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