Responsibilities
- Build, mentor, and scale a world-class AI/ML team, establishing technical standards, career development frameworks, and a culture of excellence and ownership.
- Define and execute the ML roadmap while partnering closely with Engineering to architect data warehousing solutions, ML infrastructure, and data pipelines that enable the team to rapidly prototype and deploy models at scale.
- Contribute directly to critical modeling, evaluation, and analysis work, from studies to model performance experiments, ensuring the team ships high-quality ML systems that deliver measurable clinical impact.
- Collaborate with Engineering, Product, and Clinical to translate complex clinical workflows into ML opportunities, and communicate model performance and impact to technical and non-technical stakeholders including customers and investors.
Requirements
- Ph.D. in Machine Learning, Computer Science, Statistics, or related field with 8+ years shipping ML products, and 3+ years leading ML teams at early stage startups
- Proven track record building and scaling high-performing data science and ML engineering teams in resource-constrained, scrappy environments.
- Deep technical expertise in production ML systems and data infrastructure, including hands-on experience with data warehousing, real-time prediction, model monitoring, and performance evaluation.
- Experience working with healthcare or similarly regulated industries where model decisions have high-stakes real-world consequences.
Nice to Have
- Experience leading ML organizations through 0-1 product development in healthcare or clinical settings, thriving in environments with limited tooling and infrastructure.
- Hands-on experience with clinical data standards (HL7, FHIR, EHR) and healthcare ML challenges including data quality, time-series forecasting, and anomaly detection.
- Strong technical background in data platform architecture, including modern data warehousing solutions (Snowflake, Databricks, Redshift), streaming data systems, and ML infrastructure tools.
- Track record of publishing research, speaking at conferences, or contributing to the broader ML community while delivering business results.
