Associate Director of Data and Modeling

Responsibilities

Technical Strategy and Stewardship

• Set technical direction for data platforms, AI/ML systems, scientific computing environments, and modeling capabilities.
• Establish reference architectures and reusable implementation patterns.
• Decide when to build, modernize, adopt, or partner with long-term sustainability in mind.

Production Data Platforms

• Guide the design and operation of systems that ingest, transform, harmonize, and serve large scientific and health datasets.
• Establish repeatable approaches for data quality, validation, terminology translation, lineage, versioning, documentation, and change control.

AI/ML and Emerging Methods

• Lead AI/ML capabilities including predictive modeling, computer vision, natural language processing, large language models, retrieval-augmented generation, and agentic workflows.
• Require evaluation, traceability, privacy safeguards, human review where appropriate, and post-deployment monitoring.

Modeling, Simulation, and Scientific Computing

• Build a sustainable modeling and simulation practice.
• Establish standards for reproducible workflows, versioned inputs and environments, compute strategy, and scientific validation.

From Research to Reliable Systems

• Help teams turn prototypes into dependable production capabilities.
• Strengthen testing, CI/CD, containerization, observability, release management, incident response, documentation, and technical-debt practices.

Technical Organization Leadership

• Build and lead multidisciplinary teams spanning software engineering, data engineering, machine learning engineering, data science, and computational science.
• Create technical leadership paths and develop managers and technical leads.

Program Execution and Quality

• Establish priorities, risk checkpoints, release criteria, ownership, and progress measures.
• Sequence work thoughtfully, address technical debt, and communicate tradeoffs.

Governance, Security, and Responsible Use

• Partner with security, privacy, governance, and scientific stakeholders in sensitive, highly governed environments.
• Promote access controls, auditability, intended-use controls, model review, data minimization, privacy, and responsible AI.

Open Science and Community Engagement

• Encourage technical publication, conference participation, open-source contribution, and research software community engagement.
• Support stewardship of reusable scientific platforms and tools, including Polus.

Technical Growth and Partnership

• Contribute as a senior technical leader to federal growth and proposal efforts.
• Shape solution architectures, technical approaches, staffing models, and implementation strategies.

Requirements

• Eight or more years of progressively responsible experience in software engineering, data engineering, machine learning engineering, computational science, data science, or a closely related technical discipline.
• Five or more years of leadership experience building and guiding multidisciplinary technical teams.
• Experience personally designing, building, deploying, and operating complex data, software, AI/ML, or scientific computing systems.
• Experience with technical strategy, production systems, team leadership, delivery practices, governance, security, privacy, and responsible AI.

Benefits

• 100% medical, dental, and vision coverage for employees.
• Paid time off and paid holidays.
• 401(k) match up to 5%.
• Educational benefits for career growth.
• Employee referral bonus.
• Flexible spending accounts for healthcare, parking, dependent care, and transportation.