Senior ML Data Scientist - Women’s Health

We are seeking a highly experienced and driven Senior Machine Learning Data Scientist to join our Women's Health team. You will play a pivotal role in developing and commercializing cutting-edge machine learning models and algorithms, leveraging wearable health data to improve women's health outcomes. You will lead complex projects, mentor junior team members, and contribute significantly to the strategic direction of our technology..

• →Contribute to the development and deployment of advanced machine learning pipelines and models for innovative Women's Health features, focusing on time-series analysis and robust validation.
• →Drive the commercialization of machine learning models and algorithms, collaborating closely with cross-functional teams including scientists, product managers, software developers, product designers and test engineers.
• →Design and execute rigorous statistical analyses for model performance evaluation, monitoring, and generating actionable insights to inform strategic product decisions.
• →Conduct in-depth exploratory research to identify and characterize novel health sensing features, translating proof-of-concept ideas into scalable, commercialized products.
• →Contribute to the development of best practices for machine learning within the Women's Health team.
• →Present and communicate complex technical concepts to diverse audiences.
• →This is a remote US role with a preference for candidates based in San Francisco, San Diego, or Boston.

This is a remote US role with a slight preference for candidates based in San Francisco, San Diego, or Boston.

• PhD in Biomedical Engineering, Electrical Engineering, Biostatistics, Computer Science, or a related field. And 3+ years of industry experience in machine learning and data science.
• 5+ years of advanced programming experience in Python and SQL.
• Extensive experience with a wide range of machine learning and statistical modeling techniques, including deep learning, time-series analysis, and signal processing.
• Demonstrated experience in the full lifecycle of developing and commercializing machine learning models and algorithms.
• Excellent communication skills, and the ability to articulate complex technical concepts to both technical and non-technical audiences.
• Self-driven, motivated, have a pragmatic can-do attitude and delivery-focused mindset: you can move quickly and prioritize effectively.

• Strong domain knowledge in Women's Health and experience with health data from wearables are significant plusses.