Sr Product Manager

Sr Product Manager
Description -
Product Manager – AI-Powered Seller RecommendationsRole Overview
We are looking for a strategic, customer-obsessed, and data-driven Product Manager to define and build an intelligent recommendation product that helps our internal sales teams identify which products, solutions, and offers are most relevant for each customer.
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 product vision, strategy, roadmap, business case, and execution for this capability, working at the intersection of AI/ML, data, CRM, sales enablement, enterprise software, and commercial strategy.
This role requires a unique combination of product management, structured problem solving, commercial acumen, analytical thinking, and the ability to influence senior stakeholders across a complex organization.
The ideal candidate will be comfortable moving between strategic questions, such as defining the future of AI-powered selling, and detailed product decisions around recommendation logic, ranking, explainability, seller experience, experimentation, and measurement.
Key ResponsibilitiesProduct Vision & Strategy
Define the long-term vision and product strategy for an intelligent next-best-product and next-best-action recommendation platform 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.
Seller & Customer Experience
Develop a deep understanding of seller workflows through interviews, field observation, telemetry, experimentation, and qualitative research.
Design experiences that surface the right recommendation, to the right seller, for the right customer, at the right time.
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 customer relevance and customer value, not simply product promotion.
AI, Data & Recommendation Systems
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.
Product Execution
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.
Commercial Strategy & Business Impact
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.
Cross-Functional Leadership
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.
Success Measures
Success in this role will be measured through a combination of seller adoption, recommendation quality, customer outcomes, operational efficiency, and commercial impact.
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 fe