{"id":3170382,"url":"https://alion.io/job/peloton-sr-data-scientist","title":"Sr. Data Scientist","company":{"id":5254,"name":"Peloton","domain":"onepeloton.com","url":"https://alion.io/company/peloton","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":92,"open_postings":13,"ghost_share":0,"stale_share":0.308,"repost_share":0,"time_to_fill_p50_days":51,"computed_at":"2026-10-24T06:02:35Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["New York, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":173500,"max":213100,"currency":"USD","period":"year","gross":null,"usd_annual":213100},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Java","optional":false},{"name":"Python","optional":false},{"name":"Tableau","optional":false}],"status":"live","first_seen_at":"2026-10-23T15:46:49Z","employer_posted_date":"2026-10-23","last_verified_at":"2026-10-24T11:14:15Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"ABOUT THE ROLE\nWe 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.\nThe 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.\nThe 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.\nBeyond 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.\nThe 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.\nThey 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.\nSuccess means building a capability that is statistically credible, technically scalable, and actionable enough to influence real marketing and financial decisions.\nYOUR DAILY IMPACT AT PELOTON\nDevelop, maintain, and enhance global Marketing Mix Models measuring incremental marketing impact across channels, products, markets, and business outcomes\nWork directly with underlying modeling code to diagnose, modify, and extend the modeling framework\nEvaluate alternative model specifications, methodologies, transformations, interactions, and assumptions based on statistical performance and business context\nDevelop 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\nIdentify and incorporate appropriate baseline and control variables, including seasonality, promotions, pricing, product availability, macroeconomic and industry factors, organic demand, and other relevant business drivers\nDiagnose model fit, stability, contribution estimates, incrementality, and potential issues including multicollinearity, confounding, cannibalization, or implausible model behavior\nDevelop robust validation approaches using holdouts, out-of-sample performance, experiments, and other available sources of incrementality evidence\nDevelop models measuring media's impact on intermediate outcomes, including brand metrics and organic/direct traffic\nIdentify and evaluate new variables and hypotheses that improve our understanding of how marketing influences consumer behavior\nConnect upper-, mid-, and lower-funnel measurement and account for differences between short-term response and longer-term marketing impact\nDevelop channel-level response and saturation curves to quantify diminishing marginal returns to marketing investment\nTranslate MMM outputs into measures including incremental CAC, ROI, response rates, average effectiveness, and marginal effectiveness\nDevelop a global marketing budget optimization and scenario-planning capability to evaluate alternative spend levels, channel allocations, and investment strategies\nIncorporate real-world constraints such as minimum and maximum spend levels, channel scalability, seasonality, and business priorities into optimization\nEvolve these capabilities into scalable decision-support tools for Analytics, Marketing, Media, and Finance\nGo beyond reporting model outputs to identify the business story behind changes in marketing performance\nAnalyze 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\nDistinguish between changes driven by investment levels, media efficiency, audience strategy, channel mix, market conditions, and other business factors\nTranslate analytical findings into clear recommendations around marketing investment, channel allocation, and strategy\nPartner closely with Media teams to ensure analytical conclusions are grounded in how campaigns are planned, bought, and optimized\nDevelop decision-oriented presentations for Marketing and Media teams, explaining model results, performance drivers, opportunities, and implications\nBuild concise executive-level narratives that translate sophisticated statistical analysis into business implications and investment decisions\nCommunicate model uncertainty, limitations, and tradeoffs clearly to technical and non-technical stakeholders\nPartner with Data Engineering and Analytics teams to build reliable, scalable data pipelines supporting recurring MMM refreshes\nHelp automate model ingestion, execution, validation, reporting, and monitoring\nContribute to user-facing scenario-planning and optimization tools and establish documentation, QA standards, model governance, and repeatable processes\nYOU BRING TO PELOTON\n5-6+ years of professional experience in Data Science, Marketing Science, Marketing Analytics, Econometrics, or a related quantitative field\nDemonstrated hands-on experience developing and evolving Marketing Mix Models; direct MMM modeling experience is required\nStrong understanding of MMM methodology, including adstock/carryover transformations, response and saturation curves, diminishing returns, baseline/control variables, model specification, and model validation\nStrong statistical programming skills, particularly Python and/or R, with the ability to understand, debug, modify, and extend modeling code\nExperience working across different MMM approaches and adapting methodologies based on the analytical and business problem\nExperience developing response/saturation curves and marketing budget optimization or scenario-planning methodologies using MMM outputs\nStrong understanding of marketing measurement concepts including incrementality, CAC, ROI, marginal effectiveness, response rates, attribution, and funnel dynamics\nAbility to interpret how changes in media strategy, buying approach, audience strategy, and optimization objectives influence marketing performance\nDemonstrated ability to translate sophisticated analytical work into clear business narratives, recommendations, and executive-level presentations\nStrong communication and cross-functional partnership skills\nExperience developing and scaling in-house MMM capabilities and working across custom-built and established/open-source MMM frameworks\nExperience measuring marketing's impact on brand awareness, consideration, search, site traffic, or organic demand\nExperience calibrating or validating MMM using experiments, geo tests, holdouts, or other causal measurement approaches\nData engineering experience, including automated analytical pipelines and productionized modeling workflows\nExperience developing analytical applications, optimization interfaces, or internal decision-support tools\nExperience 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\nThe 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.\nAs 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:\nMedical, dental and vision insurance \nGenerous paid time off policy\nShort-term and long-term disability\nAccess to mental health services\n401k, tuition reimbursement and student loan paydown plans\nEmployee Stock Purchase Plan\nFertility and adoption support and up to 18 weeks of paid parental leave \nChild care and family care discounts\nFree access to Peloton Digital App and apparel and product discounts\nCommuter benefits and Citi Bike Discount\nPet insurance and so much more!\nBase Salary Range\n$173,500—$213,100 USD\nABOUT PELOTON:\nPeloton (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.\nPeloton 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: .\nAt 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. \nQualified applicants with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordina...","description_format":"text","description_chars":13322,"description_truncated":true,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["401k plan","Equity","Parental leave","Vision insurance"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Consumer Goods","Fitness","Fitness Equipment"],"lifecycle":[{"event":"open","at":"2026-10-23T18:22:20Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":0,"expected_fill_days":51,"reasons":["conf:3","velocity","win:early"],"computed_at":"2026-10-24T06:02:35Z"},"pay":{"stated_usd_annual":213100,"is_top_pay":true},"html_url":"https://alion.io/job/peloton-sr-data-scientist","json_url":"https://alion.io/job/peloton-sr-data-scientist.json","meta":{"generated_at":"2026-10-24T13:09:53Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":18882,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-25T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":5254},"rest":"https://alion.io/mcp/rest/get_company?id=5254"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fpeloton-sr-data-scientist"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fpeloton-sr-data-scientist"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fpeloton-sr-data-scientist"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/peloton-sr-data-scientist\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fpeloton-sr-data-scientist"}]}