Lead Product Manager, Finance & AI

DocuSign DocuSign · Enterprise · San Francisco, CA · IT Infrastructure & Operations

Lead Product Manager for Finance & AI at DocuSign, focusing on AI-enabled products for forecasting, planning, revenue analytics, and controllership. The role involves defining vision, roadmap, and metrics for AI capabilities, partnering with various teams to identify use cases, and delivering AI features from concept to adoption. Key responsibilities include driving adoption through user experience, building prioritization frameworks, and staying current on AI/ML trends.

What you'd actually do

  1. Own the end-to-end vision, strategy, and roadmap for Finance data and AI products that power forecasting, quarter-close, opex management, and GTM performance management for Docusign
  2. Define and evolve the metrics and governance (ACV, ARR, Bookings, Revenue, pipeline, retention, unit economics, core SaaS KPIs) so it is AI-ready, consistent, explainable, and aligned to external reporting, planning, and leadership decision workflows; lead enterprise-wide rollout and adoption
  3. Partner with Corporate Finance, Business Operations, and Product to identify high-impact AI use cases across forecasting, planning, close and consolidation, and performance management and translate these into clear problem statements, data/AI use cases, and measurable success criteria
  4. Work with Data Engineering, Analytics, ML, and Platform teams to design robust, governed data and feature pipelines with high data quality, observability, lineage, SLAs, privacy-by-design, financial controls, and audit readiness
  5. Define, prioritize, and deliver AI capabilities such as forecasting models, propensity and churn predictions, recommendations, anomaly detection, scenario simulations, and natural language assistants for self-serve insights—along with experimentation design, offline evaluation, online A/B testing, and post-launch performance monitoring

Skills

Required

  • Finance and Product Management leadership
  • scaling SaaS operations
  • enterprise data products
  • business transformation
  • cross-functional change management
  • developing concepts and theories by connecting data across business domains
  • delivering ML/AI-powered features from concept through adoption
  • stakeholder management
  • storytelling
  • align Finance and Engineering leaders on outcomes and tradeoffs
  • Bias for action and clarity in ambiguous domains
  • shipping impactful products in matrixed environments
  • business acumen
  • communicating the data story to diverse audiences

Nice to have

  • LLMs
  • predictive modeling
  • retrieval/grounding
  • risk/guardrails

What the JD emphasized

  • AI-enabled capabilities
  • AI use cases
  • AI products
  • AI/ML
  • generative AI
  • LLMs
  • predictive modeling
  • retrieval/grounding
  • risk/guardrails

Other signals

  • AI-enabled capabilities
  • forecasting models
  • propensity and churn predictions
  • recommendations
  • anomaly detection
  • scenario simulations
  • natural language assistants