Senior Data Scientist – Causal Inference & Measurement Join to apply for the Senior Data Scientist – Causal Inference & Measurement role at Product Pulse. 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. Your Role 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. Your Qualifications 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. The Offer 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. #J-18808-Ljbffr