(Senior) Data Scientist Causal Inference â€" Pricing Platform
Econometrics, machine learning, data science, and operations research drive our algorithmic pricing platform to recommend discounts for 500K+ articles in 22 countries every day. We use causality and forecasting tools to predict the relationship between discounts, sales and demand. With techniques from operations research and the right tooling we help our pricing experts make data-informed decisions.We algorithmically propose aggregate country-level discounting strategies for all 22 Zalando markets. These proposals will maximize the benefit for Zalando while both fulfilling business goals and showing trade-offs between profitability and growth.We create Machine Learning models to forecast short- and long-term KPI performance and use Operations Research methods to create optimized pricing scenarios. As a new team experimentation with regards to methods and approaches is important and welcomed.We are looking for a person with a research mindset who will help us to evaluate, explore and productionize these ideas in our pricing products. You can expect to do a lot of prototyping with real data, doing presentations inside and outside the team and to contribute to our production systems. We are committed to help you grow in the areas of Causal Inference, Optimization, Forecasting, and Software Engineering. You have a chance to work with a high impact applied research-oriented team.You will model the causal impact of pricing algorithms such as demand forecasts and discount recommendations on customer lifetime value, revenue and profitability. Your cross-functional team, consisting of Applied Scientists and Software Engineers, plays a key role in informing price discount levels for our markets (countries) and product categories via counterfactual scenario simulations. You are closely working with colleagues in your team and across several collaboration teams and will have access to Zalando's comprehensive data sources and software tooling. We will help you grow your career by collaborating with other economists and machine learning experts across the company and exposing yourself to senior leadership.WHERE YOUR EXPERTISE IS NEEDED
- Define best practices for causal inference with other experts in both the department and beyond
- Work with the team to model the impact of pricing scenarios on aggregated levels to help our platform and stakeholders make long term optimal pricing decisions.
- Own the research and development pipeline for these models, from the initial prototype to delivered iterations to evaluating your team's delivered products.
- Help colleagues and stakeholders grow their understanding of causal inference
- Strong foundation in causal inference, both in terms of identification and estimation. Experience in empirical industrial organization, in particular demand estimation is a plus.
- Excellent educational background (minimum M.Sc) in economics, epidemiology, political science, statistics or other similar quantitative fields.
- Effective communication with technical and business stakeholders
- The ability to implement your models with a high level language (e.g. Python or R).
- Experience with SQL and large data sets.
- Working knowledge of machine learning.