Sr. Data Scientist

Senior Data Scientist Marketing Mix ModelingWe 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 PelotonDevelop, maintain, and enhance global Marketing Mix Models measuring incremental marketing impact across channels, products, markets, and business outcomesWork directly with underlying modeling code to diagnose, modify, and extend the modeling frameworkEvaluate alternative model specifications, methodologies, transformations, interactions, and assumptions based on statistical performance and business contextDevelop and evaluate appropriate adstock and carryover transformations, recognizing differences in how brand/upper-funnel, consideration/traffic-driving, and performance/lower-funnel media influence consumer behaviorIdentify and incorporate appropriate baseline and control variables, including seasonality, promotions, pricing, product availability, macroeconomic and industry factors, organic demand, and other relevant business driversDiagnose model fit, stability, contribution estimates, incrementality, and potential issues including multicollinearity, confounding, cannibalization, or implausible model behaviorDevelop robust validation approaches using holdouts, out-of-sample performance, experiments, and other available sources of incrementality evidenceDevelop models measuring media's impact on intermediate outcomes, including brand metrics and organic/direct trafficIdentify and evaluate new variables and hypotheses that improve our understanding of how marketing influences consumer behaviorConnect upper-, mid-, and lower-funnel measurement and account for differences between short-term response and longer-term marketing impactDevelop channel-level response and saturation curves to quantify diminishing marginal returns to marketing investmentTranslate MMM outputs into measures including incremental CAC, ROI, response rates, average effectiveness, and marginal effectivenessDevelop a global marketing budget optimization and scenario-planning capability to evaluate alternative spend levels, channel allocations, and investment strategiesIncorporate real-world constraints such as minimum and maximum spend levels, channel scalability, seasonality, and business priorities into optimizationEvolve these capabilities into scalable decision-support tools for Analytics, Marketing, Media, and FinanceGo beyond reporting model outputs to identify the business story behind changes in marketing performanceAnalyze how changes in media strategy and buying behavior—including shifts between reach, traffic, and conversion optimization—affect incremental CAC, ROI, response rates, and marginal effectivenessDistinguish between changes driven by investment levels, media efficiency, audience strategy, channel mix, market conditions, and other business factorsTranslate analytical findings into clear recommendations around marketing investment, channel allocation, and strategyPartner closely with Media teams to ensure analytical conclusions are grounded in how campaigns are planned, bought, and optimizedDevelop decision-oriented presentations for Marketing and Media teams, explaining model results, performance drivers, opportunities, and implicationsBuild concise executive-level narratives that translate sophisticated statistical analysis into business implications and investment decisionsCommunicate model uncertainty, limitations, and tradeoffs clearly to technical and non-technical stakeholdersPartner with Data Engineering and Analytics teams to build reliable, scalable data pipelines supporting recurring MMM refreshesHelp automate model ingestion, execution, validation, reporting, and monitoringContribute to user-facing scenario-planning and optimization tools and establish documentation, QA standards, model governance, and repeatable processesYou Bring To Peloton5–6+ years of professional experience in Data Science, Marketing Science, Marketing Analytics, Econometrics, or a related quantitative fieldDemonstrated hands-on experience developing and evolving Marketing Mix Models; direct MMM modeling experience is requiredStrong understanding of MMM methodology, including adstock/carryover transformations, response and saturation curves, diminishing returns, baseline/control variables, model specification, and model validationStrong statistical programming skills, particularly Python and/or R, with the ability to understand, debug, modify, and extend modeling codeExperience working across different MMM approaches and adapting methodologies based on the analytical and business problemExperience developing response/saturation curves and marketing budget optimization or scenario-planning methodologies using MMM outputsStrong understanding of marketing measurement concepts including incrementality, CAC, ROI, marginal effectiveness, response rates, attribution, and funnel dynamicsAbility to interpret how changes in media strategy, buying approach, audience strategy, and optimization objectives influence marketing performanceDemonstrated ability to translate sophisticated analytical work into clear business narratives, recommendations, and executive-level presentationsStrong communication and cross-functional partnership skillsExperience developing and scaling in-house MMM capabilities and working across custom-built and established/open-source MMM frameworksExperience measuring marketing's impact on brand awareness, consideration, search, site traffic, or organic demandExperience calibrating or validating MMM using experiments, geo tests, holdouts, or other causal measurement approachesData engineering experience, including automated analytical pipelines and productionized modeling workflowsExperience developing analytical applications, optimization interfaces, or internal decision-support toolsExperience with Java or other software engineering languages/frameworks, visualization/BI platforms such as Tableau, and/or consumer, subscription, e-commerce, or direct-to-consumer businessesThe 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 insuranceGenerous paid time off policyShort-term and long-term disabilityAccess to mental health services401k, tuition reimbursement and student loan paydown plansEmployee Stock Purchase PlanFertility and adoption support and up to 18 weeks of paid parental leaveChild care and family care discountsFree access to Peloton Digital App and apparel and product discountsCommuter benefits and Citi Bike DiscountPet insurance and so much more!Base Salary Range $173,500 —$213,1