{"id":3281776,"url":"https://alion.io/job/falabella-senior-data-scientist","title":"Senior Data Scientist","company":{"id":3271,"name":"Falabella","domain":"falabella.com","url":"https://alion.io/company/falabella","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":21000,"max_usd":46000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":75},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"CI/CD","optional":false},{"name":"Embeddings","optional":false},{"name":"GCP","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"AI Agents","optional":true},{"name":"GitHub","optional":true},{"name":"GitLab","optional":true},{"name":"Google BigQuery","optional":true},{"name":"Hugging Face","optional":true},{"name":"LLM","optional":true},{"name":"Time Series","optional":true},{"name":"Vertex AI","optional":true}],"status":"live","first_seen_at":"2026-10-26T16:35:00Z","employer_posted_date":null,"last_verified_at":"2026-10-26T16:35:00Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"Responsibilities: \nBuild and maintain demand and sales forecasting models across products, stores and channels to support planning, replenishment and commercial decisions.\nDevelop pricing models, including price elasticity, price optimisation, and competitive price analysis, to improve margin and sales.\nMeasure promotion effectiveness (lift, cannibalisation, ROI) and build tools that help teams plan better promotions.\nDesign experiments and use causal inference methods to measure the real impact of pricing and promotional actions.\nExplore and apply modern AI (LLMs, RAG, embeddings, GenAI tools) to improve models, automate insights and support business users.\nTake models from prototype to production with Data Engineering and ML Engineering, including monitoring and retraining.\nContribute to team best practices, code reviews and knowledge sharing.\n\nRequirements: \n3-6 years of experience in data science or applied machine learning, ideally in retail, e-commerce or CPG.\nBachelor's or Master's degree in Statistics, Mathematics, Economics, Computer Science, Engineering or a related field.\nStrong Python skills and experience with common data science libraries (e. g., pandas, polars, numpy, scikit-learn).\nExperience with the full ML lifecycle: data preparation, training, evaluation, deployment and monitoring.\nHands-on experience with demand or sales forecasting using statistical and/or machine learning methods.\nExperience in pricing analytics, such as price elasticity, price optimisation, or competitive pricing.\nExperience analysing or optimising promotions.\nSolid understanding of experimentation and causal inference (A/B testing, quasi-experimental methods).\nHands-on experience with a cloud platform for ML and data science (GCP preferred).\nPractical experience with modern AI, e. g., LLMs, RAG, embeddings or GenAI-based tools.\n\nShould have: \nProficiency in Python/SQL and comfort working with large datasets.\nExperience deploying models into production environments.\nUnderstanding of optimisation techniques (e. g., linear programming, constrained optimisation).\nExposure to CI/CD and model versioning practices.\nStrong communication skills and the ability to explain technical results to non-technical audiences.\n\nGood to have: \nFamiliarity with time-series foundation models or deep learning for forecasting.\nExperience building AI agents or LLM-powered tools for business users.\nExperience with dynamic pricing, markdown optimisation.\n\nTools and Technologies (indicative): \nML and AI: Vertex AI or equivalent ML platforms, Hugging Face.\nData: BigQuery or similar data warehouses, Cloud Storage.\nOrchestration and CI/CD: Airflow, GitHub/GitLab Actions.\nSkills\nPython, causal inference, elasticity, forecasting, optimization, pricing, promotions, 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