#3 - Data Scientist Senior - Causal Inference

Role overview Measure the causal impact of marketing and growth initiatives, then turn the evidence into decisions about customer acquisition, retention, and revenue. This senior data science role combines applied statistics, econometrics, and commercial analytics, partnering with marketing stakeholders and data engineering teams to answer questions such as whether a campaign or promotion produced incremental results.

Responsibilities - Design experiments and analyze A/B tests, including cases where randomization is not feasible. - Estimate the effects of campaigns, promotions, pricing, and loyalty programs using methods such as difference-in-differences, synthetic controls, matching, instrumental variables, and regression discontinuity. - Assess incrementality, attribution, return on marketing spend, customer lifetime value, and marketing mix performance. - Turn business questions into analytical plans and communicate findings to nontechnical audiences. - Build analyses, features, predictive models, and customer segments using Databricks, collaborating on data pipelines and analytical datasets. - Develop churn, propensity, or segmentation models to support targeting and personalization.

Requirements - At least three years in data science, applied statistics, econometrics, or a similar analytical role. - Hands-on experience with Databricks, including notebooks, Spark or PySpark, and Delta Lake. - Practical experience designing and evaluating A/B tests or other experiments. - Strong grounding in causal inference methods, including their assumptions and limitations. - Knowledge of statistics and econometrics, including hypothesis testing, regression, Bayesian and frequentist methods, and time series. - Proficiency in Python and advanced SQL, with experience applying data science to marketing, growth, or commercial questions.

Nice to have - A degree in economics, econometrics, statistics, or another quantitative field. - Consumer goods, retail, or beverage industry experience; marketing mix modeling or media attribution expertise. - Familiarity with causal analysis libraries, Bayesian modeling tools, MLflow, or Databricks workflows.

Benefits and work setup - Remote-first arrangement. - Certification costs covered for several cloud, data, and analytics platforms. - Birthday leave, an additional vacation week, referral bonuses, monthly benefits marketplace credits, and an annual team trip.