Jobs Data Science

Job Description

• Master’s degree in Computer Science, Data Science, Applied Mathematics, or a related field.
• 7+ years of professional experience in machine learning, data science, or AI engineering.
• Proven experience as a technical lead or solution architect for ML/AI projects, with accountability for end-to-end delivery in a production environment.
• Strong proficiency in Python and the modern ML/AI ecosystem (e.g., PyTorch, Hugging Face, LangChain/LangGraph, Scikit-learn).
• Hands-on experience with data ingestion, RAG pipeline optimization, model evaluation, deployment (MLOps), and monitoring.

• Deep understanding of generative AI (LLMs, embeddings, RAG, prompt engineering, and agentic reasoning) and its practical constraints (latency, cost, safety, hallucinations).

   

Job Responsibilities

• Work with stakeholders to translate domain-specific knowledge into "Spec-Driven" ML architectures and agentic workflows.
• Design and implement solutions that combine the reasoning power of LLMs with the precision of structured knowledge (ontologies/knowledge graphs).
• Pilot the Claude Code CLI and other agentic tools to generate code, run automated tests, and maintain "Context Hygiene" within the project repository.
• Apply structured 4-phase debugging to ML pipelines, focusing on root-cause analysis of hallucinations, retrieval failures, and data drift.
• Define and automate "Skills" (prompt libraries, evaluation scripts, and deployment templates) to be re-used across multiple AI-Native pods.
• Define and implement quality gates, safety metrics, and cost-control practices for GenAI components.

• Lead by example in adopting AI-Native practices, mentoring the AI Apprentice and other team members in the "People + Agents" delivery model.

   

Department/Project Description

We are seeking a Senior ML / Data Science Lead to join our AI & Data practice and spearhead a next-generation initiative within our AI-Native Delivery Pod. In this role, you will combine high-end machine learning engineering with a revolutionary approach to software delivery. You will not just build AI; you will build AI using AI, orchestrating agentic workflows to deliver complex solutions at a velocity traditional teams cannot match.

You will focus on building practical, production-ready solutions that bridge the gap between unstructured data and structured logic using LLMs, agentic workflows, and knowledge representations (ontologies, knowledge graphs). You are expected to be an "AI-Native" pioneer, utilizing tools like Claude Code, Cursor, and custom MCP servers to automate your own development lifecycle—from data exploration and model evaluation to code generation and documentation.

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

Progresstech Ukraine is seeking for MATLAB Simulink Engineer. The primary responsibility in this role will be addressing development of airplane systems as well as creative generation of computer models of airplane systems for future airplane design, test, and analysis endeavors.

There are some general directions of our project to support the latest functional systems for Boeing aircrafts:

• Develop, maintain, update, and integrate code using MATLAB Simulink and/or C/C++ representing the mathematical and physics behavior of airplane engineering systems
• Integrate complex software components into fully functional systems
• Develop software verification plans, test procedures and test environments, execute the test procedures and documents test results to ensure software system requirements are met
• Analysis of flight data, data verifying

Basic Qualification:

• At least a bachelor's degree in engineering, computer science, mathematics, physics, etc.
• MATLAB Simulink experience
• Project documentation skills

Preferred Qualification:

• Ability to understand complex airplane systems design, functionality, and architecture
• Non-Software Engineer with the ability to understand/use/learn software languages and development tools
• C/C++ programming experience
• Knowledge of ARINC coding rules
• Technical writing

Junior Data Scientist/Quant Researcher Internship — 
Your Entry Point Into the World of High-Frequency Trading

Are you passionate about data analysis? Do you thrive on competitions, love solving unconventional problems, and want to test yourself in an industry where every nanosecond counts?

Join us for a paid internship at Atto Trading — an American HFT firm that builds high-frequency trading algorithms for U.S. financial markets.

💼 About Atto Trading — Where Engineering Meets Finance

Atto Trading is an American tech-driven trading company that builds and runs high-frequency, algorithmic strategies on financial markets.
 

We’re a small, focused team of engineers, traders, and researchers who:

• 💡 Turn ideas into profitable strategies
• 🇺🇸 Operate on U.S. financial markets
• 🧠 Constantly learn and tackle hard technical challenges together
• 🛠 Build ultra-fast, rock-solid software
   

As an intern, you won’t be stuck on side tasks — you’ll be a real part of the engineering team from day one.

🧠 During the internship, you will do:

• Analyze large datasets of market data using Polars, SQLAlchemy, scikit-learn, etc
• Build ipynb reports and interactive visualizations using Bokeh or Shiny-Plotly that provide real value
• Work with private analytical tools to create pipelines for subindicator generation
• Collaborate with engineers, traders, and analysts in a real-world business environment
   

📌 Program Details

• 🗓 Duration: 4 weeks, summer (August)
   
• 📍 Location: Ukraine in-office (Lviv) 
   
• 💼 Format: Paid internship
   
• 🏆 One top-performing intern will be offered a full-time or part-time role (depending on academic commitments)
   
• 🎁 The selected intern will receive an iPad as part of the welcome package from Atto Trading
  
   

This is a unique opportunity to gain real-world experience in high-frequency trading (HFT) — where analytics, precision, and speed determine success.

Please note: This internship is intended for candidates located in Ukraine. 

Please briefly describe your key achievements (academic, project-based, volunteer, or personal) in your CV.

We are looking for the first Data Science engineers to join our team and play a foundational role in building a private Large Language Model from the ground up. This is a rare opportunity to shape the technical direction, influence architectural decisions, and establish best practices for intelligent systems within the organization.

What you will do: 

• Design, build, and maintain classical machine learning models to support core product needs.
• Develop agent-based systems (LLM agents, multi-step agents, agent-to-agent workflows) as part of the broader LLM ecosystem.
• Conduct rigorous experiments and iterate quickly to validate approaches and improve system performance.
• Build and optimize inference pipelines, including performance tuning, monitoring, and alerting.
• Collaborate closely with Data Engineering to deploy models securely and reliably into production environments.

• Contribute to defining standards, workflows, and tooling for the new Data Science function.

   

What we expect:

• 3+ years of hands-on experience with Python.
• Strong SQL skills and understanding of data workflows.
• Solid grasp of machine learning methods and their practical applications.
• Experience building agents or agent-based systems is a strong plus.
• Initiative, ownership, and readiness to work in a greenfield environment where many solutions must be defined from scratch.

About project: Mobile marketing and audience platform

Project: sell-side omnichannel advertising platform powered by real-time mobile audiences. We deliver hundreds of millions of ads per day across thousands of global leading apps and websites. Our platform uses AI to deliver more efficient, effective and precise digital advertising campaigns. Our direct integration with thousands of mobile publishers gives us access to more than 50 billion first-party data signals per day across the globe. Marketers use these anonymized signals to understand and predict consumer behavior, identify new opportunities and fuel business growth.
 

Role Overview:

If you’re a talented Data Scientist looking to be part of a fast-growing, innovative R&D team, shape the future of decision-making in business, work on cutting-edge technologies and vast amounts of global real-time data, affect billions of mobile users, and become an industry leader - then let’s talk!

Our mobile data platform enables organizations to uncover insights and make data-driven decisions that enhance strategies and drive growth. We are seeking a Senior Data Scientist to join our Data Science & Engineering team.
 

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:

Must:

• 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)
   

Advantage:

• 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
  
  Interview process: 2-3 stages (technical/test task and interpersonal + HR).

Responsibilities:

‌

• Design, develop and optimize Large Language Models for various NLP tasks such as text generation, summarization, translation, and question-answering
• Conduct research and experiments to push the boundaries of LLM capabilities and performance
• Collaborate with cross-functional teams (engineering, product, research) to integrate LLMs into product offerings
• Develop tools, pipelines and infrastructure to streamline LLM training, deployment and monitoring
• Analyze and interpret model outputs, investigate errors/anomalies, and implement strategies to improve accuracy
• Stay current with the latest advancements in LLMs, NLP and machine learning research
• Communicate complex technical concepts to both technical and non-technical stakeholders

Requirements:

‌

• MS or PhD degree in Computer Science, Data Science, AI, or a related quantitative field
• 4+ years of hands-on experience developing and working with deep learning models, especially in NLP/LLMs
• Expert knowledge of Python, PyTorch, TensorFlow, and common deep learning libraries
• Strong understanding of language models, attention mechanisms, transformers, sequence-to-sequence modeling
• Experience training and fine-tuning large language models
• Proficiency in model deployment, optimization, scaling and serving
• Excellent problem-solving, analytical and quantitative abilities
• Strong communication skills to present technical information clearly
• Ability to work collaboratively in a team environment
• Fluency in Ukrainian and English

Preferred:

• Research experience in LLMs, NLP, machine learning
• Experience working with multi-modal data (text, image, audio)
• Knowledge of cloud platforms like AWS, GCP for model training
• Understanding of MLOps and production ML workflows
• Background in information retrieval, knowledge graphs, reasoning

Data Science Lead

Full-time | Remote

About Us

ExpoPlatform is a UK-based product company that builds a SaaS platform for large-scale events, exhibitions, and online communities. We’re a distributed team — people work from the UK, Ukraine, Poland, Brazil, Pakistan, and other locations — so communication is mostly in English, though some internal teams also use Ukrainian. The platform itself is mature, high-load, and cloud-native — fully hosted on AWS. We are expanding our AI and data science capabilities and looking for a strong Data Science Lead to drive this direction.

About the Role

We’re looking for a Data Science Lead with a strong background in Data Science, Leadership and Operations to join our team. In this role, you will lead and execute AI-driven initiatives, build scalable data solutions, and shape the data science strategy. You will collaborate closely with product, engineering, and business teams, taking ownership of the full ML lifecycle — from data preparation and experimentation to deployment, monitoring, and retraining — while ensuring data quality, governance, and responsible AI practices.

Responsibilities

• Lead and execute AI and data science initiatives, including recommendation systems, analytics solutions, and intelligent automation
• Define and shape data science strategy aligned with business and product goals
• Own the full ML lifecycle: data preparation, experimentation, deployment, monitoring, and retraining
• Build and maintain high-performance REST APIs and asynchronous services using FastAPI
• Design and maintain scalable cloud-native solutions on AWS
• Collaborate with Product, Engineering, and business stakeholders to translate data insights into business value
• Ensure data quality, governance, and responsible AI practices
• Contribute to architectural and infrastructure decisions.

Requirements

• 4+ years of hands-on experience in Data Science / Applied Machine Learning
• Strong Python expertise (Python 3.13 as primary language)
• Solid experience with Pandas, NumPy, Scikit-Learn
• Strong experience with FastAPI for high-performance REST API development
• Production experience with AWS services, including AWS Lambda and serverless infrastructure
• Strong experience with Terraform for Infrastructure as Code (IaC) and infrastructure provisioning
• Experience working with analytical databases (ClickHouse is a strong advantage)
• Strong SQL skills and experience with large-scale data processing
• Experience leading projects and working cross-functionally
• English — Upper-Intermediate or higher.

Nice to Have

• Experience with LLMs (OpenAI, Amazon Bedrock, Hugging Face)
• Experience with vector databases
• Experience building AI-driven search or chatbot systems
• Understanding of agile methodologies and end-to-end data science project management

Technology Stack

• Python 3.13
• FastAPI
• AWS (including AWS Lambda)
• ClickHouse
• Terraform

What We Offer

• The opportunity to architect and build scalable, cloud-native systems 
• Work with cutting-edge AI and data science technologies
• Career growth within an international and fast-scaling SaaS company
• Competitive salary and long-term financial stability

• Paid vacation, sick leave, and public holidays
• Semi-flexible working hours (Mon–Fri, 8 hours)
• Fully remote and distributed team environment
• Free English courses and education budget.

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. 

Responsibilities

Model Fine-Tuning and Deployment:

Fine-tune pre-trained models (e.g., BERT, GPT) for specific tasks and deploy them using Amazon SageMaker and Bedrock.

RAG Workflows:

Establish Retrieval-Augmented Generation (RAG) workflows that leverage knowledge bases built on Kendra or OpenSearch. This includes integrating various data sources, such as corporate documents, inspection checklists, and real-time external data feeds.

MLOps Integration:

The project includes a comprehensive MLOps framework to manage the end-to-end lifecycle of machine learning models. This includes continuous integration and delivery (CI/CD) pipelines for model training, versioning, deployment, and monitoring. Automated workflows ensure that models are kept up-to-date with the latest data and are optimized for performance in production environments.

Scalable and Customizable Solutions:

Ensure that both the template and ingestion pipelines are scalable, allowing for adjustments to meet specific customer needs and environments. This involves setting up RAG workflows, knowledge bases using Kendra/OpenSearch, and seamless integration with customer data sources.

End-to-End Workflow Automation:

Automate the end-to-end process from user input to response generation, ensuring that the solution leverages AWS services like Bedrock Agents, CloudWatch, and QuickSight for real-time monitoring and analytics.

Advanced Monitoring and Analytics:

Integrated with AWS CloudWatch, QuickSight, and other monitoring tools, the accelerator provides real-time insights into performance metrics, user interactions, and system health. This allows for continuous optimization of service delivery and rapid identification of any issues.

Model Monitoring and Maintenance:

Implement model monitoring to track performance metrics and trigger retraining as necessary.

Collaboration:

Work closely with data engineers and DevOps engineers to ensure seamless integration of models into the production pipeline.

Documentation:

Document model development processes, deployment procedures, and monitoring setups for knowledge sharing and future reference.

Must-Have Skills

Machine Learning: Strong experience with machine learning frameworks such as TensorFlow, PyTorch, or Hugging Face Transformers.

MLOps Tools: Proficiency with Amazon SageMaker for model training, deployment, and monitoring.

Document processing: Experience with document processing for Word, PDF, images.

OCR: Experience with OCR tools like Tesseract / AWS Textract (preferred)

Programming: Proficiency in Python, including libraries such as Pandas, NumPy, and Scikit-Learn.

Model Deployment: Experience with deploying and managing machine learning models in production environments.

Version Control: Familiarity with version control systems like Git.

Automation: Experience with automating ML workflows using tools like AWS Step Functions or Apache Airflow.

Agile Methodologies: Experience working in Agile environments using tools like Jira and Confluence.

Nice-to-Have Skills

LLM: Experience with LLM / GenAI models, LLM Services (Bedrock or OpenAI), LLM abstraction like (Dify, Langchain, FlowiseAI), agent frameworks, rag.

Deep Learning: Experience with deep learning models and techniques.

Data Engineering: Basic understanding of data pipelines and ETL processes.

Containerization: Experience with Docker and Kubernetes (EKS).

Serverless Architectures: Experience with AWS Lambda and Step Functions.

Rule engine frameworks: Like Drools or similar

If you are a motivated individual with a passion for ML and a desire to contribute to a dynamic team environment, we encourage you to apply for this exciting opportunity. Join us in shaping the future of infrastructure and driving innovation in software delivery processes.

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:
- remote time job, B2B contract
- 12 sick leaves and 18 paid vacation business days per year
- Comfortable work conditions (including MacBook Pro and Dell monitor on each workplace)
- Smart environment
- Interesting projects from renowned clients
- Flexible work schedule
- Competitive salary according to the qualifications
- Guaranteed full workload during the term of the contract
 

Requirements:

• 5+ experience in applying data science and machine learning techniques
• Python programming using classic scientific stack (numpy, pandas, scipy, scikit-learn, pytorch, lightgbm, matplotlib or any other visualization library)
• Understanding of classic machine learning algorithms and theory (supervised/unsupervised learning techniques, metrics and visualizations for interpretation of model performance, time-series analysis, anomaly detections, recommendation models, hyperparameter optimization)
• NLP (NLP techniques, GenAI, LLM finetuning)
• Statistics and optimization basic understanding
• Data manipulation and cleaning skills (fill missing entries / filter dataframes / apply certain changes to columns)
• Prior experience with Python web frameworks would be great, but not critical (FastAPI, Flask or anything else)
• Dockerization technologies
• Cloud solutions (any of AWS, GCP, Azure)
• Understanding of prompt engineering or experience with building scripts or applications around LLMs
• Proficient with SQL (Postgres, Snowflake), NoSQL (ElasticSearch, Mongo(optional))
• Monitoring services
   
• Responsibilities:
  Work on time series forecasting problems with real, complex data. Implement features, interpret their importance, train models and incorporate them in the production pipeline.
• Work on LLM-based solutions, which use techniques like Contextual RAG (Retrieval Augmented Generation), LLM as Judge, semantic search, chatbot services, etc.
• Create anomaly detection models and clustering methods for non-stationary, “dirty”, semi-organized data

Один із провідних продуктових IT-холдингів України запрошує до команди Data Scientist!
Якщо тобі цікаво створювати ML-рішення, впроваджувати рекомендаційні системи та працювати з масштабними даними у професійному середовищі - будемо раді знайомству 🚀
 

Обов’язки:

• Розробка та навчання моделей машинного навчання (регресія, класифікація, кластеризація).
• Побудова й удосконалення рекомендаційних систем для бізнес-задач.
• Написання ефективного та масштабованого коду на Python.
• Написання та оптимізація SQL-запитів для отримання та аналізу даних.
• Співпраця з командою та дотримання кращих практик у версійному контролі (Git) та тестуванні.
   

Вимоги:

• 4+ роки досвіду у Data Science/Machine Learning.
• Впевнене володіння Python, SQL, Git.
• Глибоке розуміння методів ML (регресія, класифікація, кластеризація).
• Досвід розробки production-рішень з якісним та масштабованим кодом.
   

Буде плюсом:

• Досвід в побудові рекомендаційних систем.
• Знання тестування та MLOps-підходів.
   

Ми пропонуємо:

• Конкурентну зарплату та стабільні виплати.
• Медичне страхування.
• Гнучкий графік (старт з 8:00 до 11:00).
• L&D центр, self-learning бібліотека, доступ до внутрішніх та зовнішніх курсів.
• Програму ротації та кар’єрного розвитку.
• Підтримку ментального здоров’я (групові та індивідуальні сесії з психологом).
• Дружню команду експертів та продукти світового рівня.

SoftSvit — продуктова IT-компанія, яка займається не тільки розробкою, але і просуванням власного продукту. Зараз ми шукаємо у свою команду Risk manager-a.

Вимоги:

• Досвід роботи в е-комерції чи антифроді буде плюсом;
• Відповідальність;
• Вміння на базовому рівні працювати з Excel та Google Документами;
• Гнучке мислення, уважність;
• Досвід роботи з великим обсягом інформації;
• Здатність аналізувати отримані дані та розставляти пріоритети.

Обов’язки:

• Оперативна перевірка запитів щодо виплати коштів клієнтам;
• Аналіз логів, виявлення нестандартних закономірностей та розбіжностей;
• Ідентифікація особи клієнтів;
• Комунікація з саппортом із платіжних труднощів та питань клієнтів;
• Моніторинг статистичних показників.

Ми пропонуємо:

• Сучасний офіс поруч з метро;
• Графік роботи: 2/2 з 9:00—21:00 (2 денні зміни, 2 вихідних, 2 нічні зміни);
• Кафе на території офісу;
• Можливість кар'єрного і професійного зростання;
• Офіційне оформлення, оплачувана відпустка та лікарняні;
• Піца, напої, настільні ігри за рахунок компанії;
• Ми за спорт, тому щотижня граємо у волейбол;
• Кава, чай, печиво, фрукти та гарний настрій щодня.

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