Senior Director, Product Management

Smarsh•Atlanta, GA

14h

About The Position

We are seeking a Senior Director, Product Management, to lead the analyst experience layer of our Surveillance platform. This is a player-coach role. You will own product strategy and drive execution directly while leading a small team of product managers. You are not here to manage up and delegate down. You are here to set the standard and do the work alongside the team. The platform has two defining challenges: closing the gap between its AI capabilities and the analyst experience that surfaces them and defining the API and extensibility surfaces that let sophisticated customers build on top of the platform rather than around it. Both require a product leader who is equally fluent in UX thinking and developer-facing product design. You will own the product strategy, roadmap, and execution partnering directly with Engineering, AI/ML, Data Science, Professional Services, and the customer-facing organizations to define what the product is, who it serves, and how it wins.

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.

Nice To Haves

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.

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 policy makers and analyst teams to extend product capabilities without requiring Smarsh engineering intervention.
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 analysts have today.
Define and own the API surface: alert queues, review actions, reporting, reason code taxonomies, and workflow configuration. These are product decisions, not engineering ones.
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 defensible priorities. Show up where customers are, not just where internal stakeholders are.
Lead a small team of 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.