Job description

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 workflows Experience developing analytical applications, optimization interfaces, or internal decision-support tools Experience with Java or other software engineering languages/frameworks, visualization/BI platforms such as Tableau, and/or consumer, subscription, e-commerce, or direct-to-consumer businesses #LI-KM1 #LI-Hybrid The base salary range represents the low and high end of the anticipated salary range for this position based at our New York City headquarters. The actual base salary offered for this position will depend on numerous factors including, without limitation, experience and business objectives and if the location for the job changes. Our base salary is just one component of Peloton’s competitive total rewards strategy that also includes annual equity awards and an Employee Stock Purchase Plan as well as other region-specific health and welfare benefits. As an organization, one of our top priorities is to maintain the health and wellbeing for our employees and their family. To achieve this goal, we offer robust and comprehensive benefits including: Medical, dental and vision insurance Generous paid time off policy Short-term and long-term disability Access to mental health services 401k, tuition reimbursement and student loan paydown plans Employee Stock Purchase Plan Fertility and adoption support and up to 18 weeks of paid parental leave Child care and family care discounts Free access to Peloton Digital App and apparel and product discounts Commuter benefits and Citi Bike Discount Pet insurance and so much more! Base Salary Range $173,500 — $213,100 USD ABOUT PELOTON: Peloton (NASDAQ: PTON) provides Members with expert instruction, and world class content to create impactful and entertaining workout experiences for anyone, anywhere and at any stage in their fitness journey. At home, outdoors, traveling, or at the gym, Peloton brings together innovative hardware, distinctive software, and exclusive content. Founded in 2012 and headquartered in New York City, Peloton has millions of Members across the US, UK, Canada, Germany, Australia, and Austria. For more information, visit www.onepeloton.com. Peloton is an equal opportunity employer and complies with all applicable federal, state, and local fair employment practices laws. Equal employment opportunity has been, and will continue to be, a fundamental principle at Peloton, where all team members, applicants, and other covered persons are considered on the basis of their personal capabilities and qualifications without discrimination because of race, color, religion, sex, age, national origin, disability, pregnancy, genetic information, military or veteran status, sexual orientation, gender identity or expression, marital and civil partnership/union status, alienage or citizenship status, creed, genetic predisposition or carrier status, unemployment status, familial status, domestic violence, sexual violence or stalking victim status, caregiver status, or any other protected characteristic as established by applicable law. This policy of equal employment opportunity applies to all practices and procedures relating to recruitment and hiring, compensation, benefits, termination, and all other terms and conditions of employment. If you would like to request any accommodations from application through to interview, please email: applicantaccommodations@onepeloton.com . At Peloton, we embrace technology, including AI, to enhance productivity and accelerate innovation in the work we do for our members. However, in our hiring process, our priority remains in getting to know you and your unique qualifications. To ensure a fair and equitable process, we do not permit the use of AI tools during any stage of the application and interview process. In considering you as an applicant, we want to understand your skills, experiences, and motivations without mediation through an AI system. We also want to directly assess your communication skills without the use of an AI tool. Qualified applicants with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act, the City of Los Angeles Fair Chance Initiative for Hiring Ordinance and the San Francisco Fair Chance Ordinance, as applicable to applicants applying for positions in these jurisdictions. Please be aware that fictitious job openings, consulting engagements, solicitations, or employment offers may be circulated on the Internet in an attempt to obtain privileged information, or to induce you to pay a fee for services related to recruitment or training. Peloton does NOT charge any application, processing, or training fee at any stage of the recruitment or hiring process. All genuine job openings will be posted here on our careers page and all communications from the Peloton recruiting team and/or hiring managers will be from an @ onepeloton.com email address. If you have any doubts about the authenticity of an email, letter or telephone communication purportedly from, for, or on behalf of Peloton, please email applicantaccommodations@onepeloton.com before taking any further action in relation to the correspondence. Peloton does not accept unsolicited agency resumes. Agencies should not forward resumes to our jobs alias, Peloton employees or any other organization location. Peloton is not responsible for any agency fees related to unsolicited resumes.

Requirements

Must-haves

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

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

gopythonjavaai

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401k

Pto

Paid Time Off

Parental Leave