Associate Director of Data and Modeling

Position Overview

Axle Informatics is excited to open the search for an Associate Director of Data and Modeling to help shape the next generation of data-intensive biomedical research. This role will lead teams working across data engineering, artificial intelligence and machine learning, scientific computing, and modeling and simulation to build capabilities that make complex research data more useful, reproducible, and actionable.

The goals this position fills are ambitious; turn difficult scientific and technical problems into durable systems, create the conditions for highly technical teams to do their best work, and ensure that promising ideas become reliable capabilities that researchers can trust.

This is not a role for someone who has only advised technical teams from a distance. The Associate Director must bring the judgment that comes from having personally designed, built, deployed, and operated complex data, software, AI/ML, or scientific computing systems. You will be expected to engage deeply enough to recognize weak assumptions, ask the questions that change a design, help teams resolve difficult technical tradeoffs, and know when an experimental approach is ready to become part of a production environment.

At the same time, the role is larger than any single architecture, model, or platform. You will build and lead multidisciplinary teams, establish shared technical standards, develop emerging leaders, strengthen the operating systems that make delivery predictable, and create reusable approaches that can serve multiple research programs. You will work closely with scientists, engineers, program leaders, security and privacy teams, and federal health partners to connect technical excellence with meaningful scientific outcomes.

Our vision is a future where data, models, simulations, workflows, and analytical tools can be used together with less friction and greater confidence. We value scientific rigor, technical craftsmanship, reproducibility, openness, and service to the research community. If those values energize you, we would love to meet you.

Key Responsibilities: Summary

• Technical Strategy and Stewardship: Set the technical direction for data platforms, AI/ML systems, scientific computing environments, and modeling capabilities. Establish reference architectures and reusable implementation patterns that help teams make sound decisions while preserving room for experimentation. Decide when to build, modernize, adopt, or partner, and make those decisions with long-term sustainability in mind.

• Production Data Platforms: Guide the design and operation of data systems that can ingest, transform, harmonize, and serve large, heterogeneous scientific and health datasets. Build repeatable approaches for data quality, validation, terminology translation, lineage, versioning, documentation, and change control so that data products remain understandable and trustworthy as programs evolve.

• AI/ML and Emerging Methods: Lead the development of AI/ML capabilities where they can create measurable scientific or operational value, including predictive modeling, computer vision, natural language processing, large language models, retrieval-augmented generation, and agentic workflows. Require thoughtful evaluation, traceability, privacy safeguards, human review where appropriate, and monitoring that continues after deployment.

• Modeling, Simulation, and Scientific Computing: Build a sustainable modeling and simulation practice that supports both specialized scientific work and reusable organizational capability. Establish standards for reproducible workflows, versioned inputs and environments, compute strategy, and scientific validation. Partner effectively with domain experts when the deepest subject matter expertise resides outside your own discipline.

• From Research to Reliable Systems: Help teams cross the difficult gap between promising prototypes and dependable production capabilities. Strengthen engineering practices around testing, CI/CD, containerization, observability, release management, incident response, documentation, and technical debt. Preserve the creativity of research environments while introducing the discipline required for systems that others depend on.

• Technical Organization Leadership: Build and lead multidisciplinary teams spanning software engineering, data engineering, machine learning engineering, data science, and computational science. Create clear roles, strong technical leadership paths, and expectations that reward both rigor and collaboration. Develop managers and technical leads who can make good decisions without creating single points of failure.

• Program Execution and Quality: Create an operating cadence that makes complex technical delivery visible and predictable. Establish clear priorities, risk checkpoints, release criteria, ownership, and measures of progress. Help teams sequence work thoughtfully, address technical debt without losing momentum, and communicate tradeoffs before they become surprises.

• Governance, Security, and Responsible Use: Work with security, privacy, governance, and scientific stakeholders to ensure that data and AI capabilities are appropriate for sensitive and highly governed environments. Promote practical controls for access, auditability, intended use, model review, data minimization, privacy, and responsible AI without allowing governance to become disconnected from how systems are actually built and used.

• Open Science and Community Engagement: Encourage technical publication, conference participation, open-source contribution, and active engagement with the broader research software community. Support continued stewardship of reusable scientific platforms and tools, including Polus, and look for opportunities where open collaboration can increase impact beyond a single project or client.

• Technical Growth and Partnership: Contribute to selected federal growth and proposal efforts as a senior technical leader. Shape credible solution architectures, technical approaches, staffing models, and implementation strategies. Help Axle pursue work that matches its technical strengths and can be executed with the same standards expected of its active programs.

Required Qualifications

• 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, including responsibility for hiring, technical direction, delivery, and staff development.

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

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

• Demonstrated success moving analytical or AI/ML work from research and prototyping into reliable production use, including evaluation, deployment, monitoring, versioning, and ongoing operational ownership.

• Experience building or leading large and complex data pipelines with attention to interoperability, data quality, lineage, reproducibility, and repeatable transformation.

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

• Ability to review technical designs, identify risk, challenge assumptions, resolve difficult engineering problems, and distinguish promising emerging methods from approaches that are 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.

• Strong communication skills and the ability to move comfortably between detailed technical discussion and clear explanation for scientists, program leaders, executives, and government stakeholders.

Preferred Qualifications

• Advanced degree in computer science, bioinformatics, computational biology, data science, engineering, applied mathematics, physics, or another quantitative discipline.

• Prior experience as a software engineer, data engineer, machine learning engineer, computational scientist, or equivalent hands-on technical practitioner before moving into broader leadership.

• Experience leading organizations of approximately 20 or more engineers, scientists, and technical specialists, including managers or senior technical leads.

• Experience with biomedical or health data platforms and standards such as OMOP, FHIR, PCORnet, CDISC, or related clinical terminology systems.

• Experience with high-performance computing, large-scale scientific workflows, workflow orchestration, containerized research environments, or petabyte-scale scientific data.

• Experience deploying Generative AI capabilities with evaluation, retrieval, traceability, privacy controls, human review, monitoring, and appropriate safeguards.

• Experience applying AI/ML to scientific imaging, genomics, proteomics, real-world data, clinical data, or other high-dimensional biomedical datasets.

• Track record of technical publications, conference presentations, open-source contributions, patents, or other recognized technical leadership.

• Experience working with NIH or other federal health and biomedical research organizations.

• Experience serving as a technical or solution lead for federal proposals, capture efforts, or strategic partnerships.