# Director of Data Solutions

**Company:** [Axle](https://hotfix.jobs/companies/axle)
**Location:** Rockville, MD
**Role:** Engineering Management
**Salary:** $170k – $210k/yr
**Experience:** 6+ years
**Skills:** Data Engineering, Ml Engineering, MLOps, Llmops, ETL, ELT, Cloud Computing, Generative AI, RAG, Kubernetes, Hpc, Omop, FHIR, NLP, Computer Vision
**Posted:** 2026-04-29

> 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.

## Job Description

## 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

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