Data Scientist

This is a ground-floor opportunity to shape the future of AI-driven population health management at Onsera Health.

Backed by Population Health Partners (PHP)—the proven venture platform behind Metsera (NASDAQ:

MTSR), Areteia Therapeutics, and Corsera Health—we’re building the future of population health. As a Staff Data Scientist, you’ll lead the design and execution of advanced analytics, statistical modeling, and machine learning across Onsera’s platform, leveraging complex healthcare data—claims, clinical records, and real-world evidence—to power population risk stratification, predictive modeling, and AI-driven decision support. You’ll be a strong independent applied research operator capable of tackling greenfield projects end-to-end while shaping the strategic direction of the data science function, defining modeling strategies that balance immediate needs with long-term vision (2+ years ahead), setting ML/AI standards and tooling, and elevating the work of others through rigorous technical review in close partnership with Product, Engineering, and Clinical teams.

Responsibilities

• Lead the design and development of cutting-edge ML, AI, and statistical models for population health in close collaboration with Product, Clinical, and Engineering teams.
• Define and execute modeling strategies that balance current business needs with long-term technical vision (2+ years ahead).
• Establish ML/AI standards, best practices, and tooling across the data science organization.
• Drive the scientific rigor of Onsera’s analytical outputs, including study design, bias mitigation, and reproducibility of research.
• Review and elevate the work of other data scientists, providing technical mentorship and ensuring quality.
• Analyze and model over large-scale claims, EHR, pharmacy, lab, and other datasets to deliver clinically meaningful and commercially actionable impact.
• Communicate findings to technical and non‑technical stakeholders through clear visualizations, written reports, and presentations that support clinical and commercial decision‑making.

• Mission-driven work addressing critical public health and healthcare economics challenges.
• Ground‑floor opportunity to shape a breakthrough healthcare AI solution.
• Partnership with a world‑class team of venture capitalists, innovators, technologists, bioscientists, and clinicians from PHP and beyond.
• A fast‑paced, dynamic, and highly collaborative work environment.
• Competitive salary and benefits package, including participation in our equity program.

• 8+ years of experience in data science, applied statistics, or a related field, with demonstrated impact at scale.
• PhD in Mathematics/Statistics, Computer Science, Economics, or other quantitative disciplines (or MS with equivalent experience).
• Proven track record of independently leading applied research initiatives from conception to production deployment, with measurable business impact.
• Deep expertise in statistical modeling, classical ML approaches, experimental design, and emerging AI/ML methodologies.
• Expert‑level Python proficiency with extensive experience using scientific and ML libraries (e.g., pandas, scikit‑learn, stats models, PyTorch/Tensor Flow).
• Extensive hands‑on experience with large‑scale analytical data warehouses (Big Query or equivalent).
• Experience defining modeling strategies and technical roadmaps.
• Strong track record of establishing ML/AI standards, best practices, and tooling within data science organizations.
• Experience leading cross‑functional initiatives with Engineering, Product, and Clinical stakeholders, with ability to influence technical and strategic decisions.
• Experience mentoring and elevating the work of other data scientists through technical review and guidance.
• Exceptional written and verbal communication skills, including the ability to present complex technical work to executive audiences and influence organizational strategy.

• Healthcare domain experience, including work with claims data and/or EHR‑derived datasets.
• Experience with techniques such as survival analysis, causal inference, longitudinal modeling,…