Remote PhD Data Science Intern - MMM Research

Wal-Mart

United States

Remote

USD 34,000 - 83,000

Full time

14 days+
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Job summary

FocusKPI is seeking a highly motivated PhD Data Science Intern to join our team for a three‑month, full‑time engagement focused on MMM research and development. This remote internship offers hands-on work with real datasets and collaboration with senior data scientists.

You will design and evaluate statistical models, implement end-to-end MMM workflows, and translate findings into production-ready code while learning advanced modeling approaches and contributing to next-generation MMM

Qualifications

  • PhD required in a quantitative field.
  • Strong foundation in statistics, econometrics, or related discipline.
  • Experience building statistical models end-to-end.
  • Proficient in Python and large dataset handling.
  • Ability to work independently with senior team.

Responsibilities

  • Research and evaluate MMM approaches and marketing effectiveness.
  • Develop, test, and enhance MMM algorithms.
  • Work with time-series and panel data to model media response.
  • Develop diagnostics and validation frameworks.
  • Translate research into production-ready code.
  • Collaborate with senior data scientists and leadership.
  • Document methodology and results.

Skills

Statistical modeling
Time-series analysis
Causal inference
Python programming
Data analysis
Independent work

Education

PhD in Statistics, Economics, Econometrics, Applied Mathematics, or Data Science

Tools

Python
PyMC
Stan
NumPyro
JAX
scikit-learn
statsmodels

Job description

FocusKPI is seeking a highly motivated PhD Data Science Intern to join our team for a three‑month, full‑time engagement focused on MMM research and development. This remote internship offers hands-on work with real datasets and collaboration with senior data scientists.

You will design and evaluate statistical models, implement end-to-end MMM workflows, and translate findings into production-ready code while learning advanced modeling approaches and contributing to next-generation MMM

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