Senior Data Scientist – Causal Inference & Measurement (m/f/d), Munich
Client: SIXT Germany
Location: Munich
Job Category: Other
EU work permit required: Yes
Job Reference: 21c2a81bde8a
Job Views: 4
Posted: 30.04.2025
Expiry Date: 14.06.2025
Job Description
Join our team of data science and causal inference experts to shape next-generation pricing strategies and measure the causal impact of our initiatives! We employ state-of-the-art causal inference methods to understand and optimize pricing decisions for millions of customers. Our work not only drives efficient revenue management processes but also ensures that our strategies are grounded in robust, scientifically-valid findings.
YOUR ROLE AT SIXT
* Revenue Management & Causal Measurement: Design, develop, and implement sophisticated measurement frameworks focusing on the causal impact of price optimization strategies.
* Causal Inference Modeling: Apply advanced causal inference techniques to guide business decisions and strategy developments in revenue management.
* Experiment Design & Analysis: Develop and refine experimental designs such as DiD, RDD, synthetic control methods, A/B tests, and Double Machine Learning to measure effectiveness and inform policies.
* Algorithm & Tool Development: Build and maintain robust algorithms that integrate with production systems, ensuring accuracy and scalability in causal estimation.
* Cross-Functional Collaboration: Work closely with product managers, data engineers, and software developers to deploy solutions leveraging causal insights for business decisions.
* Thought Leadership: Stay current on research in causal inference and measurement; mentor junior team members.
YOUR SKILLS MATTER
* Industry Experience: 5+ years in data science with a focus on causal inference, experience with sparse and volatile data, ideally in pricing or marketing.
* Causal Inference Expertise: Proven track record in implementing or optimizing frameworks for impact measurement using causal inference techniques.
* Technical and Analytical Skills: Strong background in statistical analysis and methods including:
• Double Machine Learning (Double ML)
• Causal Graphs and Structural Causal Models
• Propensity Score Matching and Weighting
• Instrumental Variables (IV) and Synthetic Control Methods
• Difference-in-Differences (DiD) and Regression Discontinuity Design (RDD)
WHAT WE OFFER
* Work-life balance: 30 days vacation, up to 50% remote work, flexible hours.
* Office Perks: Modern gym, recreational activities, gaming area, employee restaurant.
* Community Engagement: One day per year volunteering with
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