Senior Data Scientist

• What you'll do:

  • Lay the foundation of ML development in the company: experiment tracking, reproducibility, model release, and quality monitoring;
  • Exploratory analysis on ClickHouse data (raw and curated layers) — trends, anomalies, and a clear picture of what powers the Intelligence product;
  • Design, train, and iterate models for Intelligence scenarios inside user cabinets: analytics and predictive product metrics (scoring, churn, forecasting, anomaly detection), personalization, and guided action scenarios;
  • LLM interface on top of analytical data: prompt design, evaluation, answer validation against ClickHouse and curated corporate data, with strict access-control compliance;
  • Feature engineering primarily in ClickHouse via SQL (aggregates and materialized views), avoiding unnecessary large-scale exports out of the cluster;
  • Ship minimal viable models; validate product impact versus heuristics and strong baselines;
  • Offline evaluation and online experiments (A/B or equivalent) on real traffic or users before full-scale rollout;
  • Partner with data engineers when new sources land or schemas evolve;
  • Align with BU product teams on hypotheses and priorities.

• What we expect:

  • Strong Python for ML and analytics;
  • Advanced SQL for analytical extracts and feature engineering in ClickHouse (aggregations, window functions, performance-aware query design);
  • Hands-on ClickHouse experience: table design, sorting keys, indexing, aggregates, materialized views;
  • Hands-on experience with Pandas, NumPy, and scikit-learn — training, validation, interpretation, and product-aligned metrics;
  • Solid probability and mathematical statistics; hypothesis testing and A/B or quasi-experiments;
  • Understanding of common ML algorithms and their limitations;
  • Understanding of columnar analytical databases;
  • End-to-end experience shipping ML models to production (from data to serving), including environments without a pre-existing ML-Ops stack;
  • MLflow, DVC, or equivalent for reproducible experiments and clean handoff to engineering;
  • Understanding of version control systems (Git) and experience using Git in team-based development;
  • Hands-on experience with AI and LLM models via APIs — prompting, model comparison, and output-quality checks for Intelligence-style scenarios grounded in ClickHouse and curated corporate data.
  • Nice to have: Experience with RAG, embeddings, or fine-tuning.

• We offer:

  • Work in the international company;
  • Hybrid working format (office/home office);
  • Corporate education — courses and trainings;
  • Voluntary health insurance after probation period;
  • Effective onboarding program for a better start;
  • Corporate events and team buildings.