Senior Data Scientist (m/f/d)

Job description

At Flix, we offer a dynamic work environment with competitive pay, strong growth opportunities, and a tech-driven approach to making travel more accessible, sustainable, and affordable. As a Senior Data Scientist in our   Commercial & Marketing Intelligence team at Flix, you can make an impact by owning and evolving our causal measurement practice — designing and running experiments that directly shape how we allocate our global marketing budget. You will work closely with a dedicated team across Marketing & Sales, Revenue Management, Reporting, and Engineering to ensure our incrementality evidence drives smarter, more transparent choices at scale. About the Role • Own the design, execution, and continuous improvement of geo-based, time-based, and synthetic control experiment frameworks, ensuring methodological rigour and scalability across global markets and channels • Apply econometric and causal inference techniques — including difference-in-differences, synthetic control, and Bayesian structural time series — to measure the true incremental effect of marketing activities on bookings and revenue • Build and manage a structured test-and-learn programme across paid channels, identifying measurement gaps and prioritising experiments by expected business value • Contribute to the development and validation of attribution models (CLV-MTA and MMM), providing reliable benchmarks that reduce reliance on platform self-reported data • Translate complex causal findings into clear, actionable recommendations for a wide range of stakeholders including marketing teams, finance, and senior management • Review experiment designs, create thorough documentation, and help shape internal standards for how Flix measures marketing effectiveness across all channels About You • Holds a Master's or PhD in Statistics, Econometrics, Applied Mathematics, Data Science, or a related quantitative field • Brings 5+ years of experience in a data science or quantitative research role, with hands-on expertise in designing and evaluating causal experiments such as geo experiments, time-series holdouts, or synthetic control studies • Demonstrate strong command of causal inference methods including difference-in-differences, synthetic control, Bayesian structural time series, or matched market testing • Proficient in Python,SQL, Power BI and experienced with statistical modelling libraries such as statsmodels, PyMC, CausalImpact, or equivalent tools <li style=&quo