Note: The job is a remote job and is open to candidates in USA. TailorCare is transforming the experience of specialty care by improving patient outcomes through a personalized, evidence-based approach. The Director of AI - Decision Intelligence will lead a team focused on leveraging data to enhance patient engagement and optimize care pathways, while ensuring scalability and technical quality in a fast-growing environment.
Responsibilities
- Lead a team of outcome-driven data scientists and ML engineers, with direct accountability for delivery, technical quality, and growth
- Drive cross-functional partnership with Medical Economics, Clinical Operations, Product, and the Data & Intelligence Foundation team
- Own the interface between modeling work and the platform and infrastructure it runs on
- Make build-versus-buy and architecture calls, set the technical bar, and stay hands-on enough to make modeling decisions yourself
- Other duties as assigned
Skills
- Master's or PhD in a quantitative field (computer science, statistics, machine learning, operations research, applied mathematics, economics, or a closely related discipline). This is a requirement for the role; a PhD with applied, production-oriented research is a strong plus
- A demonstrable history of ML systems you shipped to production that moved a business or clinical metric, with the specifics of what you built, what changed, and how it was measured
- Evidence of delivering against hard external deadlines and managing data-dependency risk without slipping quality
- A record of building and growing high-performing technical teams, including hiring, leveling, and developing data scientists and ML engineers
- Experience owning a model portfolio across its full lifecycle, retiring or refactoring models that no longer earn their place
- Ability and willingness to travel up to 10% as needed for onsite meetings, team collaboration, and company events
- Deep applied ML: supervised learning on tabular and structured data, gradient-boosted trees (XGBoost, LightGBM), feature engineering, calibration, and rigorous offline and online evaluation
- Production ML engineering: model packaging, deployment, monitoring, drift detection, and retraining pipelines. You own model quality in production, not just in a notebook
- Strong software engineering fundamentals: Python, SQL, version control, testing, and code review standards you can set and enforce
- Modern data and ML platform fluency: Databricks, dbt, and AWS (S3, Postgres, DynamoDB). Comfortable making build-versus-buy and architecture calls
- Experimentation and causal rigor: A/B testing, uplift modeling, and the judgment to distinguish correlation from decision-relevant signals
- Sound judgment on where newer methods (LLMs, agents, feature augmentation from external signals) add measured lift versus where they add cost and risk
- You lead with the recommendation and state risks plainly, escalate risk early, and decide fast. Communication is concise and structured
- Healthcare, payer, or value-based care experience, and familiarity with HIPAA-regulated data
- Experience translating actuarial or medical-economics concepts into model features and targets
- Published or peer-reviewed work in applied ML, forecasting, or causal inference
Benefits
- Our generous paid time off (PTO) and holiday plans ensure you have ample time to rest and recharge.
- We offer paid parental leave and support a healthy work-life balance, encouraging flexibility and autonomy.
- From Day 1, employees enjoy medical, dental, vision, life, and disability insurance, wellness resources and an employer HSA contribution.
- We are committed to equitable pay for all team members and support your future goals with a 401k plan that includes employer matching.
- We operate as a remote-first company with options for a hybrid work model in Nashville.
Company Overview