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About This Role
Posted Date
5/28/2026
Description
#### When you join Verizon
You want more out of a career. A place to share your ideas freely — even if they’re daring or different. Where the true you can learn, grow, and thrive. At Verizon, we power and empower how people live, work and play by connecting them to what brings them joy. We do what we love — driving innovation, creativity, and impact in the world. Our V Team is a community of people who anticipate, lead, and believe that listening is where learning begins. In crisis and in celebration, we come together — lifting our communities and building trust in how we show up, everywhere \& always. Want in? Join the \#VTeamLife.
#### What you’ll be doing...
As the Associate Director of Modeling \& Decision Intelligence, you will lead an elite advanced analytics team partnering with our Global Supply Chain, Sourcing, Global Real Estate, Global Network \& Technology and Fleet organizations. You will be the strategic architect responsible for moving our operations away from manual, reactive decision\-making and toward a proactive, autonomous ecosystem.You will work with key stakeholders to generate hypotheses and create analytic models that answer our most impactful business questions. You are responsible for leading the design of data science products while thinking strategically about how these models meet the long\-term goals of the business. You will leverage our full tech stack to develop resolutions to complex problems that require creativity, making decisions that directly impact Verizon's performance, predictability, and operational efficiency across multiple enterprise domains.You will not just manage daily operations; you will also shape the roadmap for how Verizon uses data to drive massive P\&L impact across our network, asset, and infrastructure footprint. You will lead the development of precision engines—such as localized store/SKU demand sensing, margin optimization for returned devices, spatial DC network analysis, and real estate and fleet asset optimization—and act as the ultimate bridge between technical capability and executive\-level business strategy.
- Strategic Leadership \& Decision Design: Direct the team toward high\-ROI business problems. Elevate data science from standard "predictive modeling" to Decision Intelligence, ensuring technical outputs are directly translated into actionable business cases for senior executives (e.g., inventory optimization, spend analytics, and procurement risk mitigation).
- Team Evolution \& Mentorship: Lead, inspire, and upskill a high\-performing team of data scientists and optimization engineers. Conduct regular technical and professional code/design reviews, fostering an environment that embraces AI\-native development.
- Scale the Tech Stack \& Innovation: Stay at the frontier of AI and advanced analytics. Guide the team in leveraging modern tools (Gurobi, Domino Data Lab, VS Code) and emerging technologies (LLMs, RAG, Agentic workflows) to build robust, scalable analytics products.
- Stakeholder Influence \& Translation: Serve as a trusted advisor to senior leadership across Supply Chain, GN\&T, Fleet, and Real Estate. Master the art of Executive Storytelling, translating deep statistical and mathematical programming outputs into clear strategic narratives.
- Ecosystem Governance: Oversee the end\-to\-end lifecycle of the team’s models, ensuring that production\-grade systems are governed, bias\-free, reproducible, and seamlessly integrated into the business's consumption layer.
#### What we’re looking for...
You can visualize the big picture strategy, but can also break it down into the components to get the job done. You believe that the best decisions are based in data and you’re the one that knows how to tease out the key learning within a dataset. You have credibility with people because you are a specialist in all things data but you also know how to bring it down to the level that your audience can understand—that’s practical and will help the business improve. And you’re an effective and inspiring leader who brings out the best in others.
You’ll need to have:
- Bachelor’s degree or four or more years of work experience.
- Eight or more years of relevant experience required, demonstrated through one or a combination of work and/or military experience, or specialized training.
- Experience in data science, with a proven track record in Supply Chain Analytics, Mathematical Optimization, or Predictive Modeling.
- Leadership experience managing and developing a team of data scientists and/or quantitative researchers.
- Deep proficiency in Python, SQL, and advanced statistical modeling.
Even better if you have one or more of the following:
- Master's degree or PhD in Operations Research, Industrial Engineering, Statistics, Economics, or a related quantitative field.
- Strong business and financial acumen, with a demonstrated ability to tie analytical model outcomes directly to financial metrics (CapEx/OpEx reduction, ROI, and P\&L impact).
- Functional understanding or experience working with data structures from core enterprise platforms like SAP S/4HANA (S4\) (for real\-time Supply Chain/Sourcing ERP) and IBM TRIRIGA (for Real Estate \& Facilities Portfolio Management).
- Experience with geospatial analysis, routing optimization, or location intelligence frameworks (e.g., GeoPandas, Kepler.gl, or GIS tools) applied to network or fleet logistics.
- Experience with large\-scale mathematical optimization solvers, specifically Gurobi or CPLEX.
- Experience managing enterprise\-grade data science lifecycles within platforms like Domino Data Lab.
- AI\-Native Workflow Experience: Familiarity with integrating LLMs, RAG architectures, or advanced coding assistants (e.g., Claude Code, Cursor) into data science workflows to accelerate delivery.
- Expertise in interactive visualization and delivery frameworks, such as Plotly, Streamlit, or Tableau, to democratize model consumption.
- Strong executive communication and negotiation skills, with a demonstrated ability to influence senior business stakeholders.
If Verizon and this role sound like a fit for you, we encourage you to apply even if you don’t meet every “even better” qualification listed above.
#### Where you’ll be working
In this hybrid role, you'll have a defined work location that includes working from home and a minimum of three days per week in the office, which will be set by your manager. Employees are responsible for maintaining compliance with hybrid work policies.#### Scheduled Weekly Hours
40#### Equal Employment Opportunity
Verizon is an equal opportunity employer. We evaluate qualified applicants without regard to veteran status, disability or other legally protected characteristics.
#### Benefits and Compensation
Our benefits are designed to help you move forward in your career, and in areas of your life outside of Verizon. From health and wellness benefit options including: medical, dental, vision, short and long term disability, basic life insurance, supplemental life insurance, AD\&D insurance, identity theft protection, pet insurance and group home \& auto insurance. We also offer a matched 401(k) savings plan, up to 8 company paid holidays per year and up to 6 personal days per year, paid parental leave, adoption assistance and tuition assistance, plus other incentives, we’ve got you covered with our award\-winning total rewards package. Depending on the role, employees have the opportunity to receive compensation in the form of premium pay such as overtime, shift differential, holiday pay, allowances, etc. Newly hired employees receive up to 15 days of vacation per year, which grows with additional service. For part\-timers, your coverage will vary as you may be eligible for some of these benefits depending on your individual circumstances.
The salary will vary depending on your location and confirmed job\-related skills and experience. This is an incentive based position with the potential to earn more. For part\-time roles, your compensation will be adjusted to reflect your hours.The annual salary range for the location(s) listed on this job requisition based on a full\-time schedule is: $126,000\.00 \- $242,000\.00\.
Salary
126,000\.00 \- 242,000\.00 Annual
Type
Full\-time
Salary Context
This $126K-$242K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Information Technology Senior Management Forum, 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 Required
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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. Disclosed range: $126K to $242K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Information Technology Senior Management Forum AI Hiring
Information Technology Senior Management Forum has 34 open AI roles right now. They're hiring across AI Engineering Manager, Data Scientist, AI/ML Engineer, Data Engineer. Positions span San Jose, CA, US, Jersey City, NJ, US, McLean, VA, US. Compensation range: $167K - $335K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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