Interested in this AI/ML Engineer role at SAS?
Apply Now →About This Role
: Vice President, Data, Analytics and AI- Hybrid | Cary, North Carolina
We’re a leader in data and AI. Through our software and services, we inspire customers around the world to transform data into intelligence and questions into answers.
If you’re looking for a dynamic, fulfilling career with flexibility and a world-class employee experience, you’ll find it here. We’re recognized around the world for our inclusive, meaningful culture and innovative technologies by organizations like Fast Company, Forbes, Newsweek and more.
About the job
The Data, Analytics and AI organization is looking for a Vice President, Data, Analytics and AI to turn enterprise data into trusted intelligence, scalable AI and measurable business value. This executive will define the function’s charter and structure, establish a modern enterprise strategy and lead a global team across data, analytics, AI platforms, governance, architecture, engineering and adoption.
This is a high-impact opportunity for a business-minded technology leader who can bring clarity to a complex data landscape, align senior leaders around shared priorities and standards, and move the company from fragmented data and isolated use cases toward trusted, reusable enterprise capabilities.
As a Vice President, Data, Analytics and AI, you will:
- Define and execute the enterprise data, analytics and AI strategy and multi-year roadmap, aligned with corporate priorities and measurable business outcomes.
- Build the Data, Analytics and AI organization, including its charter, operating model, organizational structure, leadership team, capabilities and ways of working.
- Maximize the value of enterprise data, analytics and AI platforms by improving trust, usability, adoption, scalability and value realization.
- Establish transparent governance and investment processes that clarify ownership, priorities, standards, decision rights and accountability.
- Partner with the C-suite, technology leaders and business executives to identify high-value opportunities and translate strategy into scalable enterprise capabilities.
- Build, develop and lead high-performing global teams across engineering, architecture, governance, data science, analytics and operations.
- Communicate strategy, tradeoffs, risks, dependencies and outcomes clearly at every level of the organization.
- Embrace curiosity, passion, authenticity and accountability. These are our values and influence everything we do.
Required qualifications
- Bachelor’s or Master’s degree in computer science, data science, business analytics or related field.
- Minimum 10 years of progressive leadership experience in data and analytics, including ownership and delivery of enterprise data strategy.
- Demonstrated success leading enterprise-scale data, analytics or AI transformation, including strategy development and operating-model evolution.
- Experience establishing data governance, improving data quality and driving adoption of trusted data across the business.
- Executive-level communication and influencing skills, with demonstrated ability to translate data, analytics and AI capabilities into business impact.
- Experience building and leading global, high-performing teams and balancing long-term strategy with hands-on execution.
- Equivalent combination of related education, training and experience may be considered in place of the above qualifications.
Additional competencies, knowledge and skills
- Enterprise Strategy: Creates a clear, business-aligned vision and roadmap for enterprise data, analytics and AI capabilities.
- Leading Change: Builds alignment, establishes accountability and guides complex organizations through transformation and adoption.
- Executive Influence: Communicates with clarity, navigates tradeoffs and gains commitment from senior stakeholders across the enterprise.
- Experience serving as the senior-most data, analytics or AI leader within a large or complex organization.
- Experience modernizing multi-platform data environments and simplifying legacy data ecosystems.
- Deep expertise in enterprise data architecture, platforms, governance and advanced data-management practices.
- Hands-on leadership of AI platforms and responsible AI practices in a large, complex organization.
- Experience leading globally distributed teams of 40 or more professionals and building councils or federated stewardship models.
- Experience developing strategies across structured and unstructured data.
World-class benefits Highlights include...* Comprehensive medical, prescription, dental and vision plans.
- Medical plan options include: + PPO with low annual deductible and copays. + HDHP combined with a health savings account with a contribution from SAS (no access to on-site health care center).
- Onsite Health Care Center (HQ) that’s free to employees and family members enrolled in the PPO plan. There’s a pharmacy too! Not local to HQ? The pharmacy will ship prescriptions for no additional charge!
- An industry-leading 401k plan.
- Tuition Assistance Program and programs and resources to support your development
- Generous time away including vacation time, a variety of paid holidays, and our much-loved U.S. Winter Wellness Break between December 25 and January 1.
- Volunteer Time Off, parental leave and unlimited paid sick days.
- Generous childcare benefits for all full-time employees.
You are welcome here.At SAS, it’s not about fitting into our culture – it’s about adding to it. We believe our people make the difference. Our inclusive workforce brings together unique talents and inspires teams to create amazing software that reflects the diversity of our users and customers.
#LI-CCAdditional Information To qualify, applicants must be legally authorized to work in the United States, and should not require, now or in the future, sponsorship for employment visa status. SAS is an equal opportunity employer. All qualified applicants are considered for employment without regard to any characteristic protected by law. Read more: Know Your Rights.
Resumes may be considered in the order they are received. SAS employees performing certain job functions may require access to technology or software subject to export or import regulations. To comply with these regulations, SAS may obtain nationality or citizenship information from applicants for employment. SAS collects this information solely for trade law compliance purposes and does not use it to discriminate unfairly in the hiring process. SAS only sends emails from verified “sas.com” email addresses and never asks for sensitive, personal information or money. If you have any doubts about the authenticity of any type of communication from, or on behalf of SAS, please contact [email protected].
Let’s stay in touch! Join our Talent Community to stay up to date on company news, job updates and more.
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 SAS, 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.
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.
SAS AI Hiring
SAS has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Cary, NC, US, Sydney, New South Wales, Australia.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.