RWD Program Lead

 

The Real-World Data (RWD) Program Lead is accountable for the mission, partner ecosystem, governance, and outcomes of a national-scale clinical data and analytics capability that enables high-impact translational research and evidence generation.

 

This leader partners with clients and stakeholder organizations (e.g., federal sponsors, academic medical centers, health systems, research networks) to co‑define and evolve the platform’s:

• Multi‑year strategy and operating model
• Outcome-based priorities and success measures (e.g., adoption, time-to-access, data quality, study throughput)
• High level architecture and approach for the platform and its various components
• High level understanding of legal and regulatory hurdles of RWD programs
• Research enablement agenda (what types of evidence and capabilities the platform must support)

 

This role serves as the senior client-facing leader for a national-scale real-world data program. The role partners closely with clients, sponsors, and stakeholders to collaboratively define multi-year strategy, outcome-based priorities, success measures, and governance approaches. Through these partnerships, the role ensures alignment between stakeholder goals, scientific impact, trust, and long-term platform sustainability.

The role leads the “what and why” of the platform, translating client needs into clear strategic direction, adoption goals, and high-level architectural intent. The role works in close collaboration with senior technical delivery leaders, architects, and data science and engineering teams who own detailed design and implementation, ensuring that execution remains aligned with client-defined priorities, governance expectations, and desired outcomes.
 

KEY RESPONSIBILITIES

Client partnership, strategy, and success measures

• Work with clients and stakeholder leaders to co‑define and refresh a multi‑year strategy for a national-scale RWD platform/program.

• Translate the jointly defined strategy into outcome-based priorities, quarterly objectives, and measurable success metrics.

• Facilitate decision-making on tradeoffs (scope, speed, risk, cost) while maintaining stakeholder alignment and trust.

National stakeholder ecosystem leadership

• Serve as a senior strategic partner and trusted advisor to a nationwide community of stakeholders (executive leaders at partner institutions, sponsors, research community leaders).

• Act as the “front door” for the platform/program: establish credibility, manage expectations, and align priorities across diverse partners.

• Lead stakeholder communications: executive updates, community-facing narratives, and transparent change management.

Governance, ethics, privacy, and compliance

• Establish and operate governance frameworks that enable compliant, ethical use of patient data under standards such as HIPAA and the Common Rule (as applicable).

• Oversee and continuously improve data access and use processes (e.g., master data sharing terms, data access committee review, auditing, and monitoring).

• Partner with privacy/security/legal teams to maintain a strong risk posture while enabling research speed and usability.

Program and product leadership (the “what/why”)

• With clients and internal leadership, define the program’s roadmap outcomes across:

  • data warehouse evolution and scalability

  • semantic layer and cohort discovery usability

  • data quality and completeness improvements

  • modernization priorities and technical debt reduction goals

• Partner with the senior technical delivery leader to ensure roadmap feasibility, sequencing, and delivery accountability.

Research enablement and scientific impact

• Shape (with clients and scientific leadership) the set of core analytical capabilities the platform must support (e.g., reproducible RWD workflows, cohort definitions, phenotyping, causal inference methods).

• Ensure research outputs are operationally supported: study intake pathways, reproducibility expectations, and publication/communication readiness.

• Promote responsible innovation (including AI/automation) through clear governance, transparency, and evaluation.

Operating model and execution leadership

• Lead a cross-functional internal/external operating model (engineers, scientists, contractors, operations) to deliver on outcomes.

• Establish a rhythm of business: steering cadence, decision logs, backlog governance, status reporting, and risk/issue management.

• Manage program-level dependencies, vendor/contractor performance (as applicable), and budget/resource alignment.

REQUIRED QUALIFICATIONS

• 10+ years of progressively responsible experience in health data, research informatics, clinical analytics, RWD programs, or related domains.

• Demonstrated success leading large, multi-stakeholder programs or consortia (multi-institution, national, or enterprise-scale), including governance and operating cadence.

• Proven ability to partner with clients/sponsors to define strategy, priorities, and success measures—then drive execution against them.

• Experience establishing or operating data governance and data access processes (e.g., data sharing terms, access committees, privacy review, auditing).

• Comfort leading within regulated environments involving sensitive health data; working fluency with privacy and compliance concepts (HIPAA/Common Rule, de-identification/limited datasets, etc., as applicable).

• Strong communication skills: can translate complex topics into executive-ready narratives; credible with senior scientific and operational leaders.

• Demonstrated leadership of cross-functional teams (internal and external), including conflict resolution and stakeholder alignment.

PREFERRED QUALIFICATIONS

• Experience with national or multi-site clinical data networks, research consortia, public-private partnerships, or large-scale translational research programs.

• Working familiarity with common clinical data standards and interoperability concepts (e.g., OMOP, FHIR, PCORNet; terminology systems such as ICD-10, SNOMED, LOINC, RxNorm).

• Track record of enabling reproducible evidence generation (e.g., pragmatic trials, target trial emulation, observational causal inference, high-impact RWE/RWD studies).

• Experience partnering with cloud/data platform teams and product organizations (even if not hands-on engineering).

• Experience implementing AI/automation governance for clinical or research analytics (model transparency, evaluation, safety review, monitoring).

• Advanced degree in a relevant field (public health, informatics, biostatistics, data science, medicine, or related) or equivalent experience.