Sr Product Manager

. This product will bring together customer signals, product usage, purchase history, account characteristics, seller activity, market insights, and business priorities to generate actionable recommendations for sellers. The product should help sellers answer questions such as:

• What should I recommend to this customer next?

• Which products or solutions are most relevant to this customer's needs?

• Which customer signals indicate a potential opportunity?

• Why is a particular product being recommended?

• What is the best next action for this account?

• Which opportunities should I prioritize across my customer portfolio?

The Product Manager will own the

• Define the long-term vision and product strategy for an intelligent

for internal sellers.

• Develop a deep understanding of seller, customer, and business needs and translate them into scalable product opportunities.

• Define the core problems the product will solve and establish clear product principles and priorities.

• Build and maintain a multi-year product roadmap balancing customer value, seller productivity, commercial impact, technical feasibility, and company strategy.

• Identify opportunities to evolve the solution from rules-based recommendations toward predictive, personalized, and AI-powered recommendations.

• Define how the product fits within the broader sales, CRM, customer intelligence, and AI ecosystem.

• Evaluate strategic opportunities and trade-offs using structured business cases, quantitative analysis, customer insights, and competitive benchmarks.

• Develop a deep understanding of seller workflows through interviews, field observation, telemetry, experimentation, and qualitative research.

• Design experiences that surface the

.

• Ensure recommendations are relevant, actionable, explainable, and easy for sellers to incorporate into customer conversations.

• Define how recommendations should be presented, prioritized, grouped, and integrated into existing seller workflows.

• Create mechanisms that allow sellers to understand the signals and rationale behind each recommendation.

• Design seller feedback mechanisms that capture whether recommendations are accepted, rejected, dismissed, acted upon, or considered irrelevant.

• Reduce seller effort by integrating recommendations directly into CRM, account planning, opportunity management, and seller productivity tools.

• Ensure the product ultimately optimizes for

• Partner closely with data science, machine learning, engineering, and analytics teams to define the recommendation architecture and product requirements.

• Identify and prioritize the customer and commercial signals required to generate high-quality recommendations.

• Define requirements for recommendation eligibility, scoring, ranking, personalization, confidence, explainability, and suppression.

• Determine how signals such as product usage, installed base, purchase history, customer profile, industry, intent, engagement, support activity, contract information, account potential, and seller activity should influence recommendations.

• Partner with technical teams to develop and improve propensity models, ranking models, customer segmentation, recommendation algorithms, and feedback loops.

• Define how seller actions and customer outcomes can continuously improve recommendation quality.

• Evaluate opportunities to use generative AI to provide recommendation explanations, customer insights, conversation preparation, opportunity summaries, and suggested next actions.

• Ensure responsible use of AI, including appropriate transparency, explainability, governance, and data quality controls.

• Translate product strategy into clear product requirements, customer scenarios, user stories, success criteria, and prioritized backlogs.

• Partner closely with engineering, design, data science, analytics, and architecture teams throughout discovery, development, launch, and iteration.

• Make prioritization decisions based on customer impact, seller value, commercial opportunity, technical complexity, model performance, and investment requirements.

• Define minimum viable products and phased rollout strategies for complex recommendation capabilities.

• Lead pilots, experiments, A/B tests, and controlled launches to validate hypotheses and improve the product.

• Use quantitative and qualitative data to identify issues, uncover opportunities, and drive continuous product improvement.

• Manage dependencies across CRM systems, customer data platforms, product catalogues, identity systems, analytics platforms, sales tools, and downstream business processes.

• Drive product adoption and operational readiness in partnership with sales operations, enablement, and go-to-market teams.

• Develop clear business cases for product investments, including potential revenue impact, seller productivity improvements, customer benefits, and implementation costs.

• Partner with Sales, Finance, Strategy, and business-unit leaders to understand portfolio priorities and commercial opportunities.

• Define approaches for balancing customer relevance with strategic product priorities and company growth objectives.

• Identify opportunities to improve cross-sell, upsell, product penetration, customer retention, and seller productivity.

• Establish measurable links between recommendation quality, seller behavior, customer outcomes, pipeline creation, and revenue.

• Analyze seller and customer segments to identify where recommendation capabilities can generate the greatest value.

• Translate complex commercial problems into scalable product capabilities rather than one-off processes or manual interventions.

• Work closely with Sales, Sales Operations, Engineering, Data Science, UX, Product Marketing, Finance, Customer Success, Strategy, and product business units.

• Build alignment across stakeholders who may have different objectives regarding product prioritization and seller engagement.

• Influence senior leaders through data, structured problem solving, clear recommendations, and compelling product narratives.

• Facilitate strategic discussions and drive decisions in ambiguous, cross-functional environments.

• Communicate product strategy, roadmap, trade-offs, risks, and business results clearly to executive stakeholders.

• Serve as the voice of the seller and customer while balancing broader company priorities.

• Build strong relationships across global and matrixed organizations to accelerate execution and adoption.

. Key metrics may include:

• Seller adoption and monthly active usage

• Recommendation engagement rate

• Recommendation acceptance rate

• Percentage of recommendations acted upon

• Recommendation precision, relevance, and quality

• Seller satisfaction and trust

• Reduction in time required for account planning and opportunity discovery

• Increase in qualified opportunities identified

• Incremental pipeline generated or influenced

• Conversion rate of recommended opportunities

• Incremental revenue influenced

• Increase in relevant cross-sell and upsell opportunities

• Growth in customer product adoption and solution penetration

• Improvement in seller productivity

• Effectiveness of seller feedback loops

• Model and recommendation performance over time

• Customer outcomes associated with recommended solutions

• 5+ years of experience in

, preferably within technology, enterprise software, SaaS, AI/ML, data products, CRM, sales technology, or digital platforms.

• Bachelor's degree in Business, Engineering, Computer Science, Economics, Finance, Mathematics, or a related discipline, or equivalent practical experience.

• Demonstrated experience defining product or business strategy and translating ambiguous problems into clear, actionable plans.

• Proven track record of taking complex products, capabilities, or strategic initiatives from problem definition through execution and measurable impact.

• Strong commercial and strategic acumen, with the ability to evaluate customer needs, business opportunities, investment priorities, and trade-offs.

• Experience developing business cases, defining KPIs, analyzing financial or operational performance, and using quantitative analysis to support decisions.

• Strong analytical and structured problem-solving capabilities.

• Ability to synthesize large amounts of qualitative and quantitative information into clear insights and recommendations.

• Experience working closely with engineering, data science, analytics, UX, and business teams.

• Strong understanding of data-driven product development, experimentation, and performance measurement.

• Ability to communicate complex technical and business concepts clearly to both technical and non-technical audiences.

• Strong executive communication and storytelling skills.

• Demonstrated ability to influence senior stakeholders and drive alignment across large, cross-functional organizations.

• Ability to operate effectively in ambiguous, fast-changing, and highly matrixed environments.

from a leading business, engineering, technology, economics, or quantitative program.

• Experience in

.

• Experience at a top-tier management consulting firm or in an internal strategy organization, particularly working on growth, digital transformation, sales effectiveness, customer strategy, or technology-related engagements.

• Experience combining

, with the ability to move from strategic problem definition into product execution.

• Experience developing go-to-market, portfolio, segmentation, growth, cross-sell, upsell, or sales productivity strategies.

• Experience building next-best-action, next-best-product, recommendation, personalization, sales intelligence, or AI-assisted decision-support products .

• Experience working with B2B enterprise sales organizations.

• Strong understanding of enterprise sales motions, account planning, opportunity management, solution selling, and customer lifecycle management.

• Experience with CRM and sales platforms such as Salesforce, Microsoft Dynamics 365, or equivalent enterprise ecosystems.

• Familiarity with recommendation systems, machine learning ranking models, propensity models, customer segmentation, predictive analytics, or personalization.

• Experience working with large-scale customer, product, behavioral, or commercial datasets.

• Experience developing AI-powered products where user trust, explainability, and responsible AI are important.

• Familiarity with cloud, SaaS, enterprise software, or complex technology product portfolios.

• Experience driving products or strategic initiatives across global, highly matrixed organizations.