The intern will research and develop Media Mix Modeling (MMM) algorithms to measure marketing effectiveness and business outcomes. They will collaborate with senior data scientists to translate statistical findings into scalable, production-ready analytical solutions.
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
Required Qualifications
PhD in Statistics, Economics, Econometrics, Applied Mathematics, Data Science, or a closely related quantitative field
Strong theoretical foundation in:
Statistical modeling
Econometrics
Regression and multivariate analysis
Time-series analysis
Probability and statistical inference
Optimization
Strong understanding of causal inference and observational data
Demonstrated ability to develop statistical models end-to-end, including:
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
Preferred Qualifications
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
Familiarity with:
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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