Associate Director – Data and Modeling

This is a fully remote position, open to applicants in United States.

📋 Description

• Define the technical vision for data platforms, AI/ML systems, scientific computing environments, and modeling capabilities.

• Create reference architectures and reusable implementation patterns; determine when to build, modernize, adopt, or collaborate.

• Oversee the design and operation of systems that ingest, transform, harmonize, and serve extensive scientific and health datasets.

• Implement practices for data quality, validation, terminology translation, lineage, versioning, documentation, and change control.

• Lead AI/ML initiatives, including predictive modeling, computer vision, natural language processing, large language models, retrieval-augmented generation, and agentic workflows.

• Set standards for reproducible modeling, simulation, scientific workflows, versioned inputs and environments, computational strategy, and scientific validation.

• Transition prototypes into dependable production systems by enhancing testing, CI/CD, containerization, observability, release management, incident response, documentation, and technical-debt practices.

• Build and manage interdisciplinary teams in software, data, machine learning, data science, and computational science.

• Cultivate managers and technical leads, establishing clear roles, leadership pathways, and performance expectations.

• Develop an operational cadence with established priorities, risk checkpoints, release criteria, ownership, and progress metrics.

• Collaborate with security, privacy, governance, scientists, engineers, program leaders, and federal health partners to ensure the responsible use of sensitive data and AI.

• Promote publications, conference participation, open-source contributions, and engagement with the research software community, including stewardship of Polus.

• Act as a senior technical leader in federal growth, proposal, capture, solution architecture, staffing, and implementation initiatives.

⛳️ Requirements

• A minimum of eight years of progressively responsible experience in software engineering, data engineering, machine learning engineering, computational science, data science, or a closely related technical field.

• At least five years of leadership experience in building and guiding multidisciplinary technical teams, including responsibilities for hiring, technical direction, delivery, and staff development.

• Proven experience in personally designing, building, deploying, and operating production-grade data, AI/ML, software, or scientific computing systems.

• Strong technical judgment regarding modern data and AI architectures, distributed processing, containerized environments, CI/CD, MLOps or LLMOps, observability, and production operations.

• Demonstrated success in transitioning analytical or AI/ML initiatives from research and prototyping to reliable production use, including evaluation, deployment, monitoring, versioning, and ongoing operational ownership.

• Experience in building or leading large and complex data pipelines, with a focus on interoperability, data quality, lineage, reproducibility, and repeatable transformation.

• Experience leading modeling, simulation, scientific computing, or computational research work directly or closely in partnership with scientific subject matter experts.

• Capability to review technical designs, identify risks, challenge assumptions, resolve complex engineering issues, and differentiate promising emerging methods from those not yet ready for production use.

• Experience delivering technical systems in research-intensive, regulated, or high-governance environments involving sensitive data, security controls, privacy requirements, or formal technical oversight.

• Excellent communication skills with the ability to convey technical concepts to scientists, program leaders, executives, and government stakeholders.

• An advanced degree in a quantitative discipline is preferred.

• Preferred qualifications include hands-on technical experience, leading organizations with approximately 20 or more technical specialists, familiarity with biomedical data platforms and standards such as OMOP, FHIR, PCORnet, and CDISC, high-performance computing, Generative AI, biomedical datasets, technical publications or open-source contributions, and involvement with NIH or federal health organizations, as well as federal proposal or strategic partnership leadership.

🏝️ Benefits

• Comprehensive Medical, Dental & Vision Coverage for Employees.

• Paid Time Off and Paid Holidays.

• 401K matching up to 5%.

• Educational Benefits to support Career Growth.

• Employee Referral Bonus.

• Flexible Spending Accounts: Healthcare (FSA), Parking Reimbursement Account (PRK), Dependent Care Assistant Program (DCAP), Transportation Reimbursement Account (TRN).