Senior Data Scientist

Oct 26, 2026 Oct 28, 2026

Falabella Senior Data Scientist

Responsibilities:

Build and maintain demand and sales forecasting models across products, stores and channels to support planning, replenishment and commercial decisions.
Develop pricing models, including price elasticity, price optimisation, and competitive price analysis, to improve margin and sales.
Measure promotion effectiveness (lift, cannibalisation, ROI) and build tools that help teams plan better promotions.
Design experiments and use causal inference methods to measure the real impact of pricing and promotional actions.
Explore and apply modern AI (LLMs, RAG, embeddings, GenAI tools) to improve models, automate insights and support business users.
Take models from prototype to production with Data Engineering and ML Engineering, including monitoring and retraining.
Contribute to team best practices, code reviews and knowledge sharing.

Requirements:

3-6 years of experience in data science or applied machine learning, ideally in retail, e-commerce or CPG.
Bachelor's or Master's degree in Statistics, Mathematics, Economics, Computer Science, Engineering or a related field.
Strong Python skills and experience with common data science libraries (e. g., pandas, polars, numpy, scikit-learn).
Experience with the full ML lifecycle: data preparation, training, evaluation, deployment and monitoring.
Hands-on experience with demand or sales forecasting using statistical and/or machine learning methods.
Experience in pricing analytics, such as price elasticity, price optimisation, or competitive pricing.
Experience analysing or optimising promotions.
Solid understanding of experimentation and causal inference (A/B testing, quasi-experimental methods).
Hands-on experience with a cloud platform for ML and data science (GCP preferred).
Practical experience with modern AI, e. g., LLMs, RAG, embeddings or GenAI-based tools.

Should have:

Proficiency in Python/SQL and comfort working with large datasets.
Experience deploying models into production environments.
Understanding of optimisation techniques (e. g., linear programming, constrained optimisation).
Exposure to CI/CD and model versioning practices.
Strong communication skills and the ability to explain technical results to non-technical audiences.

Good to have:

Familiarity with time-series foundation models or deep learning for forecasting.
Experience building AI agents or LLM-powered tools for business users.
Experience with dynamic pricing, markdown optimisation.

Tools and Technologies (indicative):

Skills