Responsibilities

• Causal Measurement & Business Impact
• - Design and apply causal inference approaches (e.g., quasi-experimental methods, incrementality testing, observational analysis) to estimate the true impact of media investments in settings where randomized experiments may be limited or infeasible.
• - Evaluate the effectiveness of marketing strategies while explicitly accounting for data limitations, confounding, selection bias, and uncertainty.
• - Translate complex causal findings into clear, decision-oriented narratives for senior marketing and business stakeholders.
• Modeling, Statistics & Analysis
• - Apply advanced statistical techniques and machine learning where appropriate, with a bias toward interpretability and causal validity over purely predictive performance.
• - Balance methodological rigor with pragmatism, selecting approaches that are fit for purpose given the data and business context.
• - Write high-quality analytical code (Python, SQL) to support reproducible research, exploratory analysis, and ongoing measurement efforts.
• - Identify opportunities to improve measurement approaches, challenge existing assumptions, and introduce best practices grounded in both academic research and industry experience.
• Data Understanding & Stewardship

Requirements

• Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.

Preferred

• - Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 6+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 8+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.
• - 5+ years’ experience building ML models.
• - 5+ years’ experience writing SQL to analyze data.
• - 5+ years’ experience writing code in Python.
• - 3+ years’ communicating complex technical concepts to non-technical partner teams.
• - 1+ years’ experience performing causal inference
• - 1+ years’ experience with media / marketing data science