

Ми пропонуємо:

• Реальний вплив ML на бізнес
• Великі обсяги даних і складні ритейл-кейси
• Сильну data-команду та підтримку бізнесу
• Конкурентну винагороду та розвиток

Пиши, якщо відгукнулося ✉️

Команда “Альянс Діджитал”  створює автоматизовані рішення у сфері фінтех. Ми працюємо з даними, продуктами та інженерією, щоб запускати моделі, які впливають на прибуток, ризики та клієнтський досвід. Підтримуємо професійний розвиток, менторство, обмін досвідом і створюємо умови для зростання спеціалістів у команді.

Ми шукаємо Data Scientist (Middle) для задач побудови моделей  (CRM / Антифрод)

Мова та технології

Python  ML Scikit-learn  XGBoost   LightGBM   CatBoost   SQL  Feature Store (Feast)
Fraud / CRM modeling

Твої обов’язки:

Побудова та впровадження моделей для CRM і антифроду (Response/Behaviour + Fraud Detection)

Розробка й продакшн-впровадження ML/гібридних моделей для прогнозування реакції клієнтів на CRM-комунікації (propensity/response/uplift) та виявлення шахрайських і підозрілих активностей у транзакціях/поведінці (fraud scoring/anomaly detection) з формуванням сегментів і тригерних сценаріїв, побудовою фіч із транзакційних/поведінкових/пристроєвих сигналів, доповненням rule-based шарами, а також дизайном і оцінкою інкрементального ефекту через A/B/контрольні групи
Підготовка великих обсягів даних: очищення, трансформація, агрегації, побудова time-window фіч

Оцінка й тюнінг моделей за ML-метриками (ROC, precision/recall, cost-based) та бізнес-KPI з аналізом стабільності/дрейфу

Інтеграція моделей у продакшн-середовище: batch та/або real-time скоринг
Співпраця з аналітиками, CRM/продуктовими командами та risk/fraud-функцією для формування вимог
Узгодження KPI, експериментального дизайну, інтерпретації результатів і рекомендацій для бізнесу

Вимоги:

Вища технічна освіта 
Досвід.  2-3+ роки у Data Science / ML
Практичний досвід побудови CRM response/behaviour моделей або моделей антифрода (бажано — обидва напрями)
Досвід роботи з імбалансними даними, витратами помилок (FP/FN), контрольними групами та вимірюванням ефекту
Python  на високому рівні
ML: scikit-learn, XGBoost/LightGBM/CatBoost
SQL — впевнено (витяг даних, вікна, агрегації, оптимізація запитів)
Досвід з Feature Store (Feast): дизайн сутностей/фіч, offline/online джерела, матеріалізація, backfill, контроль консистентності
Класифікація/ансамблі, anomaly detection, інтерпретація моделей (SHAP/feature importance). Git
Розуміння продакшн-циклу ML: від даних до сервісу
Досвід з API (FastAPI/Flask або аналоги) — буде плюсом
Хмари: AWS/Azure (плюс)

 

Добре мати:

Досвід у rule-based + ML системах (особливо для антифрода)
Розуміння CRM-воронки, сегментації, канальної атрибуції, маркетингових KPI
Досвід із графовими підходами (зв’язки між клієнтами/пристроями/мерчантами) для фрода — плюс
Досвід зі стрімінгом/потоковою обробкою даних (Kafka/Flink тощо) — плюс

About RiskSeal

RiskSeal is a B2B startup serving fintech clients globally. Our API-first credit risk management platform enables digital banks and consumer lenders to simplify onboarding through data-driven insights.

We are growing rapidly and value collaboration with passionate, creative professionals. Join us to innovate, contribute your expertise, and grow alongside our team.

Headquarters: Dallas, Texas, USA

European Office: Warsaw, Poland

Job Description

We are looking for a motivated Data Scientist to join our team in a fully remote role. The ideal candidate has experience in credit risk modelling, strong Python and SQL skills, as well as hands-on experience with scoring models. You will build and optimize advanced credit risk and anti-fraud models by leveraging machine learning, anomaly detection, and alternative data alongside traditional scoring. Working with client data, you will translate complex algorithmic outputs into clear, actionable insights for clients, partners, and internal teams.

What we're looking for:

• 2+ years of experience in credit risk modelling.
• Solid knowledge of classical methods (logistic regression, decision trees, scorecards) and ML (boosting, random forests, neural networks).
• Strong Python and SQL skills.
• Background in a bank, fintech, or data vendor environment.
• Experience with PD modelling, scoring, and portfolio performance metrics.
• Comfortable working end-to-end: from raw data to a validated, production-ready output.
• Able to present findings clearly to non-technical audiences, including clients and business partners.
• Upper-Intermediate English level or higher.

What you'll be doing:

• Build, test, validate, and implement predictive credit risk, scoring, and anti-fraud models.
• Analyze complex client data and alternative data streams to uncover hidden insights and deliver data-driven recommendations that guide strategic decisions.
• Continuously monitor and audit model performance (tracking drift, Gini, and PSI) to ensure long-term stability and accuracy.
• Design and experiment with new data attributes, leveraging digital footprint and transactional data to improve model predictability.
• Present analytical findings, model performance reports, and innovative ideas directly to clients, partners, and internal teams.
• Conceptualize new analytical products, scorecards, and advanced data solutions to expand RiskSeal's product offering.
• Partner closely with business, product, and tech teams to deploy models and seamlessly turn data insights into scalable, real-world value.

Nice to Have

• Experience working with alternative data sources.
• Knowledge of model governance and validation frameworks.
• Experience presenting analytical solutions directly to clients.
• Exposure to fintech products, digital lending, or embedded finance.

What We Offer

● A fully remote, full-time role in a growing product fintech company
● A fast-paced environment with minimal bureaucracy and quick decision-making
● The opportunity to work on credit risk models, scoring solutions, and real client data
● Direct collaboration with business, product, and tech teams
● The chance to contribute to analytical products that create real value for clients
● Competitive compensation and opportunities for professional growth

Overview
We are looking for a Machine Learning Engineer to design and implement intelligent systems that power personalized digital experiences across connected devices and mobile applications.

This is a hands-on role focused on applied machine learning and data engineering, ideal for someone who thrives at the intersection of ML models, data pipelines, and product integration. You will collaborate closely with backend engineers, data scientists, and product managers to bring ML-driven features into production and continuously optimize them based on real-world usage.

Responsibilities:

• Design, train, and deploy machine learning models for use cases such as personalization, recommendations, and visual analysis;
• Build and maintain scalable, production-grade data pipelines (batch and real-time);
• Collaborate with product and engineering teams to translate product ideas into ML-powered features;
• Implement model observability, monitoring, and retraining workflows;
• Optimize inference performance and model serving in resource-constrained environments (e.g., edge or mobile);
• Contribute to the architecture of a modern ML platform within a cloud-native microservices environment;
• Work with backend teams to expose model outputs via APIs and integrate them into customer-facing experiences;
• Stay up to date with recent ML/AI advancements and evaluate promising methods for future implementation.

Requirements:

• 3+ years of experience in applied machine learning or data science;
• Strong knowledge of:
  • Supervised learning, recommendation systems, or computer vision;
  • Python and ML libraries such as TensorFlow, PyTorch, scikit-learn;
  • Building, deploying, and maintaining ML models in production.
• Familiarity with:
  • MLOps tools (e.g., MLflow, TFX, Airflow, Vertex AI);
  • Data pipelines and messaging systems (e.g., Kafka, BigQuery, dbt);
  • Model serving frameworks (e.g., FastAPI, TensorFlow Serving, TorchServe, ONNX);
• Comfortable collaborating with product, engineering, and analytics teams;
• Experience with cloud platforms (GCP, AWS, or Azure) and container-based deployments;
• Bonus: Experience with edge AI, mobile ML, or video/image processing pipelines.

We are looking for a Quantitative Analyst to become the mathematical backbone of our Trading Core and Risk Tech squads. In a high-frequency trading environment processing billions in volume, success is defined by precision. 

In this role, you will design, backtest, and optimize the core mathematical models that drive our pricing engines, automated market-making algorithms, risk management frameworks (A/B/C-book optimization), and liquidation mechanics. You will sit at the intersection of advanced mathematics, data science, and high-performance software engineering, working directly with Core Product Managers and engineering teams to turn complex financial data into proprietary algorithmic advantages.

Responsibilities

• Research, design, and prototype quantitative models for pricing, risk management, and market making
• Build and maintain robust backtesting frameworks to validate the performance and safety of models before production deployment
• Write clear, comprehensive mathematical and algorithmic specifications for Backend Engineers (Trading Core squads)
• Cross functional with the R&D and Trading Ops teams
• Conduct post-incident deep dives (e.g., after major market gaps or liquidations) to identify algorithm performance gaps and optimize them

Requirements

• 3+ years of experience as a Quantitative Analyst / Researcher in fintech
• Exceptional knowledge of probability theory, stochastic calculus, time-series analysis, and financial mathematics
• Advanced proficiency in Python (NumPy, Pandas, SciPy, SK-Learn, Statsmodels) for data analysis, modeling, and backtesting
• Experience with Machine Learning
• Deep understanding of market microstructure, order book dynamics, risk metrics (VaR, Expected Shortfall), and margin/liquidation mechanisms
• SQL skills and experience working with large-scale historical market data (tick data, order logs)
• Strong logical thinking, initiative, and well-developed communication skills

Will be a plus

• Experience in CFD, Crypto CEX, Prop Trading Firm, or Hedge Fund
• Degree (MSc or PhD preferred) in a highly quantitative field: Mathematics, Physics, Quantitative Finance, Statistics, or Computer Science
• Understanding of Asset pricing models (e.g., Black-Scholes, local volatility models, Greeks management)
• Knowledge of the MetaTrader platforms (MT4/MT5)
• Experience with AI (Claude.io, Copilot, Codex)

We offer

• 20 paid vacation days per year
• 10 paid sick leave days per year
• Public holidays as per the company’s approved Public holiday list
• Medical insurance
• Opportunity to work remotely
• Professional education budget
• Language learning budget
• Wellness budget (gym membership, sports gear and related expenses)

Senior Data Scientist (AdTech)

About the Role

We are looking for an experienced Senior Data Scientist to join a long-term project in the AdTech domain. The ideal candidate has a strong background in machine learning, real-time systems, and large-scale production environments, with hands-on experience taking solutions from research and experimentation through implementation, deployment, and monitoring.

This role requires an engineer who has worked within high-traffic AdTech ecosystems and understands the challenges of building and maintaining ML-driven products operating at massive scale.

Requirements

Must Have

• 6+ years of commercial experience as a Data Scientist.
• Previous experience in the AdTech industry.
• Proven experience working with large-scale, high-load systems handling billions of requests.
• Strong experience in real-time environments and low-latency systems.
• End-to-end ownership of ML solutions, from research and experimentation to production deployment and monitoring.
• Strong knowledge of machine learning concepts, algorithms, and model evaluation techniques.
• Hands-on experience with:
  • Regression and classification models
  • Gradient boosting frameworks such as XGBoost and LightGBM
  • Recommendation systems
  • Feature engineering and model optimization
• Advanced Python skills and experience with Data Science and ML libraries.
• Strong SQL skills and experience working with large datasets.
• Experience with monitoring and alerting tools such as Grafana, Kibana, or similar.
• Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, Statistics, or a related field.
• Fluent English (C1 level or higher).
• Excellent communication and stakeholder management skills.

Nice to Have

• Experience with MLOps practices and tooling.
• Experience with streaming technologies and real-time data processing.
• Knowledge of cloud platforms and distributed systems.
• Experience with A/B testing and experimentation frameworks.
• Background in recommendation engines, bidding systems, CTR/CVR prediction, ranking, or personalization.

Responsibilities

• Design, develop, and deploy machine learning models for large-scale AdTech products.
• Build and optimize real-time ML systems operating under high traffic loads.
• Take ownership of the full ML lifecycle: research, prototyping, implementation, deployment, monitoring, and optimization.
• Work closely with Product, Engineering, and Data teams to deliver scalable data-driven solutions.
• Analyze large volumes of data to identify business opportunities and improve model performance.
• Develop monitoring and alerting strategies to ensure model reliability and performance in production.
• Drive best practices in machine learning engineering and production readiness.

Join us in building advanced forecasting and optimization capabilities for an energy trading company participating in electricity markets across multiple regions. We are looking for a Data Scientist to own the development of predictive models that directly support our trading team's market decisions. 

This is the first dedicated data science hire on the project. You will establish modeling standards, define best practices, and deliver the work yourself — across the full data science lifecycle: from time series forecasting and price prediction to battery optimization and model governance. The role requires strong technical depth and the ability to engage directly with business stakeholders in a domain where the problems are genuinely hard.

Key Responsibilities

• Design, build, and validate time-series forecasting models for facility load, market demand, and electricity price signals
• Select and tune appropriate modeling approaches (statistical, ML, and deep learning) based on the forecasting horizon, data availability, and accuracy requirements
• Develop and maintain ML pipelines on the Databricks platform using PySpark, MLflow, and Delta Lake
• Partner with domain experts to incorporate market structures, tariff rules, and grid dynamics into model features and evaluation frameworks
• Communicate model performance, assumptions, and limitations to both technical and non-technical stakeholders

Requirements

1. Time-Series Forecasting Expertise
 

You have hands-on experience with a broad range of forecasting methods and know when to reach for each one. This includes classical statistical approaches, gradient boosting methods, and modern deep learning architectures. You understand the practical challenges of time-series work: feature engineering with lags and rolling windows, handling seasonality and regime changes, proper train/validation/test splitting, and avoiding data leakage.

2. Databricks Platform Proficiency
 

You are comfortable working end-to-end in Databricks — from exploratory analysis in notebooks to productionizing models via MLflow tracking, model registry, and scheduled jobs or workflows. Experience with Delta Lake for versioned, audit-friendly data pipelines is a strong asset. Familiarity with Unity Catalog and Databricks Feature Store is a plus.

Nice to Have

• You have worked in or alongside the electricity sector and understand the data and dynamics that drive it
• Experience with graph-based machine learning or graph neural networks (GNNs)
• Experience with probabilistic forecasting (prediction intervals, quantile regression, conformal prediction)
• Exposure to real-time or near-real-time inference pipelines
• Familiarity with weather data integration for load and renewable forecasting
• Familiarity with battery storage optimization or operations research methods for energy asset dispatch

N-iX is looking for a motivated Data Scientist with strong technical skills and business acumen to help turn millions of potential data points into models and actionable insights that can drive product improvements, make customer acquisition more efficient, improve customer retention rates, and drive operating efficiencies on our production and logistics teams.

Responsibilities: 

• Use your quantitative skill set to tackle projects including time-series forecasting, demand planning and many other exciting projects.
• Work alongside the rest of the analytics and data engineering teams to ensure best data practices and showcase your skill set.
• Collaborate with functional stakeholders to identify areas of opportunity and optimization. 
• Create metrics to continuously evaluate the performance of models and maintain and improve the performance of existing machine learning models.

Requirements: 

• 3+ years of experience in data science
• Master’s Degree or extensive experience in Math, Statistics, Economics, Computer Science, or similar quantitative field.
• Advanced skills in SQL and Python
• Ability to use all your skills to complete your work, we are a small team and everyone wears multiple hats. You’ll need to use your data science, engineering and communication skills to get the job done
• Knowledge of ETL, statistical testing & analysis, experimental design
• Experience with exploratory data analysis and feature tuning
• Experience of developing, training, and evaluating machine learning models such as LightGBM, XGBoost, SVM, K-means, kNN and others
• Experience working with transactional data, customer segmentation, cohort analysis, time series analysis
• Previous experience with version control and continuous integration to a code base.
• Ability to manage cross functional projects with multiple works streams to completion
• Detail oriented, organized, and able to effectively communicate
• Seen as a key business expert and technical leader
• English Upper Intermediate 

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

Our Client is a cybersecurity software company that specializes in network and cloud security policy management and automation. It's platform helps large enterprises visualize, automate, and enforce consistent security policies across complex hybrid environments — including firewalls, cloud services, SASE, SD-WAN, and microsegmentation — while ensuring compliance and reducing risk.

Position Description:

This role combines excellent technical skills, driving multidisciplinary Data Science initiatives within complex, security policy management systems. The role drives the development of innovative, production-grade AI capabilities - including security AI agents and advanced machine learning models built on complex security data. Original thinking, deep technical rigor, intellectual agility, and exceptional problem-solving are essential.

Key Responsibilities

• Develop Data Science initiatives from problem definition through validation, production deployment, and monitoring of AI systems
• Develop security-focused AI agents and reasoning systems
• Develop advanced ML capabilities, including predictive modeling, anomaly detection, and classification
• Adapt and fine-tune LLM technologies for domain-specific security use cases

Position Requirements:

• M.Sc. in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative discipline
• At least 3 years of hands-on Data Science experience, delivering end-to-end solutions into production environments
• Deep understanding of machine learning theory and practical model behavior
• Expertise in Python and the modern Data Science ecosystem (NumPy, Pandas, Scikit-learn, PyTorch / TensorFlow, etc.)
• Solid understanding of LLM architectures and fine-tuning methodologies
• Excellent interpersonal skills and proven ability to work within multidisciplinary product teams
• Strong analytical rigor and structured problem-solving capability

PwC is a global network of more than 370,000 professionals in 149 countries that turns challenges into opportunities. We create innovative solutions in audit, consulting, tax and technology, combining knowledge from all over the world.

Join PwC's Data & AI team and help design and deliver the AI and machine learning solutions that drive real business impact. We're growing quickly due to a strong pipeline of client work, and we're hiring across multiple seniority levels — from junior to senior Data Scientists.

Python is the core skill we expect. Depending on your strengths, you may focus on machine learning, statistical modelling, NLP, computer vision, or GenAI. Many roles also include "full-solver" flexibility — contributing where needed, including experimentation, prototyping, deploying models, or working end-to-end on AI use cases using modern platforms (including Microsoft technologies where relevant).

What You'll Do:

• Build AI That Solves Real Problems: Design, develop, train, and evaluate machine learning and statistical models across a wide range of business challenges.
• Turn Messy Data Into Insights: Explore, analyse, and visualize complex datasets to uncover patterns that matter. Perform feature engineering, data preprocessing, and selection to maximize model performance.
• Push the Boundaries With GenAI & NLP: Design and implement solutions leveraging Large Language Models, Retrieval-Augmented Generation (RAG), and NLP techniques.
• Experiment Rigorously, Deploy Confidently: Collaborate with data engineers and platform teams to move models from notebook to production, monitor performance over time, and support continuous retraining and improvement.
• Work Directly With Clients: Collaborate with clients across Western Europe and the USA to understand their business challenges, frame problems as data science opportunities, and communicate findings clearly. 
• Stay Ahead of the Curve: Keep up with the latest in AI/ML research, emerging techniques, and new tools. 

Who We're Looking For:

• Programming Skills (Key Requirement): Strong programming skills in Python (e.g. scikit-learn, TensorFlow, PyTorch, pandas, NumPy, Hugging Face, LangChain). The ability to write clean, maintainable, and reproducible code is essential.
• Machine Learning: Demonstrated hands-on experience building, training, and evaluating ML models. Strong understanding of supervised and unsupervised learning techniques, model selection, hyperparameter tuning, and cross-validation.
• Statistics & Mathematics: Understanding of core statistical concepts, probability, and linear algebra.
• NLP & GenAI (Highly Valued): Experience with natural language processing, text analytics, large language models, prompt engineering, RAG architectures, or other GenAI techniques.
• Computer Vision (Nice to Have): Experience with image processing, object detection, visual reasoning, or satellite imagery analysis is an advantage.
• Data Handling: Proficiency in SQL and experience with data wrangling, cleaning, and transformation.
• Cloud & MLOps (Nice to Have): Experience with cloud platforms (preferably Azure: Databricks, Azure ML, Synapse) or similar services on AWS/GCP is an advantage.
• Professional Background: At least 2–3 years of professional experience in data science, machine learning, applied research, or similar analytical roles.
• Language Skills: English at B2 level or higher.

Policy statements:
https://www.pwc.com/ua/uk/about/privacy.html