Associate Director of Data and Modeling

Role overview Lead multidisciplinary teams building data platforms, AI and machine learning systems, scientific computing environments, and modeling capabilities for biomedical research. This is a hands-on technical leadership role: you will guide strategy and delivery while drawing on experience designing, deploying, and operating complex systems.

Responsibilities - Set technical direction and establish reusable architectures for data, AI/ML, scientific computing, and modeling work. - Guide production data platforms for large scientific and health datasets, including practices for quality, validation, lineage, versioning, and documentation. - Advance AI/ML methods from research into dependable use, with appropriate evaluation, privacy safeguards, traceability, and ongoing monitoring. - Lead modeling, simulation, and computational research in partnership with scientific experts. - Build and develop technical teams, strengthen delivery practices, and establish standards that support reliable work across programs. - Work with researchers, engineers, program leaders, and security and privacy specialists; contribute technical approaches to selected federal proposals and partnerships.

Requirements - At least 8 years of relevant technical experience in software, data engineering, machine learning, computational science, or a closely related field. - At least 5 years leading multidisciplinary technical teams, including technical direction, delivery, hiring, and staff development. - Personal experience designing, building, deploying, and operating production data, AI/ML, software, or scientific computing systems. - Strong judgment across data and AI architectures, distributed processing, containers, CI/CD, MLOps or LLMOps, observability, and production operations. - Experience taking analytical or AI/ML work from prototype to monitored, versioned production use. - Experience with complex data pipelines and with modeling, simulation, scientific computing, or computational research.

Nice to have - Biomedical or health data standards such as OMOP, FHIR, PCORnet, or CDISC; large-scale scientific workflows or high-performance computing; or applied AI/ML in biomedical datasets. - Generative AI deployment experience, technical publications or open-source work, federal biomedical research experience, or technical leadership in proposals.

Benefits and work setup Benefits include employee medical, dental, and vision coverage, paid time off and holidays, a 401(k) match, education support, referral bonuses, and flexible spending and transportation accounts.