UD SSC
AI Data Scientist
Не указана
- Английский язык
- RAG
- AI
- LLM
- ML
- Python
- BI
- ERP Systems
- Русский язык
- Визуализация данных
- Алгоритмы и структуры данных
- Русский — C1 — Продвинутый
- Английский — C1 — Продвинутый
Responsibilities:
- Build and validate predictive / ML models for demand and sales forecasting, promo response, price elasticity and scenario (what-if) analysis.
- Deliver commercial and customer analytics: segmentation, ABC-XYZ, RFM, LTV, churn, cross- and up-sell, assortment and price-matrix analysis.
- Design and ship GenAI and agentic solutions — document processing (OCR/NLP), retrieval-augmented generation (RAG) over corporate data, copilots and assistants for business and process tasks.
- Automate routine analytics and reporting through LLM agents and scripting, cutting manual effort across the function.
- Apply process mining to map business processes, surface bottlenecks and control gaps, and measure cycle KPIs (touchless / STP, first-time-right).
- Co-design process automations with the Business Process and functional teams; translate findings into clear functional requirements.
- Build and maintain data pipelines and integrations with source systems (ERP K-Soft / 1С, Qlik Sense); prepare data marts, features and clean datasets.
- Deploy models into production, monitor performance and retrain; embed model outputs into business processes and Qlik Sense dashboards.
- Translate ambiguous business questions into well-scoped analytical problems and communicate results to non-technical stakeholders.
- Document models, data flows and technical specifications; ensure reproducibility and knowledge transfer.
- Train and support users on analytical tools and AI-assisted workflows.
- Research and pilot new methods, models and tools; propose where AI/ML creates measurable business value.
- 5+ years in data science, machine learning or advanced analytics, with models taken to production.
- Strong Python (pandas, scikit-learn, and ML/DL frameworks) and SQL; solid data-engineering fundamentals.
- Hands-on with GenAI / LLMs and agentic tooling — prompt design, RAG, orchestration and evaluation.
- Practical command of BI / visualisation (Qlik Sense or Power BI) and of integrating models with ERP / analytics systems.
- Proven commercial or sales analytics and/or business-process analytics (segmentation, forecasting, process mining, automation).
- Industry, retail or distribution domain experience is an advantage.
- Education: higher degree in a quantitative field (data science, computer science, mathematics, statistics or engineering).
- Languages: Russian C2; English B2/C1 (professional working proficiency).
- TBD based on interview results