Senior Data Scientist – Causal Inference
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Job Description
Senior Data Scientist applying experiments and causal inference to optimize marketing, promotions, and customer growth. Building Databricks models for a beverage company engagement.
Responsibilities:
- Design, run, and analyze A/B tests and multivariate experiments, including sample size and power calculations, randomization, guardrail metrics, and interpretation of results
- Apply causal inference techniques to estimate the impact of marketing campaigns, promotions, pricing, and loyalty initiatives when randomization is not possible
- Measure and optimize marketing performance through incrementality, attribution, ROI/ROAS, customer lifetime value, and marketing mix modeling
- Translate growth and marketing business questions into well-defined analytical problems and experimental designs
- Develop analyses, features, and models on Databricks using notebooks, Spark, and Delta Lake
- Collaborate with Data Engineers and Analytics Engineers on data pipelines and analytical datasets
- Build predictive and segmentation models for churn, propensity, customer segmentation, targeting, and personalization
- Work with marketing, growth, commercial, Data Engineering, and Analytics Engineering stakeholders
- Communicate analytical findings to non-technical audiences
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 is nice to have
- Experience in CPG, retail, consumer goods, or beverage industries is nice to have
- Experience with Marketing Mix Modeling and media attribution is nice to have
- Experience with causal libraries and Bayesian modeling is nice to have
- Experience with MLflow and Databricks workflows/jobs is nice to have
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
- Birthday off + an extra vacation week (Mutt Week! 🏖️)
- Referral bonuses
- Annual Mutters' Trip
- Monthly Childcare Reimbursement
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