Job Description — Product Manager, Commercial & Technical (AI & Data Science)

Role summary

We’re hiring ahands-on Product Managerwho sits at the intersection ofcustomer discovery,technical execution, andcommercial strategy. This is a**builder/operator (“product ninja”)**role — not a pure strategy role. You’ll get your hands dirty: writing epics, shaping requirements, driving PRDs, partnering with engineering to translate into stories, and staying close to delivery realities.

You’ll be theglue across Sales, Clinical Ops, Engineering/R&D, Finance, and Marketing, and you’ll help bring structure to ambiguity — especially when new opportunities land mid-flight. You’ll also operate across multiple stakeholders, including other entities within the business and our parent company, to keep product direction aligned and execution predictable.

**Reporting:**Reports to an executive leader (BU leadership).**Dotted line:**Chief Operating Officer.
**Seat:**Leadership table within the AI & Data Science business unit.

Why this role exists

We’re scaling an AI & data science portfolio acrossLife Sciences, Public Health, and Primary Care. We need a product leader who can:

• Turn customer needs intocrisp, testable requirements
• Balance delivery constraints with commercial urgency
• Driveproductization(repeatable offerings) without losing near-term deal velocity
• Own thecommercial thesis: positioning, packaging, pricing, and competitive differentiation
• Improve how we intake and evaluate “big opportunity” requests, so we don’t thrash

Key responsibilities

1) Product vision, strategy, and roadmap (BU-level)

• Own product strategy and roadmap for one or more product lines (e.g., patient identification workflows, smart summary/narrative, smart search, evidence generation, data services).

• Build a roadmap that is:

• grounded in customer value,

• feasible with engineering capacity,

• actionable for Sales and Delivery.

• Define outcomes and success metrics (adoption, cycle time, quality, customer impact).

2) Customer discovery + feedback loops (field + delivery + users)

• Run structured discovery across:

• Sales pursuits (pre-sale requirements, objections, win/loss),

• Clinical Ops delivery learnings (what breaks at implementation),

• End-user feedback (clinicians, analysts, research teams).

• Convert insights into clear choices: what’s productized vs bespoke, what’s in/out of scope, and what we deprecate.

3) Hands-on execution: epics, PRDs, and “engineering-ready” clarity

• Write and maintainPRDs, epics, acceptance criteria, and edge cases.
• Work directly with engineering/R&D to translate epics into implementable stories and ensure requirements are unambiguous.
• Drive a high bar for product artifacts (PRDs, specs, release notes, enablement docs) to reduce churn and rework.
• Partner on release planning and ensure delivery impact is understood early (dependencies, data readiness, operational workflow changes).

4) UI/UX and user workflow design (practical, not theoretical)

• Bring a strong intuition forworkflow + UI/UX: help shape user journeys, reduce friction, and ensure product experiences are intuitive.
• Partner with design/engineering to validate concepts quickly (wireframes, prototypes, usability feedback) and iterate.

5) Commercial strategy ownership (pricing, packaging, competitive)

• Own product packaging and pricing strategy (with Sales/Finance input): value metrics, pricing fences, commercial guardrails.
• Maintain competitive intelligence and differentiation narratives.
• Ensure “deal-to-product” alignment: what we sell can be delivered and scaled.

6) Launches, messaging, and go-to-market enablement (with Marketing + Sales)

• Partner with Marketing and Sales to craft:

• product messaging and positioning,

• launch plans,

• webinars/demos,

• customer-ready materials (decks, one-pagers, FAQs, talk tracks).

• Act as the “single throat to choke” for making sure launches are coordinated and the field is enabled.

7) Cross-functional prioritization + opportunity intake (no thrash)

• Drive prioritization across Sales, Clinical Ops, Engineering, Finance — make tradeoffs explicit.

• Establish a repeatable intake model for “big opportunities”:

• problem statement → business case → feasibility/sizing → resourcing tradeoffs → decision gates

• Keep stakeholders aligned across internal entities and the parent company when priorities intersect.

8) AI-augmented product management (high velocity)

• Use AI tools to accelerate discovery synthesis, requirement drafting, competitive research, and enablement creation.
• Build lightweight systems/templates that compound throughput.

What success looks like (first 90 days)

• A clear portfolio map and narrative:what we sell, to whom, and why we win.
• One prioritized roadmap with explicit tradeoffs tied to capacity.
• A repeatable opportunity intake/gating process (so we handle the next “patient registry-like” request cleanly).
• PRDs + epics for priority initiatives that engineering can execute with minimal ambiguity.
• A v1 packaging/pricing hypothesis for the top offerings with proof points and differentiation.
• At least one coordinated launch/enablement motion (e.g., webinar + collateral + talk tracks) executed end-to-end.

Required qualifications

• 7+ years product management (or equivalent end-to-end product ownership).
• Strong technical fluency: able to engage credibly with engineering/R&D and reason about data pipelines, integrations, constraints, and tradeoffs.
• Demonstrated ability towrite PRDs/epicsand drive execution through engineering delivery.
• Strong commercial acumen: packaging/pricing instincts and customer-facing confidence.
• Excellent written communication: crisp PRDs, decision memos, and field-ready enablement content.

Strongly preferred (healthcare + regulated AI)

• Healthcare domain experience, ideally withEMRs/EHRs, clinical workflows, and healthcare data interoperability.
• Experience launching or operatingAI/ML products in highly regulated / privacy-sensitive environments(health data, security constraints, compliance-driven change control).
• Comfort with privacy/security norms and regulated operating expectations (e.g., PHIPA/HIPAA-like environments).

Working style / operating principles

-**Hands-on operator:**happy to roll up sleeves, write, iterate, and unblock teams.

• High ownership + high velocity with a bias for clarity.
• Comfortable in ambiguity; creates structure and decision gates.
• Collaborative and direct; can drive decisions across multiple stakeholders and entities.