Sr. Data Scientist

ABOUT THE ROLE

We are looking for a Senior Data Scientist specializing in Marketing Mix Modeling (MMM) to help build and scale our in-house global marketing measurement capability. This role will own the development, validation, and evolution of statistical models that measure the incremental impact of marketing investment across channels, funnel stages, products, markets, and business outcomes.

The ideal candidate combines deep hands-on MMM experience, strong statistical programming skills, and strong business storytelling capabilities. This person should be equally comfortable working directly with model code and translating complex analytical outputs into actionable recommendations for Marketing, Media, Finance, and executive leadership.

The Senior Data Scientist will have a strong understanding of MMM methodology and be comfortable working across multiple modeling approaches, including custom-built Bayesian models and established MMM frameworks. They should be able to understand underlying code, evaluate alternative methodologies, diagnose model behavior, and adapt or extend the modeling approach as business and measurement needs evolve.

Beyond core MMM development, this person will help build response and saturation curves, marketing budget optimization and scenario-planning capabilities, and models measuring marketing's impact on intermediate outcomes such as brand health and organic traffic.

The successful candidate will help establish MMM as a scalable internal global decision-making capability that enables the business to understand what marketing and media drove, why performance changed, and where to invest next.

They will build robust measurement of incremental marketing contribution across channels, products, markets, and the customer funnel; connect model results with changes in investment, media strategy, buying behavior, audience strategy, and market conditions; and translate MMM into response curves, marginal effectiveness, scenario planning, and optimization capabilities that inform future marketing investment decisions.

Success means building a capability that is statistically credible, technically scalable, and actionable enough to influence real marketing and financial decisions.

YOUR DAILY IMPACT AT PELOTON

Develop, maintain, and enhance global Marketing Mix Models measuring incremental marketing impact across channels, products, markets, and business outcomes

Work directly with underlying modeling code to diagnose, modify, and extend the modeling framework

Evaluate alternative model specifications, methodologies, transformations, interactions, and assumptions based on statistical performance and business context

Develop and evaluate appropriate adstock and carryover transformations, recognizing differences in how brand/upper-funnel, consideration/traffic-driving, and performance/lower-funnel media influence consumer behavior

Identify and incorporate appropriate baseline and control variables, including seasonality, promotions, pricing, product availability, macroeconomic and industry factors, organic demand, and other relevant business drivers

Diagnose model fit, stability, contribution estimates, incrementality, and potential issues including multicollinearity, confounding, cannibalization, or implausible model behavior

Develop robust validation approaches using holdouts, out-of-sample performance, experiments, and other available sources of incrementality evidence

Develop models measuring media's impact on intermediate outcomes, including brand metrics and organic/direct traffic

Identify and evaluate new variables and hypotheses that improve our understanding of how marketing influences consumer behavior

Connect upper-, mid-, and lower-funnel measurement and account for differences between short-term response and longer-term marketing impact

Develop channel-level response and saturation curves to quantify diminishing marginal returns to marketing investment

Translate MMM outputs into measures including incremental CAC, ROI, response rates, average effectiveness, and marginal effectiveness

Develop a global marketing budget optimization and scenario-planning capability to evaluate alternative spend levels, channel allocations, and investment strategies

Incorporate real-world constraints such as minimum and maximum spend levels, channel scalability, seasonality, and business priorities into optimization

Evolve these capabilities into scalable decision-support tools for Analytics, Marketing, Media, and Finance

Go beyond reporting model outputs to identify the business story behind changes in marketing performance

Analyze how changes in media strategy and buying behavior—including shifts between reach, traffic, and conversion optimization—affect incremental CAC, ROI, response rates, and marginal effectiveness

Distinguish between changes driven by investment levels, media efficiency, audience strategy, channel mix, market conditions, and other business factors

Translate analytical findings into clear recommendations around marketing investment, channel allocation, and strategy

Partner closely with Media teams to ensure analytical conclusions are grounded in how campaigns are planned, bought, and optimized

Develop decision-oriented presentations for Marketing and Media teams, explaining model results, performance drivers, opportunities, and implications

Build concise executive-level narratives that translate sophisticated statistical analysis into business implications and investment decisions

Communicate model uncertainty, limitations, and tradeoffs clearly to technical and non-technical stakeholders

Partner with Data Engineering and Analytics teams to build reliable, scalable data pipelines supporting recurring MMM refreshes

Help automate model ingestion, execution, validation, reporting, and monitoring

Contribute to user-facing scenario-planning and optimization tools and establish documentation, QA standards, model governance, and repeatable processes

YOU BRING TO PELOTON

5–6+ years of professional experience in Data Science, Marketing Science, Marketing Analytics, Econometrics, or a related quantitative field

Demonstrated hands-on experience developing and evolving Marketing Mix Models; direct MMM modeling experience is required

Strong understanding of MMM methodology, including adstock/carryover transformations, response and saturation curves, diminishing returns, baseline/control variables, model specification, and model validation

Strong statistical programming skills, particularly Python and/or R, with the ability to understand, debug, modify, and extend modeling code

Experience working across different MMM approaches and adapting methodologies based on the analytical and business problem

Experience developing response/saturation curves and marketing budget optimization or scenario-planning methodologies using MMM outputs

Strong understanding of marketing measurement concepts including incrementality, CAC, ROI, marginal effectiveness, response rates, attribution, and funnel dynamics

Ability to interpret how changes in media strategy, buying approach, audience strategy, and optimization objectives influence marketing performance

Demonstrated ability to translate sophisticated analytical work into clear business narratives, recommendations, and executive-level presentations

Strong communication and cross-functional partnership skills

Experience developing and scaling in-house MMM capabilities and working across custom-built and established/open-source MMM frameworks

Experience measuring marketing's impact on brand awareness, consideration, search, site traffic, or organic demand

Experience calibrating or validating MMM using experiments, geo tests, holdouts, or other causal measurement approaches

Data engineering experience, including automated analytical pipelines and productionized modeling workf