Senior ML

We are looking for a Senior ML / Data Scientist to build and scale demand forecasting and pricing models at the core of the SmartPricing platform. You will work end-to-end on retail and loyalty data: from problem framing, feature engineering and model development to optimization, parallelized training and collaboration with pricing consultants and business stakeholders. Your work will directly shape data-driven pricing decisions and measurable impact on revenue and margin for leading retailers.

Responsibilities

• Design and development of models:
  demand forecasting at SKU / store / period level; price and cross-price elasticity; promo effect and post-promo dip; customer response to price and promo changes based on loyalty program data.
• P
  articipation in pricing algorithm design
  : price optimization to hit revenue / margin / traffic targets; choosing promo depth and duration under business constraints.
• Feature engineering:
  building robust features from transactions, product hierarchies, competitor prices, loyalty data; handling seasonality, holidays, promos, assortment changes.
• Performance & production:
  speeding up and parallelizing training (Xboost, CatBoost, regression models and ect) on large volumes of data; contributing to production deployment (batch / online scoring, quality control, monitoring).
• Collaboration with business:
  joint problem definition with pricing consultants and clients; preparing reports and explaining results to category managers and top management.

Requirements:

• 3–5+ years of experience in ML / Data Science.
• Hands-on experience in
  retail
  and/or projects based on transaction data (receipts, cards, customer transactions) is a must.
• Proven experience with: demand forecasting, pricing, promo analytics, revenue / margin / demand models.
• Strong
  Python
  (pandas, NumPy, SciPy, scikit-learn) and
  SQL
  (joins, window functions, query optimization).
• Deep knowledge of
  CatBoost/XBoost
  : working with categorical features;
• training setup, CPU/GPU usage; model interpretation (feature importance, SHAP, etc.).
• Experience building and using regression models (linear, regularized; panel / hierarchical models are a plus).
• Skills in optimization and parallelization of training (multiprocessing, Dask / Spark / Ray or similar approaches).
• Solid statistics: hypothesis testing, A/B testing, understanding causal effects.
• Ability to explain complex models clearly to non-technical stakeholders.

Nice to have:

• Experience in product or consulting companies working with retail and pricing.
• Models for loyalty programs: churn, CLV, next-best-offer, segmentation.
• Experience with uplift models and recommender systems.
• Understanding of MLOps practices (orchestration, MLflow, drift monitoring).

What we offer

• Work with a "live" platform that delivers measurable impact on revenue and margin for real retailers.
• Influence on the architecture and model landscape of SmartPricing, not just "support existing models".
• Close collaboration with a strong team of experts in retail, pricing and analytics.
• Flexible work format (remote / hybrid to be discussed).
• Participation in interesting pilots and scaling projects for large retail chains.
• Competitive compensation with a salary level above market for strong candidates.