Conduct statistical analyses (correlation, linear/multiple regression, regularization techniques, ANOVA/ANCOVA) to explain variations in sales, margin and competitiveness.
Design experiments and quasi-experiments (A/B tests, difference-in-differences, event studies, synthetic control) to measure the incremental impact of Trade and Retail Media activities.
Develop marketing mix models (MMM) and time-series models for forecasting and impact decomposition.
Apply causal methods to distinguish incremental effects from cannibalization.
Define and maintain a taxonomy of metrics (OTS/LTS, reach, frequency, share, compliance, ROAS/ROI, elasticities).
Create executive dashboards (Tableau/Power BI) with director-level storytelling.
Specify and validate data layers with IT (clean, versioned SQL).
Develop Python notebooks (pandas, numpy, scipy, statsmodels, scikit-learn) following reproducibility and versioning best practices.
Translate analyses into actionable recommendations: budget allocation, channel prioritization, assortment optimization, pricing, promotions and test plans.
Standardize metrics, definitions and analysis windows.
Ensure documentation, version control and QA (checks, robustness/sensitivity tests).
Requirements
Degree in Statistics, Industrial Engineering, Economics, Mathematics, Data Science or related fields.
Experience in analytics applied to Retail, CPG, Trade and/or Retail Media.
Strong command of statistics and probability (t-tests/χ², confidence intervals, p-values, statistical power, multiple comparisons).
Experience with regressions (OLS/GLM), ANOVA and experiments/quasi-experiments.
Proficiency in Python (pandas, numpy, scipy, statsmodels, scikit-learn) and SQL.
Experience using Tableau or Power BI for executive storytelling.
Ability to explain R², statistical significance and causality to non-technical audiences.
Benefits
Health insurance;
Dental insurance;
Results-based profit-sharing (PPR);
Corporate gym membership/discount;
Discounted purchases in our stores when using the company card;
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