AI Data Scientist

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.

Requirements:

• 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…