muttdata - #3 - Data Scientist Senior - Causal Inference
Requirements
• 3+ years of experience in Data Science, Applied Statistics, Econometrics, or similar analytical roles. • Hands-on experience with Databricks (notebooks, Spark/PySpark, Delta Lake) – required. • Solid, hands-on experience designing and analyzing A/B tests and online/offline experiments. • Strong knowledge of causal inference methods and their assumptions, limitations, and practical application (DiD, Synthetic Control, Matching, IV, uplift, etc.). • Strong foundations in statistics and econometrics (hypothesis testing, regression, Bayesian and frequentist approaches, time series). • Proficiency in Python (pandas, PySpark, statsmodels, scikit-learn) and advanced SQL. • Experience applying data science to growth, marketing, or commercial problems (campaign measurement, pricing, promotions, customer analytics). • Degree in Economics, Econometrics, Statistics, or related quantitative fields (ideally with a focus on applied microeconomics or causal inference). • Experience in CPG, retail, consumer goods, or beverage industries. • Experience with Marketing Mix Modeling (e.g., Meridian, Robyn, PyMC-Marketing) and media attribution. • Experience with causal libraries (DoWhy, EconML, CausalML) and Bayesian modeling (PyMC, Stan). • Experience with MLflow and Databricks workflows/jobs.
Responsibilities
• Design, run, and analyze A/B tests and multivariate experiments (sample size and power calculations, randomization, guardrail metrics, interpretation of results). • Apply causal inference techniques (Difference-in-Differences, Synthetic Control, Propensity Score Matching, Instrumental Variables, Regression Discontinuity, uplift modeling) to estimate the impact of marketing campaigns, promotions, pricing, and loyalty initiatives when randomization is not possible. • Measure and optimize marketing performance: incrementality, attribution, ROI/ROAS, customer lifetime value (CLV), and marketing mix modeling (MMM). • Translate business questions from growth and marketing teams into well-defined analytical problems and experimental designs. • Develop analyses, features, and models on Databricks (notebooks, Spark, Delta Lake), collaborating with Data Engineers and Analytics Engineers on data pipelines and analytical datasets. • Build predictive and segmentation models (churn, propensity, customer segmentation) that support targeting and personalization strategies.
Benefits
• 🌍 Remote-first culture – work from anywhere! • 🚀 In-Company English Lessons. • 💪 Wellhub or sports club stipend to stay active • 🚀 AWS, DBT, Google Cloud, Azure & Databricks certifications fully covered • 🍕 Food credits via Pedidos Ya – because great work deserves great food. • 🎂 Birthday off + an extra vacation week (Mutt Week! 🏖️) • 🤝 Referral bonuses – help us grow the team & get rewarded! • ✈️🏝️ Annual Mutters' Trip – an unforgettable getaway with the team! • 👶 Monthly Childcare Reimbursement – Because supporting families matters too
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