Director - Product Insights & Platform Intelligence- ADC

JOB DESCRIPTION:

Role Description:

The Director, Product Management — Product Insights & Platform Intelligence will define and lead the cross-product vision, strategy, roadmap, and lifecycle management for data-driven, analytics-enabled, and AI-enabled insights across Abbott Diabetes Care’s digital ecosystem.

This role will establish a coherent product insight strategy spanning the Libre app, LibreView, and EHR-connected workflows. The Director will identify high-value opportunities to transform authorized glucose, device, and contextual data into clear, trustworthy, evidence-based insights for people using Libre, healthcare professionals, care teams, and health-system partners.

The role owns the shared insight layer—including insight strategy, portfolio prioritization, product requirements, data/model capability needs, evaluation criteria, shared platform capabilities, and value realization. This role works in close partnership with the Directors of Consumer Product and HCP Product, who lead their respective product experiences, workflows, user interfaces, content, and channel roadmaps. This collaborative structure promotes strong alignment and a cohesive experience across channels. This role partners with them to ensure insights are consistent, reusable, evidence-backed, and appropriately adapted for each audience.

The Director will also lead product strategy for evaluating, building, licensing, and integrating third-party data, analytics, AI, and machine-learning capabilities. The role is accountable for strategic and product fit, while technical, clinical, quality, regulatory, privacy, security, legal, procurement, and supplier-approval decisions remain subject to applicable functional ownership and governance.

Key Responsibilities

1. Platform intelligence product vision and portfolio strategy

• Define and communicate the multi-year vision, product strategy, and roadmap for platform intelligence, analytics, and AI-enabled insights across the Libre digital ecosystem.
• Establish a unified strategy for reusable insight capabilities supporting:
  • Consumer-facing experiences in the Libre app and LibreLinkUp
  • HCP and care-team workflows in LibreView
  • Health-system, interoperability, and EHR-connected workflows
• Ensure the product insight portfolio supports a cohesive ecosystem experience and avoids duplicative, conflicting, or inconsistent analytics across products.

2. Shared platform insight platform and product requirements

• Define product requirements for shared platform insight-generation capabilities, including:
  • Intended users, needs, and expected decisions or actions
  • Data inputs, provenance, quality, recency, and minimum data-sufficiency thresholds
  • Insight logic, output format, explanation requirements, and confidence signaling
  • User controls, permissions, consent, and data-sharing considerations
  • Interoperability, API, reliability, scalability, latency, and availability requirements
  • Monitoring, auditability, and lifecycle-management expectations
• Establish common definitions, taxonomy, and governance for concepts such as insights, patterns, baselines, trend changes, data quality, confidence, and outcome metrics.
• Partner with Software Engineering, Data Science, Data Engineering, Architecture, and UX to develop scalable and reusable platform capabilities rather than channel-specific point solutions.
• Ensure insights include appropriate supporting evidence so users and clinicians can understand why an insight was surfaced and when it should not be relied upon due to insufficient data.

3. Partnership with Consumer, HCP, and EHR product leaders

4. AI, external data, and technology capability strategy

• Define the product strategy for internal and external data, analytics, AI, machine-learning, and platform capabilities required to deliver the clinical intelligence roadmap.
• Translate business, customer, clinical, and workflow needs into clear requirements for internally developed, licensed, partnered, or reused capabilities.
• Lead product-level build, buy, partner, or reuse assessments, considering:
• Co-lead market scans and vendor evaluations with Data Science, Engineering, Enterprise Architecture, Procurement, Finance, Privacy, Security, Legal, Clinical, Quality, Regulatory, and Medical Affairs, as appropriate.
• Define product acceptance criteria and launch-readiness requirements for third-party and internally developed models, datasets, analytics services, and AI platforms.
• Develop recommendations to invest in, scale, improve, replace, pause, or retire data and AI capabilities based on evidence, product performance, cost, customer value, and strategic priorities.

5. Evaluation, evidence, and model lifecycle management

• Establish a proportionate evaluation strategy for analytics- and AI-enabled capabilities from discovery through launch and post-launch monitoring.
• Partner with Data Science and Engineering to assess technical performance, including, as applicable:
  • Accuracy, sensitivity, specificity, precision, recall, and calibration
  • Robustness to incomplete, missing, or variable-quality data
  • Reliability, latency, availability, scalability, and cost
  • Model drift and operational performance over time
• Lead product and workflow evaluation to determine whether insights are understandable, trusted, adopted, and valuable in real-world use.
• Ensure post-launch monitoring addresses customer feedback, workflow impact, alert burden, data quality, equity, safety signals, model performance, and value realization.
• Use evaluation outcomes to recommend whether to launch, limit, revise, monitor, pause, retire, or scale an insight or underlying capability.

6. Cross-functional governance and compliant execution

• Lead cross-functional alignment across Product, R&D, Software Engineering, Data Science, UX Research and Design, Clinical Affairs, Medical Affairs, Quality, Regulatory Affairs, Privacy, Cybersecurity, Legal, Commercial, Market Access, Finance, and Procurement.
• Ensure product concepts and roadmaps are designed to support responsible AI practices, including data minimization, transparency, appropriate human oversight, performance monitoring, fairness assessment, and safe behavior when data are inadequate.

7. Measurement, operating model, and team leadership

• Define common KPIs for insight quality, customer value, workflow impact, and business performance across the digital ecosystem.
• Recruit, develop, and lead a high-performing product-management team, as applicable.
• Communicate strategy, progress, tradeoffs, risks, and recommendations clearly to senior leaders and cross-functional stakeholders.

Required Qualifications

• Demonstrated experience delivering data-driven, analytics, algorithm-enabled, AI/ML-enabled, or platform products.
• Experience defining product acceptance criteria, success metrics, and evaluation plans for algorithmic, analytics, or AI-enabled products.
• Strong executive communication, stakeholder management, influence, prioritization, and organizational leadership skills.

Preferred Qualifications

• Experience conducting build-versus-buy analyses, vendor assessments, data-platform selection, or third-party AI/model evaluations.
• Working knowledge of AI/ML concepts such as model validation, calibration, explainability, robustness, bias/fairness, drift monitoring, and human-in-the-loop design.

Leadership Competencies

The successful candidate will demonstrate:

• Ecosystem thinking: Connects people using Libre, HCPs, health systems, products, data, and workflows into a cohesive strategy.
• Platform product leadership: Builds reusable capabilities while enabling channel teams to own their customer experiences.
• Pragmatic AI judgment: Separates technically interesting AI from AI that is useful, trustworthy, explainable, evidence-supported, and scalable.
• Clinical and customer empathy: Understands both the lived experience of diabetes management and the operational realities of clinical care teams.
• Risk-aware innovation: Advances meaningful innovation while respecting clinical, quality, regulatory, privacy, security, and responsible-AI obligations.
• Influence without authority: Aligns diverse stakeholders and manages tradeoffs constructively.
• Operational discipline: Translates strategy into measurable outcomes, governed decisions, and reliable delivery.

The base pay for this position is

In specific locations, the pay range may vary from the range posted.

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From the posting on Abbott's job site. Read the original before you apply.