Data Scientist, Causal Inference & Growth Marketing

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MuttdataGrowth marketing

Remote-first culture – work from anywhere!Full-TimeMiddle

Salary not disclosed

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Job Details

Experience: 3+ years of experience in Data Science, Applied Statistics, Econometrics, or similar analytical roles.
Required Skills: PythonSQLSparkA/B testingDatabricksPySpark

Have 3+ years of experience in Data Science, Applied Statistics, Econometrics, or similar analytical roles.
Have hands-on experience with Databricks notebooks, Spark/PySpark, and Delta Lake.
Have hands-on experience designing and analyzing A/B tests and online/offline experiments.
Know causal inference methods and their assumptions, limitations, and practical applications, including Difference-in-Differences, Synthetic Control, Matching, Instrumental Variables, and uplift modeling.
Have strong foundations in statistics and econometrics, including hypothesis testing, regression, Bayesian and frequentist approaches, and time series.
Be proficient in Python, including pandas, PySpark, statsmodels, and scikit-learn.
Have advanced SQL proficiency.
Have experience applying data science to growth, marketing, or commercial problems such as campaign measurement, pricing, promotions, or customer analytics.
A degree in Economics, Econometrics, Statistics, or a related quantitative field is a nice to have.
Experience in CPG, retail, consumer goods, or beverage industries is a nice to have.
Experience with Marketing Mix Modeling, media attribution, causal libraries, Bayesian modeling, MLflow, or Databricks workflows/jobs is a nice to have.

Design, run, and analyze A/B tests and multivariate experiments, including sample size and power calculations, randomization, guardrail metrics, and result interpretation.
Apply causal inference techniques to estimate the impact of marketing campaigns, promotions, pricing, and loyalty initiatives when randomization is not possible.
Measure and optimize incrementality, attribution, ROI/ROAS, customer lifetime value, and marketing mix modeling.
Translate growth and marketing business questions into 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, and customer segmentation to support targeting and personalization.

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