Sr. Data Scientist

Peloton; New York (NY); On-site; USD 174,000 - 213,000; Full time

Job summary

Peloton is seeking a Senior Data Scientist to build and scale in-house, global marketing measurement powered by Marketing Mix Modeling (MMM). This onsite role in New York, NY offers the opportunity to develop, validate, and evolve statistical models that translate incremental marketing impact into budget optimization, scenario planning, and executive recommendations.

You will work across channels, products, and markets, collaborating to diagnose and extend MMM frameworks, define baseline

Qualifications

• 5–6+ years of professional experience in Data Science, Marketing Science, Marketing Analytics, Econometrics, or related quantitative field.
• Direct MMM modeling experience is required.
• Strong knowledge of MMM methodology including adstock, carryover, and saturation curves.

Responsibilities

• Develop, maintain, and enhance global MMM that measures incremental marketing impact across channels, products, markets, and outcomes.
• Collaborate with modeling code to diagnose, modify, and extend the MMM framework.
• Assess model specifications and validation using statistical performance and business context.
• Design adstock and carryover transformations and account for funnel differences.
• Define baseline and control variables such as seasonality, promotions, and macro factors.
• Diagnose model fit, stability, and incrementality; address multicollinearity and confounding.
• Build robust validation using holdouts, holdout experiments, and other sources of incrementality evidence.
• Translate MMM outputs into business measures like CAC, ROI, and marginal effectiveness.
• Develop budget optimization and scenario-planning capabilities for global marketing.

Skills

Marketing Mix Models

Python

R

Statistical modeling

Executive presentations

Tools

Java

Tableau

Job description

Peloton is looking for a Senior Data Scientist to build and scale in-house, global marketing measurement powered by Marketing Mix Modeling (MMM). In this onsite role in New York, NY, you will develop, validate, and evolve statistical models that help translate incremental marketing impact into budget optimization, scenario planning, and executive recommendations.

What you’ll do

• Develop, maintain, and enhance global MMM that measures incremental marketing impact across channels, products, markets, and business outcomes
• Collaborate with modeling code to diagnose, modify, and extend the MMM framework
• Assess alternative model specifications, methodologies, transformations, interactions, and assumptions using statistical performance and business context
• Design and evaluate adstock and carryover transformations, accounting for differences between brand/upper-funnel, consideration/traffic-driving, and performance/lower-funnel media
• Define baseline and control variables such as seasonality, promotions, pricing, product availability, macroeconomic and industry factors, organic demand, and other business drivers
• Diagnose model fit, stability, contribution and incrementality estimates, and issues such as multicollinearity, confounding, cannibalization, or implausible behavior
• Build robust validation using holdouts, out-of-sample performance, experiments, and other incrementality evidence sources
• Model media impact on intermediate outcomes including brand metrics and organic/direct traffic
• Create channel-level response and saturation curves to quantify diminishing marginal returns
• Translate MMM outputs into business measures such as incremental CAC, ROI, response rates, average effectiveness, and marginal effectiveness
• Develop global marketing budget optimization and scenario-planning capabilities, including real-world constraints like minimum/maximum spend and channel scalability
• Evolve decision-support tools for Analytics, Marketing, Media, and Finance, including automation for ingestion, execution, validation, reporting and monitoring
• Present the business story behind changes in marketing performance and communicate uncertainty, limitations, and tradeoffs
• Partner with Media teams to ensure conclusions reflect how campaigns are planned, bought, and optimized
• Work with Data Engineering and Analytics to build reliable, scalable data pipelines for recurring MMM refreshes
• Contribute to documentation, QA standards, model governance, and repeatable processes, including user-facing scenario-planning and optimization tools

What you bring

• 5–6+ years of professional experience in Data Science, Marketing Science, Marketing Analytics, Econometrics, or a related quantitative field
• Hands-on experience developing and evolving Marketing Mix Models (direct MMM modeling experience required)
• Strong knowledge of MMM methodology, including adstock/carryover, response and saturation curves, diminishing returns, baseline/control variables, model specification, and validation
• Strong statistical programming skills in Python and/or R, with the ability to understand, debug, modify, and extend modeling code
• Experience comparing MMM approaches and adapting methodologies to analytical and business problems
• Experience with marketing measurement concepts such as incrementality, CAC, ROI, marginal effectiveness, response rates, attribution, and funnel dynamics
• Ability to interpret how changes in media, buying strategy, audience strategy, and optimization objectives influence performance
• Demonstrated ability to translate advanced analysis into clear recommendations and executive-level presentations
• Experience scaling in-house MMM capabilities, working across custom-built and established/open-source MMM frameworks
• Experience measuring marketing impact on brand awareness, consideration, search, site traffic, or organic demand
• Experience calibrating or validating MMM with experiments, geo tests, holdouts, or other causal approaches
• Data engineering experience with automated analytical pipelines and productionized modeling workflows
• Experience building analytical applications, optimization interfaces, or internal decision-support tools
• Experience with Java or other software engineering languages/frameworks, and visualization/BI tools such as Tableau, plus experience across consumer, subscription, e-commerce, or direct-to-consumer businesses

Tools you’ll use

Python, R, Java, Tableau

Compensation: USD 173,500 to 213,100 per year.

Location: New York, NY (onsite).