Remote PhD Data Science Intern – Media Mix Modeling

About the role

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

Posting text as published by FocusKPI.

The evidence behind this listing

Workplace

Remote Employer field

Seniority

intern From the job title

Employment type

full time

Location as listed

Any, CA, United States

Pay and work authorization

Stated pay

Not stated

Visa sponsorship

Not stated

Security clearance

Not established

Citizenship

Not established