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Applied AI Strategy Business Planning Lead \- Chief of Staff
Description \-
HP's Applied AI organization is at the center of HP's enterprise AI transformation. The team builds internal AI solutions, runs the AI platform, develops new AI tools and infrastructure, and enables AI training and adoption for employees across the company.
We are seeking a highly organized, strategic, and execution\-oriented senior professional to serve as the Strategy \& Business Planning Lead for Applied AI. This person will act as an operating partner to the VP, Head of Applied AI, helping a 120\+ person organization run with clarity, discipline, and speed while ensuring the team is well represented with senior leaders and cross\-functional partners.
The ideal candidate is a strong collaborator and executive communicator who can manage a continual flow of leadership requests, synthesize complex AI and technology work into crisp narratives, maintain financial and operational discipline, and drive coordination across leaders, workstreams, and peer Chief of Staff / business operations teams.
Key responsibilities
Executive communications and storytelling
- Develop high\-quality presentation materials for C\-suite and senior executive audiences, including strategy updates, operating reviews, transformation narratives, investment cases, and progress reports.
- Partner with Applied AI leaders to translate technical AI, platform, infrastructure, and product work into clear business narratives and measurable outcomes for HP.
- Create reusable communication frameworks and establish recurring forums to improve how Applied AI explains its strategy, priorities, and impact.
Operating rhythm and organizational effectiveness
- Design and manage the Applied AI operating cadence, including staff meetings, leadership meetings, business reviews, OKR checkpoints, team\-wide meetings, planning cycles, and offsites.
- Drive the annual and quarterly priority\-setting process, ensuring goals, OKRs, milestones, dependencies, and owners are clear across the organization.
- Coordinate internally facing initiatives that improve team effectiveness, coordination, engagement, planning discipline, and cross\-functional alignment.
- Partner with finance, HR, and Applied AI leadership to maintain budget, forecast, headcount, contractor, vendor, and investment trackers.
Transformation initiatives
- Engage regularly with Chiefs of Staffs, strategy teams, and peer organizations across HP to share information, resolve dependencies, and align on enterprise initiatives.
- Lead or coordinate high\-priority special projects for Digital, especially initiatives that cut across multiple teams or require executive visibility.
- Bring structured problem solving, analytical rigor, and practical judgment to ambiguous business and operating challenges.
Required capabilities and qualifications
- 10\+ years of experience in business operations, strategy and planning, Chief of Staff roles, program / portfolio management, transformation, consulting, or a related field.
- Exceptional executive communication skills, including the ability to build clear, concise, visually strong presentations for C\-suite and senior leadership audiences.
- Strong business acumen and financial literacy, including comfort with budgets, headcount / resource planning, and operational metrics.
- Demonstrated ability to manage complex intake, competing priorities, multiple stakeholders, and fast\-moving deadlines with minimal supervision.
- Strong collaboration and influencing skills; able to work through leaders and peers without direct authority and build trust across functions.
- Technical fluency and curiosity: ability to understand AI, data, software, platform, infrastructure, and responsible AI concepts well enough to translate them into business\-relevant narratives and operating plans.
- Bachelor's degree required; MBA, MS, or advanced degree preferred.
Preferred qualifications
- Experience working in or supporting AI, data science, software engineering, cloud / platform, infrastructure, automation, or enterprise technology organizations.
- Experience supporting a VP\-level or senior executive leadership team in a Chief of Staff, business planning, strategy, or operations capacity.
- Familiarity with AI adoption, AI governance, responsible AI, privacy, security, model lifecycle, or enterprise AI enablement topics.
- Experience building operating mechanisms for a complex organization, including OKRs, portfolio reviews, roadmap reviews, executive dashboards, and team cadences.
Pay \& Benefits
The pay range for this role is $130,700 to $205,200 USD annually with additional
opportunities for pay in the form of bonus and/or equity (applies to United
States of America candidates only). Pay varies by work location, job\-related
knowledge, skills, and experience.
Benefits:
HP offers a comprehensive benefits package for this position, including:
- Health insurance
- Dental insurance
- Vision insurance
- Long term/short term disability insurance
- Employee assistance program
- Flexible spending account
- Life insurance
- Generous time off policies, including;
- 4\-12 weeks fully paid parental leave based on tenure
- 11 paid holidays
- Additional flexible paid vacation and sick leave
- US benefits overview https://hpbenefits.ce.alight.com/
The compensation and benefits information is accurate as of the date of this
posting. The Company reserves the right to modify this information at any time,
with or without notice, subject to applicable law.
Job \-
Business Planning
Schedule \-
Full time
Shift \-
No shift premium (United States of America)
Travel \-
Relocation \-
Equal Opportunity Employer (EEO) \-
HP, Inc. provides equal employment opportunity to all employees and prospective employees, without regard to race, color, religion, sex, national origin, ancestry, citizenship, sexual orientation, age, disability, or status as a protected veteran, marital status, familial status, physical or mental disability, medical condition, pregnancy, genetic predisposition or carrier status, uniformed service status, political affiliation or any other characteristic protected by applicable national, federal, state, and local law(s).
Please be assured that you will not be subject to any adverse treatment if you choose to disclose the information requested. This information is provided voluntarily. The information obtained will be kept in strict confidence.
For more information, review HP’s EEO Policy or read about your rights as an applicant under the law here: “ Know Your Rights: Workplace Discrimination is Illegal "
Salary Context
This $130K-$205K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At HP, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $250,000. This role's midpoint ($167K) sits 22% below the category median. Disclosed range: $130K to $205K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
HP AI Hiring
HP has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Spring, TX, US, San Francisco, CA, US, Palo Alto, CA, US. Compensation range: $175K - $230K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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