Director, Forward-Deployed AI Solutions

ROLE OVERVIEW

As a forward-deployed AI operator within Real-World Data Solutions (RWDS), you will work at the intersection of people, process, data, and technology to turn messy operational challenges into AI-enabled systems that perform in daily work.

You will embed directly with the teams doing the work: observing how workflows actually operate, identifying the right intervention, building the smallest useful solution, testing it with real users, and improving it until it delivers measurable value. Sometimes that solution will use AI; sometimes deterministic automation, an existing platform, or a process redesign will be the better answer. Your job is to make that judgment and own the outcome.

This is a senior, hands-on builder role for an operations-minded problem solver—not a traditional software engineering position. You should be comfortable building with modern AI, automation, analytics, and low-code tools, while partnering with data and engineering teams when a solution requires production-grade infrastructure.

Because this role sits at the center of how RWDS delivers for clients, the work you do here should show up in two core ways: how clients experience working with us, and how profitably we operate

KEY RESPONSIBILITIES

• Discover and document how work actually happens: the people, approvals, exceptions, and informal tools (spreadsheets, Slack threads, workarounds) that fill the gaps in official process
• Build and ship AI-enabled tools, workflows, and automations directly with the analysts and teams who’ll use them
• Own your tools past the demo stage — make sure the underlying data logic is sound and the tool holds up as usage scales, partnering with engineering to productionize and incorporate into enterprise tooling
• Use current AI/LLM tooling (agents, RAG, coding assistants) to build faster, and know when a lightweight tool or application is the right call versus when it needs to be a real engineered solution
• Design appropriate human review, exception handling, monitoring, and governance into each solution from the beginning
• Actively gather voice-of-client feedback from analysts and business stakeholders to determine what should be built
• Pilot new tools with a small group of real users before scaling team-wide, and use that feedback to shape the broader rollout
• Communicate technical decisions and tradeoffs clearly to non-technical stakeholders
• Take ownership of a defined portion of the AI/tooling roadmap for RWDS with minimal oversight — you’re expected to drive, not just execute against someone else’s plan
• Train the RWDS and dependent teams on AI tooling and maintain ongoing communication to support adoption

QUALIFICATIONS

Required

• Ability to understand data structures, trace data through a workflow, validate business logic, and collaborate effectively with analysts and engineers
• Track record of driving a project or initiative from ambiguous problem to shipped solution with real adoption, without needing heavy oversight
• Demonstrated experience with LLM-based tools and practical AI integration patterns
• Understanding of responsible AI and data governance in a regulated industry
• Able to recognize when a lightweight solution is appropriate—and when security, scale, data complexity, or reliability requires engineering partnership
• Strong communication skills — able to explain technical tradeoffs to non-technical stakeholders and turn a vague business ask into a scoped technical plan

Preferred

• Analytics/data engineering, or ETL background, or equivalent depth working directly with large relational databases (SQL, Python, Databricks, or equivalent)
• Experience building playbooks, training programs, or enablement materials that scaled technology adoption across a team
• Some prior leadership or informal mentorship experience — this role is expected to grow into more ownership over time
• Experience evaluating or piloting new AI platforms and tools, and making a real build-vs-buy call rather than just experimenting
• Exposure to complex analytics workflows leveraging large data environments
• Familiarity with real-world healthcare data (claims, EHR, labs, registries, or similar patient-level datasets) and how it’s used in life sciences work

We welcome portfolios, capstone projects, workflow maps, automation examples, or case studies that demonstrate how you diagnosed a problem, chose an approach, built and deployed a solution, earned user a…