Senior Product Manager - AI

This role will own product strategy and outcomes for AI capabilities across symplr’s portfolio working with product leaders, AI Engineering/Data Science, Security and go-to-market stakeholders to define, prioritize and scale AI initiatives. This role focuses on portfolio-level value creation and adoption: identifying the highest-impact AI use cases, shaping roadmaps, ensuring governance and trust while standardizing repeatable patterns (intake, evaluation, monitoring, feedback loops) that enable teams to deliver AI safely in a multi-tenant healthcare SaaS environment. This role will lead AI productization across predictive modeling, optimization, anomaly detection, natural language processing, document intelligence, information retrieval and question answering, recommendation systems and generative AI capabilities. This role will also focus on cross-product enablement (reusable evaluation frameworks, telemetry, guardrails and platform service integration).

Product strategy, vision & roadmap (primary)

• Define the ML/Applied AI product vision and roadmap aligned to enterprise strategy
• Identify and prioritize ML and AI opportunities across product lines; drive ROI framing, sequencing and ‘build vs. buy’ evaluations
• Establish portfolio success metrics (adoption, outcomes, quality, reliability, cost) and ensure roadmap decisions reflect them

Discovery, customer value & use-case validation

• Lead discovery with customers and internal product teams to understand workflows, pain points and constraints of regulated healthcare environments
• Convert opportunities into clearly defined use cases: target users, jobs-to-be-done, workflow touchpoints, expected benefits and measurable outcomes
• Define criteria for when ML/AI is appropriate vs. when deterministic rules provide better value

Requirements, data readiness & delivery alignment

• Produce clear product requirements (PRDs) for ML/AI capabilities: data needs, feature definitions, integration contracts, UX requirements and release criteria
• Drive data readiness gating: availability, quality, labeling needs, lineage and tenant isolation requirements; coordinate with data engineering and product teams to close gaps
• Partner with POs and engineering leads to translate strategy into executable delivery plans, milestones and dependency management

Model quality, evaluation & lifecycle management

• Define success metrics and evaluation strategy: offline evaluation, gold datasets, human-in-the-loop review, thresholding/confidence behavior and regression testing
• Establish post-launch lifecycle plans: monitoring, drift detection signals, retraining/refresh cadence (where applicable) and iterative improvement backlogs
• Ensure consistent release criteria and quality bars across ML/AI solutions (performance, latency, scalability, reliability, and cost)

Trust, safety, governance & compliance (healthcare-grade)

• Partner with Security, Compliance, Legal and Architecture to define product requirements for PHI/PII handling, tenant isolation, retention policies and auditability
• Ensure safe and trustworthy AI practices are embedded in requirements including (as applicable):
• Content filtering and policy enforcement
• Grounding/citation patterns for retrieval-based or generative outputs
• Safe failure modes, fallback behavior and user transparency
• Prompt/data handling constraints and vendor/model risk considerations
• Define audit and traceability expectations: logging, model/prompt/versioning, evaluation artifacts and incident response workflows

Platform enablement & standardization

• Standardize reusable ML/AI delivery patterns across symplr (intake templates, feasibility gates, evaluation harnesses, monitoring dashboards, feedback capture and launch checklists)
• Drive alignment with shared platform capabilities to ensure scalable adoption (tenant configuration, event status propagation, identity/access patterns, feature flagging, observability)
• Enable multi-tenant configurability and consistent policy enforcement across products and environments

• 5+ years of product management experience delivering enterprise SaaS products (platform, data, analytics or workflow products preferred)
• Demonstrated experience delivering ML/AI-enabled product capabilities (predictive models, NLP/document intelligence, recommendations, optimization, anomaly detection and/or generative AI)
• Strong product discovery and requirements skills: customer research, PRDs, roadmap planning, prioritization and stakeholder alignment
• Working knowledge of ML/AI evaluation and lifecycle management: metrics, offline testing, human review loops, monitoring signals and iterative improvement
• Strong cross-functional leadership across engineering, data science and data engineering

Preferred Qualificiations:

• Experience in healthcare or other regulated environments
• Familiarity with LLM product patterns (retrieval-augmented generation, tool use, guardrails) and/or cloud-native ML delivery (AWS preferred)
• Experience with multi-tenant product design and shared service adoption

Knowledge, Skills and Abilities:

• Outcome-driven product thinking and comfort operating in ambiguity
• Excellent written communication (PRDs, customer narratives, exec-ready updates)
• Metrics-first mindset connecting model performance to user outcomes and business impact.
• Pragmatic governance orientation—ships safely without stalling delivery
• Strong judgment on trade-offs: quality vs. speed, cost vs. benefit, generalization vs. customization