Data Scientist, Product Analytics

ABOUT THE ROLE

Peloton is seeking a Data Scientist on the Product Analytics team to support the innovation, iteration, and optimization of our engagement products. This Data Scientist, Product 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 and evaluate new features and experiences developed by the Product team. 

YOUR DAILY IMPACT AT PELOTON 

• Own analytics for one core workstream on Peloton digital apps, from metrics definition and data instrumentation design to ongoing reporting, investigation, and generative research 
• Be a key contributor to strategic decisions and roadmap formation for workstream
• Collaborate with Product Managers, User Researchers, Designers, and Engineers to lead experimentation processes: designing and measuring A/B tests that help us understand the impact of new/alternate versions of features on user behavior
• Define and own reporting and interpretation of KPI’s & metrics for workstream, feature launches and A/B tests
• 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 workstream: provide insight into behaviors and difficulties of members on their journey from app download through sustained long-term engagement; help guide roadmap and evaluation of new features to enhance this user journey
• 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 3+ years of overall experience in an analytics capacity with a focus on problem-solving and providing concrete insights; some experience conducting analysis with a product/UX focus is desirable (an advanced degree may contribute to years of experience)
• Have solid technical skills when it comes to data wrangling and handling large datasets: can use SQL for data extraction (including sophisticated joins, CTE, window functions and query optimization), and have a solid understanding of Python syntax required for data cleaning and exploratory analysis (e.g. pandas, numpy)
• Have experience with A/B testing and have demonstrated experience designing and assessing experiments
• Have an understanding 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 communicate your insights to non-technical audiences and senior leadership
• 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 humble: 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.)
• Experience with social engagement features/products, growth loops, network effects and product virality 
• Understanding of adjacent quantitative and qualitative research methods and techniques (e.g. usability testing, concept testing, market sizing, etc.)

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