Data Scientist; Machine Learning

Are you passionate about health and making a difference?

Phenome Health is a 501(c)(3) non‑profit research organization established in 2021 to contribute solutions to major challenges causing a health crisis. Our team, led by internationally recognized visionary scientist Lee Hood, is promoting a radical paradigm change—namely shifting from a reactive system that focuses on treating disease to a proactive system focused on optimizing health. Phenome Health is partnering with research participants to build a disease‑agnostic discovery framework.

We are leveraging advancements in biotechnologies, digital health, and AI to execute projects that will drive personalized and equitable medicine of the future.

Our headquarters are based in Seattle, Washington with team members distributed across the United States.

Phenome Health is seeking a full‑time Data Scientist (Machine Learning) to join our Data Science Team.

Location: remote or hybrid (if in the Seattle area)

The Phenome Health data science team is a multidisciplinary research team working in collaboration with other parts of the non‑profit organization and external partners. Its goals are to use human data resources (internal and external) to understand the biology of wellness and longevity and to develop insights into potential interventions and/or diagnostics that extend healthy aging. Several large, public and private multi‑omic datasets are available to the team including a comprehensive in‑house deeply phenotyped dataset, the UK Biobank, Twins

UK, and NIH All Of Us. By integrating phenotypic information with multi‑omic human study data, the team aims to define pathways that are critical to identify disease transitions and extend healthspan and related outcomes. Major emphases include identifying multi‑omic signatures that are associated with disease transition and longevity, as well as using systems‑oriented methods to identify the major pathways involved. This effort will require creative use of state‑of‑the‑art data integration, harmonization and statistical approaches.

The Data Scientist (Machine Learning) assists the Phenome Health team in designing, analyzing, visualizing, interpreting and communicating data and results from large multi‑omic studies. We are looking for a Machine Learning Scientist who brings strong experience in deep learning and a genuine interest in biology. You should be comfortable working with deep learning models such as variational autoencoders, Graph Neural Networks and Transformers.

This role centers on learning rich representations from complex biological data and using them to uncover meaningful patterns. You will also help us explore early ideas for agentic AI systems that can interpret multi‑omic data through vectorization, retrieval, and biologically informed reasoning. This role is not a fit for machine learning experts who have no experience with biology.

• Master's degree or higher in computational biology, biostatistics, systems biology, or related field(s)
• 5+ years of hands‑on academic or industry experience in a relevant field
• Strong experience with deep learning frameworks such as PyTorch or Tensor Flow
• Experience with high‑dimensional biological data
• Attention to detail in handling complex data sets, interacting with collaborators and other analysts
• Excellent interpersonal skills and a strong propensity for collaborative teamwork
• Ability to work well in a multidisciplinary team and communicate clearly with technical and non‑technical colleagues
• Comfort working in a fast‑moving & hybrid environment where projects evolve and new ideas are encouraged.
• Previous analysis of Alzheimer's, immunology or metabolic disorders is highly desirable.
• Experience in electronic health record analysis is a strong plus.

• Leads data science project(s) involving machine learning, and is accountable for meeting established deadlines
• Designs, directs, and evaluates assigned scientific analyses appropriate to project objectives
• Build and evaluate deep learning models, including VAEs and biologically informed architectures.
• Utilizes proper strategic design in meeting project…