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Over 500K customers and millions of people use our platform to accelerate the process of doing business and simplify people’s lives. With intelligent agreement management, our platform unleashes business-critical data that is trapped inside of documents. Until now, these were disconnected from business systems of record, costing businesses time, money, and opportunity. Using our platform's Intelligent Agreement Management platform, companies can create, commit, and manage agreements.
What you'll do
As a P5 Lead Product Manager Analyst AI for Marketing and Prospecting, you own Agent Use Case Definition & Business Outcomes by identifying high-impact business workflows.
You define technically viable agent requirements and translate business problems into clearly defined agent capabilities feasible within the enterprise tech stack. For Applications Workflow Design & Orchestration, you define the end-to-end workflow an agent will perform across enterprise systems, specifying triggers, context, actions, and escalation paths.
You handle Collaboration, Value measurement, and Continuous improvement by defining success metrics, tracking outcomes, and refining agent behaviors based on operational feedback.
Finally, you manage Autonomous Boundaries and Safety by determining the extent of agent autonomy, defining guardrails, approval checkpoints, and the human-in-the-loop model.
This position is an individual contributor role reporting to the Director, Martech & Prospecting Applications.
Responsibility
Identify the highest-value problems for business development, sales and account executives where an agent can save time, reduce effort, and take useful action
Be very savvy with data, crunch the current state funnel metrics, and pipeline performances to determine the highest value use cases to agentify
Influence senior leaders and cross-functional teams on use cases we should automate, what to build first, and how to stay at the forefront of this space
Understand intent signals, prospecting signals, propensity scores, next best recommendations etc to determine the best execution strategies for prospecting
Evaluate rapidly new capabilities from model providers, tooling ecosystems, and internal platforms, and translate them into clear product bets
Build scrappy but convincing prototypes using available models, agent frameworks, tools, and lightweight interfaces to test usefulness, trust, and feasibility
Partner with engineering, ML, design, and platform teams to turn promising prototypes into scalable product experiences
Shape how memory, context, orchestration, permissions, and human handoff should work in prospecting journeys
Define safe patterns for agents that take action, including clear explanations, user approvals, visibility into what the agent is doing, and recovery paths when things go wrong
Establish success metrics, launch criteria, and learning loops for these experiences, balancing ambition with trust, reliability, latency, and cost
Job Designation
Hybrid: Employee divides their time between in-office and remote work. Access to an office location is required. (Frequency: Minimum 2 days per week; may vary by team but will be weekly in-office expectation)
What you bring
Basic
6+ years of product management experience, including meaningful work on AI/ML products, automation systems, developer tools, platforms, or other technically sophisticated products.
Hands-on builder mindset, with the ability to use model APIs, agent tooling, and simple code in Python or TypeScript/JavaScript to create or guide working prototypes
Proven track record of taking new product areas from ambiguous opportunity to strategy, prototype, launch, and measurable impact
Preferred
Experience building assistants, agents, copilots, or AI products that use tools to complete tasks
Familiarity with memory, tool calling, browser or computer use, voice, multimodal interaction, or agent orchestration
Background in software engineering, ML, data science, or a similarly technical field
Strong point of view on when an agent should act autonomously, when it should ask for permission, and how to keep users in controlStrong technical fluency and comfort working at the boundary of product and engineering and be able to reason about APIs, orchestration, data flows, permissions, latency, and failure modes
Strong judgment in trust-sensitive experiences, including transparency, control, privacy, and reliability
Excellent written and verbal communication skills, with the ability to create clarity and alignment across senior cross-functional stakeholders
Strong Data Analysis, Data Science and engineering skills