Senior Applied Scientist

ABOUT THE ROLE

The Personalization team at Peloton is looking for a Senior Applied Scientist to help us build the future of personalization at Peloton. You will work closely with ML Engineers, data engineers, and product analysts to improve our understanding of the largest multi-modal fitness data and leverage machine learning to enable new and exciting ways to guide our members in their fitness journey. You’ll be able to spot gaps in how we’ve done things before, and you’ll be empowered to find better ways to do them. We embrace the culture of experimentation, so you will have the opportunity to rapidly test out your ideas and measure their impact.

YOUR DAILY IMPACT AT PELOTON

• Develop a framework for organizing Peloton data into a knowledge graph, and build high impact inference models on top of it to create new ways of personalizing member experience and to enrich existing ones. 
• Work closely with Product Managers and Product Analysts to understand new opportunities for the personalization team. Collaborate with ML engineers to quickly prototype solutions and be an equal partner in deploying the end-to-end solutions at scale. 
• Complete A/B tests as needed to evaluate the performance of ML models against the status quo. 
• Be a steward for high scientific standards in everything we do in Personalization and beyond. Inform and influence the development of better practices for experimentation and deployment of ML models. 
• Conduct ground breaking research in recommendation related problems, and apply the technology to different business problems.
• Represent Peloton at top tier conferences and meetup groups.

YOU BRING TO PELOTON

• MS in highly quantitative fields including Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc. 
• 4+ Years of experience developing innovative ML algorithms and solutions that power ML-driven product features. 
• Familiar with graph databases like Neo4j and graph inference techniques like Node2Vec, Graph Neural Networks etc. 
• Solid programming background, with extensive experience in Python.
• Familiar with the underlying frameworks and implementation mechanisms of at least one of the mainstream deep learning frameworks, like TensorFlow, PyTorch or MXNet
• Applied knowledge of A/B testing, self-sufficient to evaluate your machine learning models 
• Entrepreneurial and self-directed, innovative, biased towards action in fast-paced environments. 
• Able to take complete ownership of a feature or project. 
• Experience working with one of the cloud providers preferably AWS.
• Experience working in a shared code repository with a version control system (Git / SVN)
• Bonus: PhD in highly quantitative fields including Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc.
• Bonus: Publications in top tier machine learning conferences such as ICML, NeurIPS, ICLR, RecSys and KDD.

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