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People Operations

• Own core People Business Partner responsibilities for an assigned Product client group, managing day-to-day work independently within established policies and practices.
• Provide guidance to managers on performance management, workforce planning, employee relations, coaching, and the rollout of People initiatives.
• Execute key People programs—such as performance cycles, engagement surveys, and talent processes—with consistency, accuracy, and strong follow-through.
• Use data, trends, and stakeholder insights to inform recommendations and support manager decision-making.
• Partner cross-functionally with People Operations, Talent Acquisition, Finance, and other teams to support effective delivery of People programs and initiatives.
• Support organizational and structural changes by helping implement management best practices and change initiatives within the Product organization.
• Make sound decisions within established guidelines and escalate complex, sensitive, or high-risk situations appropriately.

People Business Partner, Product

Product Manager

• Identify opportunities emerging from new technologies, AI, data, platform capabilities, or infrastructure shifts
• Translate weak signals and technical possibilities into clear product hypotheses
• Explore ideas before there is a clear buyer, category, or demand signal
• Maintain an exploration backlog with risks, assumptions, and learning goals
• Frame experiments around the single riskiest assumption
• Define explicit kill criteria before building anything
• Choose the right fidelity: prototype, technical spike, wizard-of-oz, or live pilot
• Build just enough to learn — never more
• Execute scrappy prototypes, MVPs, and pilots with minimal scope
• Work closely with Engineering, Design, Data, and GTM during experiments
• Ruthlessly protect learning speed and avoid premature optimization
• Synthesize results into opinionated recommendations: Scale / Iterate / Kill
• Clearly communicate what was tested, what was learned, and what remains unknown
• Avoid zombie initiatives — every experiment must end with a decision
• Kill your own ideas quickly when evidence is weak
• Maintain a visible Lab portfolio: what's being explored, why, and what the signals say
• Publish decision memos and learning summaries
• Share failed experiments openly when the learning is clear
• Use modern AI tools to speed up research, synthesis, prototyping, and experimentation
• Selectively launch bold initiatives even when short-term adoption is uncertain
• Treat launches as real product bets, not demos
• Be explicit about intent: learning, optionality, or external signaling

Product Manager, Lab

People Business Partner – Product

• Identify opportunities emerging from new technologies, AI, data, platform capabilities, or infrastructure shifts
• Translate weak signals and technical possibilities into clear product hypotheses
• Explore ideas before there is a clear buyer, category, or demand signal
• Maintain an exploration backlog with risks, assumptions, and learning goals
• Frame experiments around the single riskiest assumption
• Define explicit kill criteria before building anything
• Choose the right fidelity: prototype, technical spike, wizard-of-oz, or live pilot
• Execute scrappy prototypes, MVPs, and pilots with minimal scope
• Work closely with Engineering, Design, Data, and GTM during experiments
• Ruthlessly protect learning speed and avoid premature optimization
• Synthesize results into opinionated recommendations: Scale / Iterate / Kill
• Clearly communicate what was tested, what was learned, and what remains unknown
• Avoid zombie initiatives — every experiment must end with a decision
• Kill your own ideas quickly when evidence is weak
• Prepare Clean Escalation & Handoffs with validated hypotheses and success metrics
• Maintain a visible Lab portfolio: what's being explored, why, and what the signals say
• Use modern AI tools to speed up research, synthesis, prototyping, and experimentation
• Selectively launch bold initiatives even when short-term adoption is uncertain
• Treat launches as real product bets, not demos
• Use launches to test future categories, shape market perception, and signal technical leadership

• Identify opportunities emerging from new technologies, AI, data, platform capabilities, or infrastructure shifts
• Translate weak signals and technical possibilities into clear product hypotheses
• Explore ideas before there is a clear buyer, category, or demand signal
• Maintain an exploration backlog with risks, assumptions, and learning goals
• Frame experiments around the single riskiest assumption
• Define explicit kill criteria before building anything
• Choose the right fidelity: prototype, technical spike, wizard-of-oz, or live pilot
• Build just enough to learn — never more
• Execute scrappy prototypes, MVPs, and pilots with minimal scope
• Work closely with Engineering, Design, Data, and GTM during experiments
• Ruthlessly protect learning speed and avoid premature optimization
• Synthesize results into opinionated recommendations: Scale / Iterate / Kill
• Clearly communicate what was tested, what was learned, and what remains unknown
• Avoid zombie initiatives — every experiment must end with a decision
• Kill your own ideas quickly when evidence is weak
• Maintain a visible Lab portfolio: what's being explored, why, and what the signals say
• Publish decision memos and learning summaries
• Share failed experiments openly when the learning is clear
• Use modern AI tools to speed up research, synthesis, prototyping, and experimentation
• Explore AI-enabled product ideas with a realistic lens: cost, latency, data, accuracy
• Distinguish hype from actual capability shifts
• Help others understand when AI meaningfully accelerates learning — and when it doesn't
• Selectively launch bold initiatives even when short-term adoption is uncertain
• Treat launches as real product bets, not demos
• Use launches to test future categories, shape market perception, and signal technical leadership
• Be explicit about intent: learning, optionality, or external signaling

• Identify opportunities emerging from new technologies, AI, data, platform capabilities, or infrastructure shifts
• Translate weak signals and technical possibilities into clear product hypotheses
• Explore ideas before there is a clear buyer, category, or demand signal
• Maintain an exploration backlog with risks, assumptions, and learning goals
• Frame experiments around the single riskiest assumption
• Define explicit kill criteria before building anything
• Choose the right fidelity: prototype, technical spike, wizard-of-oz, or live pilot
• Build just enough to learn — never more
• Execute scrappy prototypes, MVPs, and pilots with minimal scope
• Work closely with Engineering, Design, Data, and GTM during experiments
• Ruthlessly protect learning speed and avoid premature optimization
• Synthesize results into opinionated recommendations: Scale / Iterate / Kill
• Clearly communicate what was tested, what was learned, and what remains unknown
• Avoid zombie initiatives — every experiment must end with a decision
• Prepare Clean Escalation & Handoffs
• Maintain a visible Lab portfolio: what's being explored, why, and what the signals say
• Publish decision memos and learning summaries
• Share failed experiments openly when the learning is clear
• Use modern AI tools to speed up research, synthesis, prototyping, and experimentation
• Selectively launch bold initiatives even when short-term adoption is uncertain
• Treat launches as real product bets, not demos
• Be explicit about intent: learning, optionality, or external signaling

Product Manager – Lab

• Explore High-Risk, Technology-Driven Opportunities
• Identify opportunities emerging from new technologies, AI, data, platform capabilities, or infrastructure shifts
• Translate weak signals and technical possibilities into clear product hypotheses
• Explore ideas before there is a clear buyer, category, or demand signal
• Maintain an exploration backlog with risks, assumptions, and learning goals
• Design Experiments & Define Kill Criteria
• Frame experiments around the single riskiest assumption
• Define explicit kill criteria before building anything
• Choose the right fidelity: prototype, technical spike, wizard-of-oz, or live pilot
• Build just enough to learn — never more
• Run Fast Experiments & Pilots
• Execute scrappy prototypes, MVPs, and pilots with minimal scope
• Work closely with Engineering, Design, Data, and GTM during experiments
• Ruthlessly protect learning speed and avoid premature optimization
• Make Clear Investment Decisions
• Synthesize results into opinionated recommendations: Scale / Iterate / Kill
• Clearly communicate what was tested, what was learned, and what remains unknown
• Avoid zombie initiatives — every experiment must end with a decision
• Kill your own ideas quickly when evidence is weak
• Prepare Clean Escalation & Handoffs
• When an opportunity shows strong signal, prepare it for handoff with validated value and problem hypotheses
• Evidence from experiments or pilots
• Clear risks, assumptions, and success metrics
• A proposed ownership and scaling model
• Transfer ownership fully — the Lab does not run scaled products.
• Operate Transparently & Share Learnings
• Maintain a visible Lab portfolio: what's being explored, why, and what the signals say
• Publish decision memos and learning summaries
• Share failed experiments openly when the learning is clear
• Leverage AI to Accelerate Learning
• Use modern AI tools to speed up research, synthesis, prototyping, and experimentation
• Explore AI-enabled product ideas with a realistic lens: cost, latency, data, accuracy
• Distinguish hype from actual capability shifts
• Help others understand when AI meaningfully accelerates learning — and when it doesn't
• Launch High-Risk, High-Signal Initiatives
• Selectively launch bold initiatives even when short-term adoption is uncertain
• Treat launches as real product bets, not demos
• Use launches to test future categories, shape market perception, and signal technical leadership
• Be explicit about intent: learning, optionality, or external signaling

• Identify opportunities emerging from new technologies, AI, data, platform capabilities, or infrastructure shifts
• Translate weak signals and technical possibilities into clear product hypotheses
• Explore ideas before there is a clear buyer, category, or demand signal
• Maintain an exploration backlog with risks, assumptions, and learning goals
• Frame experiments around the single riskiest assumption
• Define explicit kill criteria before building anything
• Choose the right fidelity: prototype, technical spike, wizard-of-oz, or live pilot
• Build just enough to learn — never more
• Execute scrappy prototypes, MVPs, and pilots with minimal scope
• Work closely with Engineering, Design, Data, and GTM during experiments
• Ruthlessly protect learning speed and avoid premature optimization
• Synthesize results into opinionated recommendations: Scale / Iterate / Kill
• Clearly communicate what was tested, what was learned, and what remains unknown
• Transfer ownership fully — the Lab does not run scaled products

Marketing

• Build and execute 1:1, 1:few, and early-stage 1:many ABM programs across a focused list of Strategic accounts.
• Orchestrate integrated account-based plays across LinkedIn, direct mail, paid and organic social, content hubs, email, website personalization, and field events.
• Work with RevOps to operationalize intent data, account scoring, and engagement signals to support outbound and expansion efforts.
• Create clear, personalized messaging, value narratives, and microsites for executive buyers including CMOs, CDOs, CTOs, CFOs, and Compliance leaders.
• Partner with Field Marketing to deliver high-touch executive experiences, including curated dinners, virtual events, and DXS.
• Co-build account plans with AEs to identify whitespace, engage buying groups, and move deals forward.
• Support with account research, insights, messaging, and custom assets that help secure meetings and influence opportunities.
• Track and report on account coverage, engagement, intent and pipeline impact.
• Build dashboards that help Marketing and Sales understand what’s working and where to focus next.
• Hold yourself accountable for pipeline creation, acceleration, and revenue impact.
• Test new ABM approaches, executive engagement formats, and creative ideas to stand out in the market.
• Continuously improve personalization, execution quality, and cross-team coordination.

People Business Partner, Product

Product Manager, Lab

People Business Partner, Product

People Business Partner – Product

Product Manager, Lab

Product Manager, Lab

Product Manager – Lab

Product Manager, Lab

Product Manager – Lab

People Business Partner, Product

People Business Partner, Product

People Business Partner, Product

People Business Partner, Product

People Business Partner, Product

Senior Account-Based Marketing Manager