lavori da Wissenschaftlicher Mitarbeiter in località Germania

We are partnering with a leading global mobility company to find a skilled and motivated Data Science and Causal Inference Expert to join their team. Help shape next-generation pricing strategies and measure the causal impact of their initiatives using state-of-the-art causal inference methods. Your work will optimize pricing decisions for millions of customers and ensure strategies are grounded in scientifically-valid findings.

• Revenue Management & Causal Measurement: Design, develop, and implement sophisticated measurement frameworks that focus 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 using techniques such as Difference-in-Differences (DiD), Regression Discontinuity Design (RDD), synthetic control methods, A/B tests, and Double Machine Learning to measure effectiveness and inform policy decisions.
• Algorithm & Tool Development: Build and maintain robust algorithms that integrate seamlessly 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 end-to-end solutions that leverage causal insights to drive business decisions.
• Thought Leadership: Stay up to date on the latest research in causal inference and measurement, while mentoring and guiding junior team members.

• Industry Experience: 5+ years in data science with a focus on causal inference, ideally within pricing and/or marketing domains, with experience in handling sparse and volatile data.
• Causal Inference Expertise: Proven track record of implementing and optimizing frameworks to measure and validate the impact of revenue management systems and pricing strategies using causal inference techniques.
• Technical and Analytical Skills: Strong background in statistical analysis and causal inference methods
• Double Machine Learning (Double ML): Familiarity with Double/Debiased ML methods that combine machine learning models to estimate causal effects.
• Causal Graphs and Structural Causal Models: Proficiency in using Directed Acyclic Graphs (DAGs) for causal identification.
• Propensity Score Matching and Weighting: Advanced application of propensity score techniques to estimate treatment effects.
• Instrumental Variables (IV) and Synthetic Control Methods: Experience with IV and synthetic controls for causal impact estimation in observational settings.
• Difference-in-Differences (DiD) and Regression Discontinuity Design (RDD): Application of DiD and RDD in measuring causal effects over time.

• Generous Time Off: Enjoy 28 days of vacation, an additional day off for your birthday, and 1 volunteer day per year.
• Work-Life Balance & Flexibility: Benefit from a hybrid working model, flexible working hours, and no dress code.
• Great Employee Benefits: Access discounts on SIXT rent, share, ride, and SIXT+, along with partner discounts.
• Training & Development: Participate in training programs, external conferences, and internal dev & tech talks for personal growth.
• Health & Well-being: Private health insurance to support your well-being.
• Additional Perks: Enjoy the Coverflex advantage system to enhance your employee experience.