Engineer Strategy Program Manager, Commercial Personal Systems

HP · Taipei, Taipei City, Taiwan Region

Spotted 4d agoFull time

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Job description

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Engineer Strategy Program Manager, Commercial Personal Systems

Description -

Job Summary

The Engineering Strategy Program Manager drives data-informed decisions across global Engineering. This role leads strategic analysis, workforce and budget planning, portfolio prioritization, and AI-enabled transformation initiatives.

Partnering with engineering, product, finance, and HR leaders, the Expert develops clear recommendations, planning models, and executive-ready insights that align resources, investments, and organizational capabilities with business priorities.

Responsibilities

Lead end-to-end strategic analyses for engineering investments, product portfolios, resource allocation, operating expenses, capital expenditures, NRE, and productivity initiatives.
Develop fact-based recommendations for executive decisions, including options, trade-offs, risks, financial implications, capacity impacts, and implementation considerations.
Build and maintain planning models for headcount, skills, workforce mix, site strategy, engineering capacity, demand forecasts, hiring plans, contractor utilization, and budget scenarios.
Partner with R&D, product, finance, and program leaders to establish planning assumptions, align priorities, identify gaps, and recommend actions to improve delivery confidence.
Translate ambiguous business questions into structured problem statements, analytical approaches, decision frameworks, and executive-ready outputs.
Design dashboards, scorecards, and management-review materials that provide transparent visibility into portfolio health, resource deployment, budget performance, engineering productivity, and strategic-program outcomes.
Lead analyses related to AI-enabled engineering transformation, including use-case prioritization, adoption measurement, productivity baselines, value realization, training needs, governance, and change-management implications.
Develop business cases and investment proposals for major initiatives, including benefits hypotheses, cost estimates, sensitivity analyses, risk assessments, milestone plans, and success metrics.
Define and improve planning processes, analytical standards, data definitions, governance mechanisms, and reporting rhythms across globally distributed engineering organizations.
Influence senior stakeholders through concise executive storytelling, clear data visualization, and practical recommendations that balance strategic ambition with implementation realities.
Mentor analysts and senior analysts in structured problem solving, financial and operational modeling, executive communication, data quality, and stakeholder management.
Serve as a recognized subject-matter expert in one or more areas such as R&D planning, engineering economics, AI transformation, workforce strategy, portfolio management, operating-model design, or data-driven executive decision support.

Education & Experience Recommended

Four-year or Graduate Degree in Business Administration, Statistics, MIS, or any other related discipline or commensurate work experience or demonstrated competence.
Typically has 7-10 years of relevant experience in engineering strategy, R&D planning, business operations, product strategy, management consulting, finance, program management, technical operations, or a comparable analytical leadership role.
Demonstrated experience supporting decisions in a complex technology, engineering, hardware, software, semiconductor, platform, manufacturing, or product-development environment.
Expert-level ability to structure ambiguous problems, formulate hypotheses, analyze evidence, identify root causes, and develop actionable recommendations.
Strong experience with financial and operational analysis, including budget planning, investment prioritization, resource allocation, scenario modeling, cost-benefit analysis, and business-case development.
Demonstrated capability in workforce and capacity planning, including headcount forecasting, skills analysis, location or site strategy, hiring plans, build-buy-partner decisions, and demand-versus-capacity modeling.
Advanced proficiency in Excel and presentation development, including complex models, scenario analysis, executive dashboards, data visualization, and concise leadership narratives.
Experience using data-analysis and business-intelligence tools such as Power BI, Tableau, SQL, Python, or equivalent platforms; ability to assess data quality and establish reliable metrics.
Ability to communicate complex analyses clearly to executive audiences and tailor the message for technical, financial, and business stakeholders.
Proven ability to lead cross-functional workstreams, align stakeholders with competing priorities, and drive decisions in a global and matrixed organization.
Strong business judgment, intellectual curiosity, attention to detail, and comfort making recommendations despite incomplete or evolving information.
Professional fluency in English, including the ability to write executive pre-reads, decision memos, operating-review materials, and business cases.

Preferred Certifications

PMI PMP, ACP, BPA

Knowledge & Skills

Expert knowledge of engineering strategy, R&D planning, portfolio management, and resource-allocation methodologies.
Strong understanding of technology-product development environments, including hardware, software, platform, firmware/BIOS, system architecture, and global engineering execution.
Deep capability in business analysis, strategic problem solving, hypothesis-driven analysis, and translating ambiguous questions into structured decision frameworks.
Advanced expertise in financial planning and investment analysis, including OPEX, CAPEX, NRE, budget development, cost modeling, return-on-investment analysis, and scenario planning.
Strong knowledge of workforce planning, engineering-capacity modeling, skills forecasting, headcount planning, site strategy, hiring plans, contingent workforce strategy, and demand-versus-capacity analysis.
Advanced analytical and modeling skills using Excel, including complex formulas, pivots, Power Query, financial models, sensitivity analyses, forecasting models, and executive dashboards.
Proficiency in business-intelligence and data-analysis tools such as Power BI, Tableau, SQL, Python, or equivalent platforms for data preparation, visualization, automation, and insight generation.
Ability to establish data definitions, validate source-data quality, reconcile conflicting datasets, and create trusted management metrics.
Knowledge of portfolio prioritization frameworks, including strategic alignment, customer or business impact, technical feasibility, cost, risk, resource demand, and time-to-value.
Strong understanding of engineering productivity, development-cycle performance, operational metrics, delivery health, and mechanisms for identifying execution bottlenecks.
Working knowledge of AI transformation, generative AI, agentic AI, RAG, knowledge-management systems, AI governance, and AI-enabled engineering productivity workflows.
Ability to define AI use cases, build value hypotheses, establish baselines, measure adoption, evaluate realized benefits, and identify change-management needs.
Experience developing executive-ready business cases, including strategic rationale, alternatives, financial impact, risk assessment, decision requests, implementation roadmaps, and success measures.
Exceptional executive communication skills, including concise pre-reads, decision memos, operating-review materials, presentation storytelling, and data visualization.
Ability to communicate effectively with technical engineers, senior engineering leaders, finance partners, product managers, HR, program-management teams, procurement, and external development partners.
Strong stakeholder-management and influence skills, with the ability to build alignment across global, cross-functional, and matrixed organizations.
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