Sr. Data Scientist, Product Analytics

ABOUT THE ROLE

Peloton is seeking a Senior Data Scientist on the Product Analytics team to support the innovation, iteration, and software optimization for our Bike and Tread products. This Senior Data Scientist will work as part of a cross-functional team of Product Managers, Engineers, Designers, and User Researchers to better understand how members are interacting with Peloton products and to inform overall product strategy, focusing on business outcomes. 

YOUR DAILY IMPACT AT PELOTON 

• Own analytics for two to three core workstreams, from metrics definition and data instrumentation design to ongoing reporting, investigation, and generative research
• Collaborate with Product Managers, User Researchers, Designers, and Engineers best evaluate Peloton’s features, helping product understand the overall impact to user behavior and business outcomes
• Define and own reporting and interpretation of KPIs & metrics for workstream, feature launches and A/B tests
• Use causal inference methodologies to provide product insights and strategic recommendations
• Develop a deep understanding of user personas, fitness routines, and retention drivers for our members
• Evaluate engagement trends across markets - compare workout/content engagement and feature usage among members within our growing global community
• Become an expert on your workstreams: deep understanding of Peloton’s products, provide insight into behaviors and difficulties of members on their Peloton journey from post-purchasing through sustained long-term engagement; help guide roadmap and evaluation of new features to enhance this user journey
• Partner with cross-functional Product and Business teams on research & development efforts related to software improvements and new market expansion
• Work in a lean and agile way, focusing on team members' desired outcomes over granular tasks and continuously assessing tradeoffs between precision and action-ability 

YOU BRING TO PELOTON

• BA/BS degree, preferably in a technical field
• Have 6+ years of overall experience in an analytics capacity with a focus on problem-solving and providing concrete insights (an advanced degree may contribute to years of experience); at least 2+ years experience working directly with product teams
• Have solid technical skills when it comes to data wrangling and handling large datasets: expert in SQL for data extraction (including sophisticated joins, CTE, window functions, query optimization), and have a solid understanding of Python syntax required for data cleaning and exploratory analysis (e.g. pandas, numpy)
• Have strong experience with A/B testing and have demonstrated experience designing and assessing experiments
• Strong command of statistics and data science methodologies (e.g. hypothesis testing, regressions, and other machine learning techniques such as clustering)
• Have proven experience defining and owning project success metrics
• Have a passion for data storytelling via decks and dashboards and a proven ability to efficiently relay your insights to non-technical audiences and senior leadership
• Able to communicate effectively with product stakeholders and product leadership, providing overall member and business impact, focusing on outcomes
• Are comfortable owning your project timelines, prioritizing tasks with user and business impact in mind, assessing speed/precision tradeoffs, and proactively communicating project statuses to collaborators and teammates
• Work well collaboratively, both in a cross-functional context - with Product Managers, Project Managers, User Researchers, Designers, and Engineers - and as a part of joint research projects with Product Analytics teammates
• Are curious and a self-starter: ask good questions, are always excited to learn new methods, approaches, and perspectives, and are eager to investigate interesting trends in the data 

BONUS 

• Previous experience working on a product analytics team for a B2C software or software+hardware product
• Solid understanding of event-based data collection and related technical concepts (e.g. SDK, device vs. cloud mode, schema management) and tools (e.g. Segment)
• Experience with product analytics SaaS tools (e.g. Amplitude, Mixpanel, Pendo, etc.)
• Understanding of adjacent quantitative and qualitative research methods and techniques (e.g. usability testing, concept testing, market sizing, etc.)

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