Data Scientist (Initiative and Investment Performance Insights)

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Design and run experiments (A/B tests) and quasi-experiments (difference-in-differences, synthetic control, propensity score matching), and apply causal ML to estimate the incremental and heterogeneous effects of operational, commercial, and investment initiatives.
Build uplift / heterogeneous-treatment-effect models to learn which guests, segments, and stores respond most, and quantify effects on key metrics — purchase frequency, share-of-wallet / guest spend, average check, CLV, and retention (via survival models) — for pilot versus control groups.
Develop interpretable driver models (gradient boosting with SHAP, causal forests) that explain project performance, and rank the factors with the highest impact on results.
Build store-level potential-scoring and effect-forecasting models — probabilistic and hierarchical forecasting, transfer learning and store embeddings to borrow signal across the network — to guide and prioritize scaling decisions.
Design adaptive early-stop logic using Bayesian sequential testing, multi-armed bandits, and change point detection, to reallocate or halt rollouts as soon as the economic signal turns marginal.

Bachelor’s Degree in Mathematics / Quantitative Economics / Econometrics / Statistics / Computer Sciences / Finance;
At least 2 years working experience on Data Science;
Strong foundation in Statistics, Causal Inference, and Experimental Design — A/B testing, Hypothesis Testing, Power Analysis;
Proven experience with SQL (Window functions, CTEs, joins) and Python;
Expertise in Machine Learning, Time Series Analysis;

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