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Job Summary:
Axle is a bioscience and information technology company specializing in translational research, biomedical informatics, and data science applications. The Director of Data Solutions will lead technical delivery for data platforms and AI/ML solutions, setting strategy and overseeing cross-functional teams to build enterprise-grade capabilities and ensure high-quality data products.
Responsibilities:
• 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 + production-grade discipline,' emphasizing accountability, documentation, and sustainability.
Qualifications:
Required:
• 6+ years in data science, ML engineering, data platform engineering, applied research engineering, or closely related fields
• 3+ years leading multi-disciplinary teams.
• Demonstrated success delivering production data/AI platforms (not only analyses), including architecture, delivery planning, and operational ownership.
• Strong familiarity with modern data stacks and cloud delivery (distributed compute, ETL/ELT, data quality tooling, MLOps/LLMOps concepts).
• Ability to translate ambiguous stakeholder needs into shipped products and measurable outcomes.
• Strong people leadership: recruiting, coaching, performance management, org design.
• Comfort operating in regulated and high-governance environments (privacy, compliance, access control).
Preferred:
• Healthcare data platform experience, especially interoperability/harmonization at scale (OMOP/FHIR/PCORNet/CDISC) and clinical terminology systems.
• Experience shipping GenAI solutions with governance (PII handling, traceability, human review, evaluation, monitoring).
• Experience with privacy-preserving ML patterns (federated learning/inference) and/or sensitive data platforms.
• Experience leading simulation/modeling initiatives (scientific computing, HPC workflows, domain simulations) and partnering effectively with scientific SMEs.
• Track record of publications, open-source leadership, or scientific impact.
Company:
At Axle, we are driven by the mission to accelerate discovery and enhance organizational outcomes by revolutionizing operations with our innovative solutions. Founded in 2002, the company is headquartered in Rockville, USA, with a team of 501-1000 employees. The company is currently Late Stage.