Lead Applied Data Scientist (Economist)

Acting as a science lead across multiple teams, managing 2 data scientists and analysts on causal inference and experimental design, running workshops to upskill product and commercial teams on measurement thinking, and driving alignment on scientific priorities and roadmap with senior product leadership. Building reusable tools and frameworks to standardize how experiments are analyzed and decisions are made, partnering with data engineers and PMs to embed economist-driven models into products.

Leading the scientific roadmap for the lifestyle expansion at Zalando, driving strategic experimentation and long-term incrementality measurement. Partnered cross-functionally with product, engineering, and analytics to define causal measurement frameworks, evaluate sustained treatment effects, and translate findings into actionable product decisions.

Led and built a machine learning model to predict the probability of ordering for customer-level (70 million customers), extracted data insights for VP-SVP, and Management Board level stakeholders, utilizing designed A/B experiments to test the model.

Taught graduate and undergraduate courses in Econometrics, Causal Inference, and Statistics, including hands-on Python/R labs for data analysis and model implementation.

Conducted applied research on the economic impact of technological interventions; utilized large panel datasets, Stata, R, and Python for data cleaning, causal analysis, and visualization.

Developed quantitative models in R and Python to analyze large-scale household survey data; applied fixed-effects regressions, IV methods, and panel data techniques.

Analyzed wage inequality trends in the USA using Stata and R; provided policy recommendations to senior economists.

Dissertation: Advanced Causal Inference Techniques; specialized in causal inference methods for policy evaluation.