Data Scientist

Research and define the gold standard for evaluation methods to improve quality of the Retail online journey. Solve the most ambiguous, high-impact analytical problems by applying advanced statistical, ML, and LLM-driven methods - Design, execute, and oversee robust observational and experimental studies, advancing causal inference methodologies across large, complex data sets - Develop AI-native automated solutions to deliver prescriptive insights and proactive alerts - Drive the feature evaluation philosophy, proactively shape the product roadmap with insights, and establish a rigorous culture of experimentation Architect scalable data solutions and AI pipelines to drive exploratory analyses, reports, experimentation and insights delivery Develop and productionize ML models, causal inference, forecasting, anomaly detection, attribution, and recommendation with ownership of model health Integrate LLMs and Generative AI into core data science workflows (automated EDA, synthetic data generation, agentic pipelines, code acceleration), mitigate hallucinations, manage bias in automated pipelines to multiply team output. Influence upstream data model design, define KPI standards at the org level, and architect customized data solutions. Drive org-level decisions on tooling, methodology, and data infrastructure partnering with data engineering and ML platform teams. Mentor data scientists and drive team-wide best practices. Communicate complex technical findings to executive audiences; develop frameworks that non-technical partners can use. Work independently on sophisticated, highly visible projects; develop strategic frameworks. Masters in Statistics, Mathematics, Data Science, ML, Physics, Engineering, CS or equivalent 5+ years of experience as a Data Scientist Expert proficiency in statistical analysis, causal inference, experimentation design, observational methods (DiD, synthetic control, IV, PSM), drift analysis, predictive modeling and heterogeneous treatment effects Proficiency in SQL, Spark or equivalent; Python or R for modeling and analysis Experience building solutions with LLMs prompt engineering, RAG architectures, fine-tuning basics, and model evaluation Excellent communication skills, product intuition and customer pain point awareness PhD in Statistics, Mathematics, Data Science, ML, Physics, Engineering, Computer Science or in a quantitative field Publications or patents in causal inference, ML, or applied statistics Experience with causal ML methods (CATE estimation via econml/grf, causal forests) Experience building LLM-powered analytical tools, synthetic data generation for training or privacy preservation Experience with recommendation systems and ML ranking models, data architecture, data lakes, streaming vs. batch, and data contracts. Familiar with MLOps: CI/CD for models, Kubernetes, feature stores

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