USADirector of Data Solutions
4 мес. назад
USADirector
aimldata platformscloud
Senior technical delivery leader for data platforms, AI/ML solutions, and advanced modeling/simulation capabilities, setting technical strategy and architecture.
Требования
- 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).
Будет плюсом
- 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.
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- Disclaimer: The above description is meant to illustrate the general nature of work and level of effort being performed by individuals assigned to this position or job description. This is not restricted as a complete list of all skills, responsibilities, duties, and/or assignments required. Individuals may be required to perform duties outside of their position, job description or responsibilities as needed.
- The diversity of ’s employees is a tremendous asset. We are firmly committed to providing equal opportunity in all aspects of employment and will not tolerate any illegal discrimination or harassment based on age, race, gender, religion, national origin, disability, marital status, covered veteran status, sexual orientation, status with respect to public assistance, and other characteristics protected under state, federal, or local law and to deter those who aid, abet, or induce discrimination or coerce others to discriminate.
- Accessibility: If you need an accommodation as part of the employment process please contact: careers@
- This role has a market-competitive salary with an anticipated base compensation range listed below. Actual salaries will vary depending on a candidate’s experience, qualifications, skills, and location.
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Другое
- (ID: 2026-1572)
- 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).
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- Benefits We Offer:
- 100% Medical, Dental & Vision Coverage for Employees
- 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)
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- 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.
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- 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.
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- 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.