Director of Data Solutions

 

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.

 

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.