Data Scientist

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
• Comfortable with version control (Git), reproducible workflows, and cloud or data warehouse environments such as BigQuery or Snowflake
• Proven ability to present statistical findings to non-technical audiences and contribute to strategic choices with data