Onos Health’s mission is simple but ambitious: ensure every healthcare dollar goes toward delivering the highest quality care. Today, 30% of total U.S. healthcare spending is wasted due to ineffective care and administrative burden caused by misalignment between providers and payers.
Onos is addressing this by building the largest AI-driven healthcare data platform. Our model enables payers to make faster, more accurate decisions across their populations. By guiding members to the right care, Onos is channeling more dollars to high-quality care that drives outcomes while making healthcare more affordable.
Come join a category-defining company and transform how healthcare is managed in the U.S.
Why Onos?Meaningful impact: Help fix what is fundamentally broken in healthcare
Direct collaboration: Work alongside experienced founders with deep healthcare and data expertise
Culture: Join a high-performing, transparent, and results-oriented team
Ownership: Significant responsibility and autonomy from day one
Opportunity: Play a pivotal role in building a fast-growing, category-defining healthcare AI company
We’re looking for a Lead Data Scientist who thrives in a fast-moving startup, loves solving complex problems in messy healthcare data, and wants to directly shape how payers manage care. This is a highly strategic and hands-on role at the intersection of data science, product, and clinical operations.
As Onos’ lead data scientist, you’ll own the development of our core analytics and AI models, from designing predictive frameworks to measuring clinical appropriateness and cost savings. You’ll partner closely with product, engineering, and client teams to translate data into measurable impact for health plans, driving smarter utilization, improved outcomes, and reduced waste across Behavioral Health.
What you'll be doing at Onos:Model Development: Design and deploy predictive, risk-scoring, and optimization models that identify waste, inappropriate utilization, and care improvement opportunities across behavioral health services.
AI/ML Infrastructure: Help define and evolve our data science stack, from feature stores and pipelines to model monitoring and evaluation frameworks.
Data Exploration & Analysis: Dive deep into claims and clinical data to uncover trends, outliers, and actionable insights.
Client & Product Impact: Partner with client teams to translate complex models into clear insights that demonstrate ROI, inform payer workflows, and maintain clear, impactful dashboards.
Cross-Functional Leadership: Collaborate with the CEO, CPO, and engineering team to guide product direction, data strategy, and key client engagements.
8+ years of experience in data science or advanced analytics (preferably in healthcare or health plans; experience with claims and clinical data strongly preferred).
Deep expertise in statistical modeling, causal inference, and ML (Python, SQL, and related libraries such as scikit-learn, statsmodels, PyTorch, or similar).
Familiarity with healthcare data standards (claims, eligibility, EHR/clinical data, coding sets like ICD, CPT, HCPCS)
Experience building production-grade models and deploying them in analytical or product environments.
Proven experience building, mentoring, and leading high-performing data science or analytics teams.
Scrappy with an entrepreneurial mindset: resourceful, proactive, thrives in ambiguity, and moves fast
Excellent communication skills an ability to translate complex data into clear business insights
Exceptional references from colleagues and former managers
Flexible hybrid arrangement: ~3 days/week at San Francisco office (Financial District)
Unlimited vacation policy
Paid parental leave
Medical, dental, and vision insurance
Pre-tax commuter benefits
401(k)
Significant equity as an early employee
Direct mentorship from experienced founders
Ground-floor opportunity to help build a team and culture
Regular team events and off-sites
Company-provided equipment and home office setup
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
