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Overview: Role overview
IDC is seeking a Vice President, Research, Planning & Operations t to join its AI Economics research team. The role will help enterprise leaders prioritize investments in artificial intelligence (AI), understand the full cost of deployment and operation, and measure business value across a portfolio of business processes and use cases.
We are looking for a senior practitioner who has held direct responsibility for the financial and operational outcomes of AI deployments in large organizations, across multiple business functions and domains. You will bring that experience into independent research, financial models, and executive advice for chief information officers (CIOs), chief financial officers (CFOs), and business leaders. Responsibilities* Contribute to the research agenda for enterprise AI economics, drawing on deployment experience to address investment prioritization, budgeting, forecasting, cost management, and value realization across business functions.
- Help develop and validate IDC AI economic models and methods covering cost structures, value realization, unit economics, payback, and scenario analysis. Distinguish initial investment from recurring costs, and forecast benefits from measured outcomes.
- Advise CIOs, CFOs, and business leaders on AI portfolio decisions, including build or buy choices, deployment options, funding, scaling, and when to redesign or discontinue use cases. Explain assumptions, uncertainty, and financial trade-offs.
- Build benchmarks and case studies across horizontal business functions and domain-specific processes. Analyze how adoption, workflow changes, output quality, human oversight, and risk affect cost and realized business value.
- Partner with the IDC sourcing advisory team to incorporate ongoing price benchmarks and commercial terms into economic models, including their effects on cost at scale.
- Produce syndicated and custom research, brief clients, and represent IDC in executive discussions and industry events. Translate evidence from enterprise deployments into clear, defensible research and practical guidance.
Required qualifications* Senior practitioner experience leading AI deployments in large organizations, with direct responsibility or defined shared accountability from business case and funding through production, adoption, and post-deployment performance. Candidates must explain their own decisions and resulting outcomes.
- Hands-on delivery across multiple distinct business functions or domains, including horizontal corporate workflows and domain-specific operational processes. Examples include finance, procurement, human resources, customer service, and supply chain or industry operations. Experience within one industry is acceptable; multiple projects within one functional silo do not meet the breadth requirement.
- Direct responsibility for AI budgets, forecasts, and investment models covering capital and operating expenditure, cost structures, value realization, unit economics, and sensitivity analysis. Experience tracking actual costs and benefits against forecasts and revising assumptions as adoption and scale change.
- Detailed knowledge of lifecycle costs: data, integration, software and model licensing, compute, implementation, evaluation and monitoring, security and governance, adoption, human oversight, and ongoing support. Ability to model unit cost at different volumes and service levels, and explain the financial implications of AI platform, deployment, and sourcing choices.
- Evidence of measuring and realizing business value. Ability to distinguish cash savings, cost avoidance, capacity released, revenue or margin contribution, and service or risk improvements; establish baselines and benefit owners; and address attribution, timing, and double counting.
- Experience presenting to and advising both CIOs and CFOs on AI investments, funding, operating costs, and realized value, supported by strong working relationships with finance, technology, and business leaders.
- Ability to produce clear research and executive presentations, evaluate financial and operational data, normalize survey and benchmark findings, and turn lessons from individual deployments into defensible models and broader guidance.
- Candidates may come from enterprise operating roles or consulting firms. Consulting candidates must demonstrate hands-on delivery for large enterprise clients, with identifiable responsibility for implementation economics and measurement of results after launch.
- A bachelor's degree is required.
Preferred qualifications* Experience publishing research, developing reusable economic models, or advising multiple enterprise clients; experience with generative AI and agentic AI.
- A master's degree in a relevant field.
Location and travel
This role can be based remotely in an approved U.S. location or at IDC's Boston office. Travel of approximately 25-30% is expected. Why This Role Stands OutAt IDC, your work helps shape how the world understands technology and where it goes next. You collaborate with curious, high-caliber colleagues who value rigor, integrity, and shared success. As the premier global provider of trusted technology intelligence, IDC equips business and technology leaders with the evidence they need to make confident decisions. Our insights inform strategy, investment, and innovation across industries and regions. Recognized by IIAR as Analyst Firm of the Year for five consecutive years, IDC sets the standard for credibility and impact. With more than 1,000 analysts worldwide and a truly global perspective, we combine deep expertise with practical relevance. Here, your ideas matter, your voice is heard, and your contributions provide the insights leaders rely on every day. It is meaningful work, backed by a culture that supports growth, collaboration, and long-term career development with a globally respected brand. Compensation Transparency At IDC, we are committed to fair and equitable pay practices. Employees are compensated equitably for their work, aligned with their skills and experience. Salary and incentive structures are determined through a rigorous process that considers experience, education, certifications, role-specific requirements, internal equity, and verified U.S. market data from an independent third-party partner. The expected total annual compensation, depending on location and experience, is between $180,000-$240,000, inclusive of base salary and variable compensation. Equal Opportunity Employer IDC is committed to providing equal employment opportunities for all qualified persons. Employment eligibility verification required. We participate in E-Verify. IDC is currently able to employ remote workers in the following states: Arizona (AZ), California (CA), Colorado (CO), Connecticut (CT), Washington D.C. (DC), Florida (FL), Georgia (GA), Illinois (IL), Indiana (IN), Kansas (KS), Massachusetts (MA), Maryland (MD), Maine (ME), Michigan (MI), Minnesota (MN), Missouri (MO), Mississippi (MS), North Carolina (NC), New Hampshire (NH), New Jersey (NJ), New York (NY), Ohio (OH), Oregon (OR), Pennsylvania (PA), Rhode Island (RI), South Carolina (SC),
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Salary Context
This $180K-$240K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At IDC Research Inc., 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 $212,000 based on 11,866 positions with disclosed compensation. Disclosed range: $180K to $240K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
IDC Research Inc. AI Hiring
IDC Research Inc. has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Boston, MA, US, US. Compensation range: $190K - $240K.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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