Director of Data Solutions

Overview

Axle is a bioscience and information technology company that offers advancements in translational research, biomedical informatics, and data science applications to research centers and healthcare organizations nationally and abroad. With experts in biomedical science, software engineering, and program management, we focus on developing and applying research tools and techniques to empower decision-making and accelerate research discoveries. We work with some of the top research organizations and facilities in the country including multiple institutes at the National Institutes of Health (NIH).

• 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)

The Director of Data Solutions is the senior technical delivery leader for data platforms, AI/ML solutions (including GenAI), and advanced modeling/simulation capabilities. This leader owns the “how and when” of building reusable, enterprise-grade capabilities that turn complex, multi‑modal data into trusted products and measurable outcomes.

In practice, this role:

• Sets technical strategy and reference architecture across modern data stacks and cloud environments.
• Leads cross-functional teams (data engineering, ML engineering, applied science, software engineering) to ship and operate production systems.
• Establishes an organization-wide modeling and simulation practice that ensures reproducibility, compute strategy, and strong quality standards.

Axle role identifier:  

• Define reference architectures and technical standards for data/AI platforms (security, scalability, reliability, cost governance, developer experience).
• Own platform modernization plans and technical debt reduction sequencing.
• Make build/buy/partner decisions and establish patterns that can be reused across programs.

• Lead delivery of repeatable ingestion and transformation pipelines with testing, validation, and change control.
• Own harmonization capabilities (terminology translation, unit normalization, episode building) as production services with documentation and quality dashboards.
• Partner with governance and stakeholders to define “minimum acceptable quality” and publish transparent quality measures.

• Lead delivery of production AI/ML solutions (NLP, CV, predictive models, representation learning) and deploy them with evaluation and monitoring.
• Own GenAI patterns and platforms (RAG, agentic workflows, human-in-the-loop review, traceability, privacy safeguards) as reusable services.
• Establish model lifecycle governance: approvals, audits (as needed), drift monitoring, incident response, and continuous improvement.

• Build reusable “engines” for RWE execution: cohorting/phenotyping pipelines, reproducible protocol templates, causal inference/target trial tooling patterns, and integration templates for multiple data sources.
• Staff and support analysis pods for time-sensitive, high-stakes deliverables with rigorous QC and reproducibility practices.

• Define the modeling/simulation practice charter: scope, service model, standards, compute strategy (HPC/cloud), and hiring/partnering plan.
• Lead simulation/modeling teams directly or via domain SMEs; ensure reproducible workflows and high quality bars.
• Identify and prioritize high-value hybrid ML+simulation opportunities.

• Partner with security/privacy to implement strong access controls, auditability, and (where needed) privacy-preserving approaches.
• Establish operational excellence: release management, observability, on-call/incident processes (as appropriate), and runbooks.

• Hire, grow, and retain a high-performing organization; create clear roles, career paths, and performance expectations.
• Build a culture of “research-grade rigor +…