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AxleAxle

Director of Data Solutions

Leads technical strategy, architecture, and delivery of enterprise data platforms, AI/ML solutions including GenAI, and modeling/simulation practices. Oversees cross-functional teams to build reusable capabilities from complex data, ensuring production-grade quality and governance. Requires 6+ years experience and strong leadership.

About the job

Key Responsibilities

Technical strategy & architecture

  • 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.

Interoperability, harmonization & data quality

  • 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.

AI/ML and GenAI solution delivery

  • 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.

Real-world evidence enablement engines

  • 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.

Simulations & modeling practice leadership

  • 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.

Privacy, security & operational excellence

  • 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.

People leadership & culture

  • 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.

Required Qualifications

  • 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 Qualifications

  • 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.

Salary Range: $170,000—$210,000 USD

Skills

Data Engineering, Ml Engineering, MLOps, Llmops, ETL, ELT, Cloud Computing, Generative AI, RAG, Kubernetes, Hpc, Omop, FHIR, NLP, Computer Vision

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