Type

Full-time

Experience

3+ yr

About this role

Leveraging our expertise, we build modern Machine Learning systems for demand planning and budget forecasting.

Developing scalable data infrastructures, we enhance high-level decision-making, tailored to each client.

Offering comprehensive Data Engineering and custom AI solutions, we optimize cloud-based systems.

Using Generative AI, we help e-commerce platforms and retailers create higher-quality ads, faster.

Building deep learning models, we enhance visual recognition and automation for various industries, improving product categorization, quality control, and information retrieval.

Developing recommendation models, we personalize user experiences in e-commerce, streaming, and digital platforms, driving engagement and conversions.
Amazon Web Services

Astronomer

Databricks
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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.
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).