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Senior data scientist – causal inference & measurement (m/f/d)

München
Sixt
Data Scientist
100.000 € - 125.000 € pro Jahr
Inserat online seit: 2 Juni
Beschreibung

Senior Data Scientist – Causal Inference & Measurement (m/f/d)

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 drives efficient revenue management processes and ensures strategies are based on scientifically valid findings.


YOUR ROLE AT SIXT

* Revenue Management & Causal Measurement: Design, develop, and implement 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 development in revenue management.
* Experiment Design & Analysis: Develop and refine experimental designs using methods like DiD, RDD, synthetic control, A/B tests, and Double Machine Learning to measure effectiveness and inform policies.
* Algorithm & Tool Development: Build and maintain algorithms that integrate with production systems, ensuring accuracy and scalability in causal estimation.
* Cross-Functional Collaboration: Work with product managers, data engineers, and developers to deploy solutions leveraging causal insights for business decisions.
* Thought Leadership: Stay updated on causal inference research and mentor junior team members.


YOUR SKILLS MATTER

* Industry Experience: 5+ years in data science with a focus on causal inference, especially with sparse and volatile data, ideally in pricing or marketing.
* Causal Inference Expertise: Proven experience implementing or optimizing frameworks to measure and validate impacts of revenue and pricing strategies using causal methods.
* Technical Skills: Strong background in statistical analysis and causal inference techniques, 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, and international options.
* Office Perks: Modern gym, recreational activities, gaming area, choir, and premium employee restaurant.
* Community Engagement: One day annually to volunteer for
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