Data Scientist Semi Senior - Databricks – Causal Inference & Growth Marketing #5

Role overview A mid-level data scientist embedded with a major beverage client in Mexico, focused on measuring the true impact of growth and marketing initiatives. The role blends experimental analysis, predictive modeling, and stakeholder communication to guide decisions on acquisition, retention, and budget across campaigns, promotions, and channels. It sits at the intersection of statistics, economics, and commercial strategy within a remote-first startup culture.

Responsibilities - Design, run, and analyze A/B tests and multivariate experiments, including power calculations, randomization strategy, and guardrail metrics. - Apply causal inference techniques such as difference-in-differences, synthetic control, propensity score matching, instrumental variables, regression discontinuity, and uplift modeling to estimate campaign and promotion impact. - Measure and optimize marketing performance through incrementality, attribution, ROI/ROAS, customer lifetime value, and marketing mix modeling. - Translate business questions from growth and marketing teams into well-defined analytical problems and experimental designs. - Build analyses and models on Databricks using notebooks, Spark, and Delta Lake, partnering with data and analytics engineers on pipelines and datasets. - Develop predictive and segmentation models including churn, propensity, and customer segmentation to support targeting and personalization.

Requirements - 3+ years of experience in data science, applied statistics, econometrics, or similar analytical roles. - Hands-on experience with Databricks, including notebooks, Spark or PySpark, and Delta Lake. - Solid experience running A/B tests and online or offline experiments with rigorous interpretation. - Strong knowledge of causal inference methods, their assumptions, and practical limitations. - Strong foundations in statistics and econometrics, including hypothesis testing, regression, Bayesian and frequentist approaches, and time series. - Proficiency in Python (pandas, PySpark, statsmodels, scikit-learn) and advanced SQL, with experience applying data science to growth, marketing, or commercial problems.

Nice to have - Degree in economics, econometrics, statistics, or a related quantitative field with a focus on applied microeconomics or causal inference. - Background in CPG, retail, consumer goods, or beverage industries. - Experience with marketing mix modeling tools such as Meridian, Robyn, or PyMC-Marketing and media attribution. - Familiarity with causal libraries like DoWhy, EconML, or CausalML and Bayesian frameworks such as PyMC or Stan. - Experience with MLflow and Databricks workflows or jobs.

Benefits and work setup - Remote-first culture with work-from-anywhere flexibility. - In-company English lessons. - Wellhub or sports club stipend. - Full coverage of AWS, DBT, Google Cloud, Azure, and Databricks certifications. - Food credits, a birthday day off, an extra vacation week, and referral bonuses. - Annual company-wide team trip. - Monthly childcare reimbursement.