Senior Machine Learning Data Scientist

Role overview

The Senior Machine Learning Data Scientist will help develop foundation models for longitudinal wearable and physiological data, turning research advances into useful health capabilities. This is an end-to-end researcher-builder role: you will define important questions, create modeling approaches and experimental systems, establish credible evaluation methods, and help move promising results into prototypes and production experiences. The work requires independence, scientific rigor, and close collaboration with scientists, clinicians, engineers, and product partners.

Responsibilities

- Set and lead research directions for foundation models trained on large, longitudinal health and wearable datasets. - Translate open-ended questions into testable hypotheses, modeling approaches, datasets, and experimental infrastructure. - Design evaluations that account for leakage, confounding, fragile benchmarks, generalization, and real-world validity. - Advance representation learning, forecasting, personalization, multimodal modeling, and generative modeling capabilities. - Take successful research from investigation through prototyping, validation, and shipped product capabilities. - Communicate important findings through publications when appropriate while maintaining focus on practical impact.

Requirements

- At least five years of relevant machine-learning research and applied experience, including doctoral research where applicable. - A PhD or MSc in machine learning, computer science, statistics, electrical engineering, or a related quantitative discipline. - Strong research judgment and a record of identifying meaningful questions, forming original hypotheses, and producing reliable evidence. - Deep expertise in foundation models, representation learning, large-scale training, and model evaluation. - Strong foundations in probability, statistics, experimental design, confounding, leakage, and generalization. - Advanced Python skills and proficiency with modern frameworks such as PyTorch or JAX. - A record of first-author peer-reviewed publications in machine learning, time series, digital health, or related fields.

Nice to have

- Experience with longitudinal time series, wearable sensors, physiological signals, or real-world health data. - Expertise in multimodal or generative modeling, forecasting, personalization, efficient or on-device models, or ML systems. - Experience translating research into production capabilities, or familiarity with clinical research and causal inference.

Benefits and work setup

This is a fully remote role within the United States, with a preference for candidates in the Eastern time zone and no in-office requirement. Compensation varies by location, with stated regional ranges of $147,900–$203,000. The package includes salary, equity, health, dental and vision insurance, mental-health resources, paid time off, holidays, wellness time, sick leave, parental leave, and an employee product benefit.