Lead Product Manager - AI

About the role

The Lead Product Manager will own the roadmap and strategy for insights-driven and AI-enabled capabilities, including decision intelligence and Next Best Action solutions, across multiple lines of business. This role is well-suited for a data-fluent, strategy-oriented product leader who thrives in fast-moving environments and brings hands-on experience building AI-powered products.

Responsibilities

• Own and drive execution of AI-enabled and insights-driven features, including decision intelligence and Next Best Action capabilities, with accountability for adoption and measurable business outcomes.
• Partner with channel Product leaders to apply AI and insights consistently across products and lines of business.
• Identify, validate, and prioritize high-impact AI and insights use cases through stakeholder and customer engagement, including GenAI and agentic workflow opportunities.
• Collaborate with Data Product and Data Science teams to translate models, analytics, and insights into usable product features; ensure outputs are explainable, auditable, and aligned with responsible AI standards.
• Work closely with Integrations and Engineering to surface AI and insights via scalable, API-driven approaches across internal and external platforms.
• Define features, epics, and stories with clear acceptance criteria; manage priorities in a fast-paced delivery environment.
• Define success metrics tied to advisor workflow impact and business outcomes; use data and feedback to drive continuous improvement, adoption, and demonstrated ROI.
• Communicate product goals, progress, outcomes, and tradeoffs to Product leadership and partners.
• Ensure AI and insights features solve real advisor, enterprise, and asset manager workflow needs.
• Adhere to and apply Envestnet's legal, compliance, risk, business continuity, and administrative policies within the role and department(s).

Requirements

• Bachelor’s degree in Business, Computer Science, Engineering, Data Science, or a related field; equivalent practical experience considered.
• 8-10 years of experience in product management, with ownership of complex, data-driven products or features; experience shipping AI-enabled or analytics products strongly preferred.
• Strong experience working in agile product development environments.
• Demonstrated ability to partner closely with Engineering, Data, and Analytics teams on technically complex initiatives.
• High data fluency, with experience translating analytics, insights, or models into customer-facing product capabilities; working familiarity with GenAI, LLM-based features, or agentic workflows is a strong plus.
• Proven ability to manage competing priorities and operate effectively in fast-changing, ambiguous environments.
• Excellent written and verbal communication skills, with the ability to articulate product value and technical concepts clearly.

Qualifications

• Bachelor’s degree in Business, Computer Science, Engineering, Data Science, or a related field; equivalent practical experience considered.
• 8-10 years of experience in product management, with ownership of complex, data-driven products or features; experience shipping AI-enabled or analytics products strongly preferred.
• Strong experience working in agile product development environments.
• Demonstrated ability to partner closely with Engineering, Data, and Analytics teams on technically complex initiatives.
• High data fluency, with experience translating analytics, insights, or models into customer-facing product capabilities; working familiarity with GenAI, LLM-based features, or agentic workflows is a strong plus.
• Proven ability to manage competing priorities and operate effectively in fast-changing, ambiguous environments.
• Excellent written and verbal communication skills, with the ability to articulate product value and technical concepts clearly.

Skills

• Data fluency
• Agile methodologies
• Collaboration with engineering, data, and analytics teams
• Translation of analytics and insights into product features
• GenAI and agentic workflow understanding
• Product discovery and prioritization
• Feature definition and management
• Metrics and iteration
• Stakeholder communication
• Customer focus
• Risk and compliance awareness

Benefits

• Competitive compensation
• Performance-linked incentives
• 401(k) company match
• Paid parental leave
• Education reimbursement
• Disability coverage
• Mental health & wellness support

Pay

TBD

Schedule

Hybrid work model with primary work location in Berwyn