Adstock Data Jobs

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Sr. Data Scientist

Peloton New York, NY

New York, NY

Today

$174k - $213k/yr Full time Marketing Adstock Data Analysis

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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).

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