Top Data & Analytics Jobs in NYC, NY

The Consumer Insights Analyst at Peloton collaborates with cross-functional teams to develop actionable insights from qualitative and quantitative research studies. They lead long-term insights programs, integrate consumer insights into decision-making processes, and identify market trends.

As a Data Scientist at Peloton, you will be responsible for optimizing operations using data and insights, performing deep dive analysis, collaborating with cross-functional partners, building data-driven models, and communicating recommendations clearly. You will work with a team to drive decision-making and achieve transformative impact through data.

The Machine Learning Engineer at Peloton will be responsible for driving personalization and recommendations for members through the application of AI and ML techniques. They will work on optimizing member engagement and content discovery by collaborating with various teams. The role involves building ML pipelines, researching ML techniques, implementing models, running A/B tests, and deploying ML models.

The Marketing Automation Analyst at Peloton is responsible for leading the development of MarTech product/system requirements, educating business and technical teams on MarTech product recommendations, monitoring key metrics, and staying informed on trends in Marketing Automation and Technology.

Seeking an experienced Senior Systems Analyst to support supply chain teams by enhancing and maintaining warehouse and transportation management systems. Responsibilities include analyzing system process problems, developing business requirements, and testing system changes to improve supply chain operations.

Lead a dynamic team of analysts and data scientists, develop a strategic data science strategy, collaborate with business leaders, and oversee team performance in extracting valuable insights from data for Supply Chain and Global Member Support organizations.

Senior Software Engineer role focusing on ML infrastructure and operations for AI/ML teams in the fitness domain. Responsibilities include building and scaling machine learning system infrastructure, implementing scalable solutions, and collaborating with engineers for model development and monitoring.