{"id":2786496,"url":"https://alion.io/job/hp-sr-product-manager","title":"Sr Product Manager","company":{"id":2088298,"name":"HP","domain":"hewlett-packard.de","url":"https://alion.io/company/hewlett-packard-de","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":null},"role":"Product","role_family":"Product","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Tlaquepaque, Mexico","Sant Cugat del Vallès, Spain"],"countries":["MX","ES"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":57000,"max_usd":127000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":3904},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Machine Learning","optional":false},{"name":"Recommender Systems","optional":false}],"status":"live","first_seen_at":"2026-10-16T00:00:00Z","employer_posted_date":"2026-10-16","last_verified_at":"2026-10-17T19:29:56Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"Sr Product ManagerDescription -\nProduct Manager - AI-Powered Seller Recommendations\nRole Overview\nWe 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.\nThis 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.\nThe product should help sellers answer questions such as:\nWhat should I recommend to this customer next?\n\nWhich products or solutions are most relevant to this customer's needs?\n\nWhich customer signals indicate a potential opportunity?\n\nWhy is a particular product being recommended?\n\nWhat is the best next action for this account?\n\nWhich opportunities should I prioritize across my customer portfolio?\n\nThe 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.\nThis 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.\nThe 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.\nKey Responsibilities\nProduct Vision & Strategy\nDefine the long-term vision and product strategy for an intelligent next-best-product and next-best-action recommendation platform for internal sellers.\n\nDevelop a deep understanding of seller, customer, and business needs and translate them into scalable product opportunities.\n\nDefine the core problems the product will solve and establish clear product principles and priorities.\n\nBuild and maintain a multi-year product roadmap balancing customer value, seller productivity, commercial impact, technical feasibility, and company strategy.\n\nIdentify opportunities to evolve the solution from rules-based recommendations toward predictive, personalized, and AI-powered recommendations.\n\nDefine how the product fits within the broader sales, CRM, customer intelligence, and AI ecosystem.\n\nEvaluate strategic opportunities and trade-offs using structured business cases, quantitative analysis, customer insights, and competitive benchmarks.\n\nSeller & Customer Experience\nDevelop a deep understanding of seller workflows through interviews, field observation, telemetry, experimentation, and qualitative research.\n\nDesign experiences that surface the right recommendation, to the right seller, for the right customer, at the right time.\n\nEnsure recommendations are relevant, actionable, explainable, and easy for sellers to incorporate into customer conversations.\n\nDefine how recommendations should be presented, prioritized, grouped, and integrated into existing seller workflows.\n\nCreate mechanisms that allow sellers to understand the signals and rationale behind each recommendation.\n\nDesign seller feedback mechanisms that capture whether recommendations are accepted, rejected, dismissed, acted upon, or considered irrelevant.\n\nReduce seller effort by integrating recommendations directly into CRM, account planning, opportunity management, and seller productivity tools.\n\nEnsure the product ultimately optimizes for customer relevance and customer value, not simply product promotion.\n\nAI, Data & Recommendation Systems\nPartner closely with data science, machine learning, engineering, and analytics teams to define the recommendation architecture and product requirements.\n\nIdentify and prioritize the customer and commercial signals required to generate high-quality recommendations.\n\nDefine requirements for recommendation eligibility, scoring, ranking, personalization, confidence, explainability, and suppression.\n\nDetermine 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.\n\nPartner with technical teams to develop and improve propensity models, ranking models, customer segmentation, recommendation algorithms, and feedback loops.\n\nDefine how seller actions and customer outcomes can continuously improve recommendation quality.\n\nEvaluate opportunities to use generative AI to provide recommendation explanations, customer insights, conversation preparation, opportunity summaries, and suggested next actions.\n\nEnsure responsible use of AI, including appropriate transparency, explainability, governance, and data quality controls.\n\nProduct Execution\nTranslate product strategy into clear product requirements, customer scenarios, user stories, success criteria, and prioritized backlogs.\n\nPartner closely with engineering, design, data science, analytics, and architecture teams throughout discovery, development, launch, and iteration.\n\nMake prioritization decisions based on customer impact, seller value, commercial opportunity, technical complexity, model performance, and investment requirements.\n\nDefine minimum viable products and phased rollout strategies for complex recommendation capabilities.\n\nLead pilots, experiments, A/B tests, and controlled launches to validate hypotheses and improve the product.\n\nUse quantitative and qualitative data to identify issues, uncover opportunities, and drive continuous product improvement.\n\nManage dependencies across CRM systems, customer data platforms, product catalogues, identity systems, analytics platforms, sales tools, and downstream business processes.\n\nDrive product adoption and operational readiness in partnership with sales operations, enablement, and go-to-market teams.\n\nCommercial Strategy & Business Impact\nDevelop clear business cases for product investments, including potential revenue impact, seller productivity improvements, customer benefits, and implementation costs.\n\nPartner with Sales, Finance, Strategy, and business-unit leaders to understand portfolio priorities and commercial opportunities.\n\nDefine approaches for balancing customer relevance with strategic product priorities and company growth objectives.\n\nIdentify opportunities to improve cross-sell, upsell, product penetration, customer retention, and seller productivity.\n\nEstablish measurable links between recommendation quality, seller behavior, customer outcomes, pipeline creation, and revenue.\n\nAnalyze seller and customer segments to identify where recommendation capabilities can generate the greatest value.\n\nTranslate complex commercial problems into scalable product capabilities rather than one-off processes or manual interventions.\n\nCross-Functional Leadership\nWork closely with Sales, Sales Operations, Engineering, Data Science, UX, Product Marketing, Finance, Customer Success, Strategy, and product business units.\n\nBuild alignment across stakeholders who may have different objectives regarding product prioritization and seller engagement.\n\nInfluence senior leaders through data, structured problem solving, clear recommendations, and compelling product narratives.\n\nFacilitate strategic discussions and drive decisions in ambiguous, cross-functional environments.\n\nCommunicate product strategy, roadmap, trade-offs, risks, and business results clearly to executive stakeholders.\n\nServe as the voice of the seller and customer while balancing broader company priorities.\n\nBuild strong relationships across global and matrixed organizations to accelerate execution and adoption.\n\nSuccess Measures\nSuccess in this role will be measured through a combination of seller adoption, recommendation quality, customer outcomes, operational efficiency, and commercial impact.\nKey metrics may include:\nSeller adoption and monthly active usage\n\nRecommendation engagement rate\n\nRecommendation acceptance rate\n\nPercentage of recommendations acted upon\n\nRecommendation precision, relevance, and quality\n\nSeller satisfaction and trust\n\nReduction in time required for account planning and opportunity discovery\n\nIncrease in qualified opportunities identified\n\nIncremental pipeline generated or influenced\n\nConversion rate of recommended opportunities\n\nIncremental revenue influenced\n\nIncrease in relevant cross-sell and upsell opportunities\n\nGrowth in customer product adoption and solution penetration\n\nImprovement in seller productivity\n\nEffectiveness of seller feedback loops\n\nModel and recommendation performance over time\n\nCustomer outcomes associated with recommended solutions\n\nRequired Qualifications\n5+ 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.\n\nBachelor's degree in Business, Engineering, Computer Science, Economics, Finance, Mathematics, or a related discipline, or equivalent practical experience.\n\nDemonstrated experience defining product or business strategy and translating ambiguous problems into clear, actionable plans.\n\nProven track record of taking complex products, capabilities, or strategic initiatives from problem definition through execution and measurable impact.\n\nStrong commercial and strategic acumen, with the ability to evaluate customer needs, business opportunities, investment priorities, and trade-offs.\n\nExperience developing business cases, defining KPIs, analyzing financial or operational performance, and using quantitative analysis to support decisions.\n\nStrong analytical and structured problem-solving capabilities.\n\nAbility to synthesize large amounts of qualitative and quantitative information into clear insights and recommendations.\n\nExperience working closely with engineering, data science, analytics, UX, and business teams.\n\nStrong understanding of data-driven product development, experimentation, and performance measurement.\n\nAbility to communicate complex technical and business concepts clearly to both technical and non-technical audiences.\n\nStrong executive communication and storytelling skills.\n\nDemonstrated ability to influence senior stakeholders and drive alignment across large, cross-functional organizations.\n\nAbility to operate effectively in ambiguous, fast-changing, and highly matrixed environments.\n\nPreferred Qualifications\nMBA or other relevant advanced degree from a leading business, engineering, technology, economics, or quantitative program.\n\nExperience in management consulting, corporate strategy, investment banking, or a strategic role within a leading technology company.\n\nExperience 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.\n\nExperience combining product management and strategy or consulting, with the ability to move from strategic problem definition into product execution.\n\nExperience developing go-to-market, portfolio, segmentation, growth, cross-sell, upsell, or sales productivity strategies.\n\nExperience building next-best-action, next-best-product, recommendation, personalization, sales intelligence, or AI-assisted decision-support products.\n\nExperience working with B2B enterprise sales organizations.\n\nStrong understanding of enterprise sales motions, account planning, opportunity management, solution selling, and customer lifecycle management.\n\nExperience with CRM and sales platforms such as Salesforce, Microsoft Dynamics 365, or equivalent enterprise ecosystems.\n\nFamiliarity with recommendation systems, machine learning ranking models, 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