Senior Data Scientist, Healthcare Operations

The Role

Hone is looking for a Senior Data Scientist, Healthcare Operations to join our data science team. In this role, you will report to the Sr. Director, Data, Analytics, and Machine Learning and focus on applying data science and AI to improve clinical outcomes and physician productivity across Hone’s virtual care platform.

You will work closely with clinical leadership, product, and engineering to design, build, and evaluate models and AI-powered workflows that support care delivery, reduce operational friction, and enable clinicians to practice at the top of their license. This is a senior individual contributor role focused on execution and delivery, with ownership over well-scoped, high-impact projects. This role is ideal for someone who thrives at the intersection of healthcare, data science, and applied AI—and who wants their work to have direct, measurable impact on patient care.

Primary Responsibilities

Key responsibilities for this role include (but are not limited to) the following:

• Partner with Clinical, Medical, and Operations teams to apply data science and AI to longevity-focused care delivery, including hormone optimization, metabolic health, cardiovascular risk, and preventative care pathways.
• Develop and evaluate models and AI-driven workflows that support clinicians in delivering personalized, evidence-based longevity care at scale.
• Design and implement agentic AI workflows to assist with tasks such as longitudinal chart review, lab trend interpretation, clinical summarization, follow-up planning, and patient outreach.
• Analyze longitudinal patient data; including labs, vitals, prescriptions, and engagement signals; to identify opportunities to improve outcomes, adherence, and care consistency over time.
• Build analytical frameworks to measure clinical effectiveness, physician productivity, and patient safety in a virtual care environment.
• Collaborate with product and engineering teams to integrate AI-powered tools directly into telehealth clinical workflows, ensuring high usability and clinician trust.
• Define and track key performance indicators related to longevity outcomes (e.g., biomarker improvement, therapy adherence, patient engagement) and operational efficiency (e.g., time per visit, panel capacity).
• Evaluate and monitor AI-assisted clinical tools with a focus on reliability, bias, and real-world performance, incorporating clinician feedback and human-in-the-loop controls.
• Communicate insights, tradeoffs, and recommendations clearly to clinical leaders and cross-functional stakeholders.
• Contribute to shared standards and best practices for responsible, compliant AI use in longevity telehealth.

Qualifications

In order to qualify for this role, candidates must meet the following criteria:

• 5+ years of professional experience in Data Science, Machine Learning, Analytics, or a related quantitative role.
• Master’s degree in a quantitative field (Statistics, Computer Science, Biomedical Informatics, etc.); advanced clinical training (e.g., MD, DO, RN, PA, NP) or equivalent healthcare experience is strongly preferred–we have multiple MDs in Data Science at Hone.
• Demonstrated experience working with healthcare data, clinical workflows, or care delivery systems.
• Demonstrated experience analyzing video with computer vision techniques
• Strong proficiency in Python for data analysis, modeling, and applied machine learning.
• Strong proficiency in SQL and experience working with complex, production healthcare datasets.
• Hands-on experience building and evaluating applied ML or AI systems in real-world settings.
• Familiarity with modern AI techniques, including LLM-based systems and agentic or workflow-driven AI approaches.
• Understanding of responsible AI considerations in healthcare, including safety, bias, and human oversight.
• Experience collaborating with cross-functional partners in product, engineering, and clinical teams.
• Strong communication skills, with the ability to translate complex technical concepts into clear, actionable guidance for clinical stakeholders.
• Ability to manage multiple projects independently and deliver high-quality work in a fast-paced environment.