Senior Product Manager - AI

symplr

2d•$120,000 - $140,000

About The Position

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).

Requirements

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
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

Nice To Haves

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

Responsibilities

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