Remote PhD Data Science Intern – Media Mix Modeling

Location: Remote

• 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

• • 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

• PhD in Statistics, Economics, Econometrics, Applied Mathematics, Data Science, or a closely related quantitative field

• • Statistical modeling
  • Econometrics
  • Regression and multivariate analysis
  • Time-series analysis
  • Probability and statistical inference
  • Optimization
• Strong understanding of causal inference and observational data

• • Problem formulation
  • Data preparation and feature engineering
  • Model specification
  • Estimation
  • Model diagnostics
  • Validation
  • Interpretation
  • Implementation
• Strong programming skills in Python
• Experience working with large, complex datasets
• Ability to translate mathematical and statistical concepts into practical algorithms
• Strong analytical and problem-solving skills
• Ability to work independently while collaborating closely with senior technical team members

• Direct experience with Media Mix Modeling (MMM)
• Experience with marketing measurement, marketing analytics, or advertising data
• Experience with Bayesian hierarchical models
• Experience with causal inference, experimentation, or uplift modeling
• Experience with time-series econometrics

• • Bayesian inference / MCMC
  • State-space models
  • Regularization
  • Constrained optimization
  • Nonlinear regression
  • Response curve estimation
  • Monte Carlo simulation
• Experience with modern statistical computing frameworks such as PyMC, Stan, NumPyro, JAX, scikit-learn, statsmodels, or equivalent
• Experience taking research concepts and converting them into reusable production code

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