Senior Director, Product Management

Smarsh is seeking a Senior Director of Product Management to lead the analyst experience layer of their Surveillance platform. This role involves owning product strategy and execution while leading a team of product managers, focusing on integrating AI capabilities into a user-friendly product experience.

Responsibilities:

• Define and evolve the platform strategy, roadmap, and capability priorities across analyst experience, workflow infrastructure, and extensibility surfaces
• Translate AI and ML capabilities into a product experience that compliance analysts can use, and regulators can audit
• Own the product extensibility strategy: define the API and integration surfaces that allow customers and partner teams to self-service and embed product capabilities
• Maintain a clear view of the competitive landscape and develop differentiated positioning grounded in product reality
• Own the end-to-end analyst experience: from alert surfacing to resolution, including explainability, reason code workflows, and audit trail completeness
• Drive UX investment as a strategic lever, not a polish pass. The analyst workflow directly affects model quality through the feedback loop. UX decisions have downstream data consequences
• Work closely with Design to move from concept to delivered capability, with direct accountability for closing the gap between the platform’s AI narrative and the actual experience compliance analysts have today
• Define and own the API surface: alert queues, review actions, reporting, reason code taxonomies, and workflow configuration
• Build the product strategy for programmatic extensibility, including integrations with external agents, policymaker prompting surfaces, and developer-facing documentation
• Establish the commercial packaging model for extensibility capabilities: what is a subscription tier, what is usage-based, and what justifies a premium price point
• Partner with Engineering on capacity allocation, release readiness, and operational discipline. You will bring specifications that are complete enough for engineering to build without coming back for clarification
• Partner with the AI/ML Product team on the handoff between AI/ML capabilities and the experience surface that exposes them. Own the feedback routing requirements that make the compounding data flywheel work
• Partner with Professional Services and Customer Experience to translate customer pain into data-backed defensible priorities. Show up where customers are, not just where internal stakeholders are
• Lead a small team of geographically distributed product managers as a player coach. Set standards through your own work, not through delegation
• Develop PM craft on the team: problem framing, evidence-based prioritization, and outcome measurement. This team needs a model to follow, not a manager to report to
• Own the team’s operating cadence, including sprint reviews, prioritization forums, and stakeholder communications

Requirements:

• 10+ years of product management experience, with a track record of shipping product at enterprise scale
• 3+ years of management experience. Player-coach: you have managed PMs while still owning product work directly
• Demonstrated UX product ownership: experience defining analyst or end-user workflows, not just feature requirements. You have shipped products where the experience quality was differentiator
• Demonstrated API product experience: experience defining and owning developer-facing API surfaces or extensibility layers as product decisions. You have written API product specs, not just API documentation
• Experience with enterprise SaaS products in regulated industries. You understand what it means to ship software where wrong answers create legal or regulatory exposure
• Strong written communication. You write product specs that engineering can build from strategy documents that executives can act on. These are different documents and you know the difference
• Experience working in a cross-functional model with Engineering, Design, and Data Science as genuine peers, not as service organizations
• Prior experience in financial services, compliance, legal technology, or another domain where AI explainability and auditability are not optional
• Experience commercializing AI or ML capabilities: translating model performance into product value, and product value into pricing and packaging decisions
• Experience owning products where the feedback loop between end users and AI models was a first-class product concern, not a data science pipeline
• Experience managing products with both a UI surface and a developer-facing surface simultaneously. The two require different decision frameworks, and you have operated in both
• Experience in a PE-backed or exit-horizon company where product decisions are evaluated against near-term financial metrics alongside long-term platform value