Associate Director of Data and Modeling

About this role

(ID: 2026-3404) Axle Informatics is a bioscience and information technology company committed to accelerating biomedical discovery through data science, software engineering, scientific computing, and research technology. We work alongside scientists and federal health partners to solve complex technical problems, create durable research infrastructure, and make advanced computational methods more accessible to the people who can use them to advance science.

We believe the best technical organizations combine curiosity with discipline. They make room for experimentation, but they also finish what they start. They build systems that others can understand and sustain. They share knowledge, invest in people, and measure success by what their work enables the research community to accomplish.

If building that kind of organization, and helping it solve some of the most difficult data and computational challenges in biomedical research, excites you as much as it excites us, we would love to talk.

Benefits We Offer:

• 100% Medical, Dental & Vision Coverage for Employees

• Paid Time Off and Paid Holidays

• 401K match up to 5%

• Educational Benefits for Career Growth

• Employee Referral Bonus

• Flexible Spending Accounts:

• Healthcare (FSA)

• Parking Reimbursement Account (PRK)

• Dependent Care Assistant Program (DCAP)

• Transportation Reimbursement Account (TRN)

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 tech

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