Duration: 3 months

Employment: Full-time, Paid Internship
Compensation: Based on experience
Location: Remote
Company: FocusKPI

About the Role

FocusKPI is seeking a highly motivated PhD Data Science Intern to join our team for a three-month, full-time engagement focused on the research, development, and advancement of Media Mix Modeling (MMM) algorithms.

This is a hands-on, research-oriented role for someone with a strong foundation in statistics, econometrics, economics, or a closely related quantitative discipline who is interested in applying rigorous statistical methodology to real-world marketing and business problems.

The ideal candidate will have deep theoretical knowledge combined with practical experience developing statistical models end-to-end—from problem formulation and data preparation through model development, validation, interpretation, and production implementation.

The intern will work closely with senior data scientists and leadership to evaluate and enhance our MMM methodology, explore new modeling approaches, and translate advanced statistical techniques into scalable analytical solutions.

What You Will Do

• Research and evaluate statistical and econometric approaches for Media Mix Modeling and marketing effectiveness measurement
• Develop, test, and enhance MMM algorithms across the full modeling lifecycle
• Work with time-series, panel, and observational marketing data to develop robust models of media response and business outcomes
• Explore methodologies for:
  • Media response curves and saturation effects
  • Adstock and carryover effects
  • Incrementality and causal inference
  • Channel interaction and synergies
  • Seasonality, trends, and external factors
  • Model regularization and variable selection
  • Uncertainty estimation and statistical inference
  • Bayesian and frequentist modeling approaches
• Develop model diagnostics and validation frameworks to assess model stability, predictive performance, statistical significance, and business interpretability
• Conduct simulation and experimentation to understand algorithm behavior under different data-generating conditions
• Compare alternative modeling methodologies and identify opportunities to improve model accuracy, robustness, and interpretability
• Translate research findings into production-ready algorithms and analytical workflows
• Work with real client datasets and understand the practical challenges of applying MMM to imperfect business data
• Collaborate with senior data scientists to document methodology, assumptions, limitations, and results
• Contribute to the development of next-generation MMM capabilities within FocusKPI