Sr Product Manager

Hewlett Packard Enterprise; Tlaquepaque; Presencial; MXN 900.000 - 1.300.000; Jornada completa

Descripción de la vacante

Hewlett Packard Enterprise is seeking a Sr Product Manager to define and build an AI-powered seller recommendations platform for internal sales teams. You will own the product vision, strategy, roadmap, and execution across AI/ML, data, CRM, and enterprise software ecosystems.

The role demands strategic thinking, strong analytical skills, and the ability to influence senior stakeholders across a complex organization while delivering measurable impact on seller productivity and customer relevance.

Formación

• 5+ years of experience in Product Management, Management Consulting, Corporate Strategy, Product Strategy, or a related role.
• 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 from problem definition through execution and measurable impact.
• Strong commercial and strategic acumen with the ability to evaluate customer needs and opportunities.

Responsabilidades

• Define product vision and long-term strategy for AI-powered seller recommendations.
• Translate strategy into clear requirements, user stories, and prioritized backlogs.
• Collaborate with data science, engineering, analytics, and CRM teams to ship features.
• Influence senior leaders with data-driven recommendations and compelling narratives.
• Drive adoption and measure impact on seller productivity and revenue.

Educación

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

Descripción del empleo

Sr Product Manager Description - Product Manager – AI-Powered Seller Recommendations Role 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 Responsibilities

Product 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

• 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

Required Qualifications

• 5+ years of experience in Product Management, Management Consulting, Corporate Strategy, Product Strategy, or a related role, 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.

Preferred Qualifications

• MBA or other relevant advanced degree from a leading business, engineering, technology, economics, or quantitative program.
• Experience in management consulting, corporate strategy, investment banking, or a strategic role within a leading technology company.
• 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 product management and strategy or consulting, 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.

Key Product Challenges

Recommendation Relevance

How do we ensure recommendations solve a real customer need rather than simply reflecting what the company wants to sell?

Seller Trust

How do we make recommendations sufficiently transparent and explainable so sellers understand why they were generated and trust them?

Signal Quality

Which customer, product, behavioral, and commercial signals are most predictive of a relevant opportunity?

Prioritization

When dozens of products or solutions could potentially be relevant to a customer, how do we identify the few opportunities most deserving of a seller's attention?

Timing

How do we determine when a customer is most likely to benefit from a particular product, solution, or conversation?

Portfolio Complexity

How do we create coherent, customer‑centric solution recommendations across a large product portfolio rather than generating disconnected product suggestions?

Feedback Loops

How do seller actions, customer responses, product adoption, and commercial outcomes continuously improve future recommendations?

Business Priorities vs. Customer Value

How do we incorporate strategic business priorities while maintaining the relevance, credibility, and objectivity of recommendations?

Measurement

How do we distinguish correlation from incremental business impact and accurately measure whether recommendations are creating additional customer and commercial value?

Candidate Profile

The strongest candidate will think of this product not simply as a cross-sell engine, but as an AI-powered decision-support platform for enterprise sellers. You combine the strategic thinking and structured problem-solving skills typically associated with management consulting with the customer obsession and execution discipline of a strong product leader. You are comfortable starting with an ambiguous business question such as: \"What should our seller recommend to this customer, and why?\" You can break that question into customer problems, business opportunities, data requirements, product capabilities, and measurable outcomes — and then work with engineering and data science teams to turn that strategy into a scalable software product. You are equally comfortable discussing business strategy and commercial impact with senior executives, recommendation models with data scientists, product requirements with engineers, and workflows with frontline sellers. You are highly analytical but understand that data alone does not create a successful product. You actively seek customer and seller feedback, challenge assumptions, test hypotheses, and use evidence to make decisions. Most importantly, you believe that the most successful recommendation product is one that helps sellers have more relevant, informed, and valuable conversations with their customers while creating sustainable business growth.

Job - Software Schedule - Full time Shift - First Shift (Mexico) Travel - Relocation - Equal Opportunity Employer (EEO) - HP, Inc.

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