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About This Role
Foods you love. Brands you trust. And a career that empowers you to grow.
At Nestlé USA, we’re all working towards the same goal – to delight and deliver for our consumers. With a rich portfolio of beloved brands, including DiGiorno, Toll House, and Coffee mate, in 97% of U.S. households, we have a unique opportunity – and responsibility – to be there for every moment in our consumers’ lives.
Joining Nestlé means becoming part of an inclusive workplace that inspires innovation, encourages strategic thinking and creativity, and celebrates your achievements. No matter where you work within the organization, you are empowered to challenge the status quo, embrace risk-taking, and pioneer new ideas. Our supportive and collaborative environment encourages bold ambitions and continuous learning so that everyone can grow and thrive.
This position is not eligible for Visa Sponsorship.
Position Summary
This leadership position will be responsible for overseeing Commercial advanced analytics and AI/GenAI strategy, acting as the primary bridge between Commercial business stakeholders and IT/technical delivery teams. This role identifies opportunities and connects with internal/external delivery partners to deliver solutions that drive growth and influence the commercial strategy and daily operations through the implementation of data science, advanced analytics, and AI/GenAI capabilities.
This role will collaborate closely with and report to the Director of Business Intelligence & Data Analytics of Commercial Intelligence Hub to drive the future of advanced analytics and AI/GenAI innovation within the Nestlé Center for Demand Generation. The ideal candidate will have a strong technical/data science background with proven experience as a business-savvy innovation leader — someone who understands the craft deeply enough to guide, challenge, and quality-check technical teams, and work closely with business stakeholders, shaping strategy, prioritizing use cases.
This role requires excellent communication skills, strategic thinking, and the ability to drive data-driven decision-making across the organization while acting as a trusted intermediary between business stakeholders and technical implementers. The role will lead, both directly & indirectly, a team of analysts and advisors responsible for generating advanced analytics and AI/GenAI solutions and driving business alignment for inflight and future innovation projects. The role will also be responsible for boosting solution adoption by different functions within Nestlé's Commercial organization.
Primary Responsibilities
- Work closely with commercial business functions to assess needs and determine the most effective means of facilitating decision-making through accurate, relevant and timely insights.
- Collaborate with cross-functional teams to identify and prioritize data science, AI, and GenAI projects that drive business value and align with Commercial strategic priorities.
- Lead the development and implementation of advanced AI and GenAI solutions (e.g., agentic workflows, LLM-powered assistants, machine learning models) in partnership with IT and technical delivery partners, ensuring solutions are both technically sound and grounded in business value.
- Serve as the primary liaison between Commercial business stakeholders and IT/technical implementation teams — framing ambiguous business problems into clear technical specifications and ensuring delivered solutions map back to measurable business outcomes.
- Identify, evaluate, and champion emerging AI, GenAI, and automation capabilities, translating them into scalable innovation initiatives aligned with Nestlé's commercial priorities.
- Drive the implementation, operationalization, and adoption of advanced analytics and AI/GenAI solutions within Commercial teams, recognizing that adoption — not just technical delivery — is the primary driver of ROI.
- Collaborate with commercial leaders by providing technical expertise to guide the prioritization and feasibility of analytics and AI initiatives.
- Represent Commercial team as part of Nestle’s cross functional Data Council to influence enterprise analytics strategic priorities and ensure alignment with other pillars in the organization (finance, supply chain, etc.).
Skills & Competencies
- Self-starter with a history of applying advanced analytics and AI/GenAI expertise to identify insights that get results.
- Passionate about solving the most critical business problems through M/L, GenAI, and advanced analytics.
- Demonstrated ability to translate ambiguous business problems into clear, actionable AI/data science solutions — equally comfortable engaging business executives and technical architects/engineers.
- A technical foundation (data science, ML/AI) that has evolved into strategic, business-facing leadership — able to guide, challenge, and quality-check technical teams.
- Strategic thinker with ability to see the big picture and draw connections outside the immediate organization.
- Knowledge of how to identify, scope and manage work to execute advanced analytics and AI/GenAI opportunities to meet business needs with an emphasis to deliver value with speed.
- Knowledge of process improvement and design methodologies.
- Project management and stakeholder management experience and expertise to deliver scalable solutions in a fast-paced and challenging environment, including leading through influence across matrixed business and IT teams.
- Strong executive presence and storytelling skills, with the ability to prepare and deliver clear, compelling presentations that communicate recommendations, findings, results, and status updates on advanced analytics and AI/GenAI initiatives — tailoring message and technical depth for audiences ranging from technical delivery teams to senior business and IT leadership.
- An Agile mindset with ability to shift priorities to meet rapidly evolving business needs.
Qualifications & Requirements
- Bachelor's degree required, preferably in Computer Science, Engineering, Mathematics, Statistics, Data Science, or a related field. MBA or advanced degree is a strong plus.
- Deep understanding of sales, marketing, or broader commercial functions, with 3-5 years of experience leading teams in a consumer products organization preferred.
- 7+ years of experience partnering with cross-functional stakeholders to translate business needs into technical solutions and drive adoption.
- Demonstrated career progression from a hands-on technical/data science practitioner into a strategic, business-facing leadership role — with the credibility to guide technical teams.
- 5+ years with a demonstrated track record of leading effective teams that deliver enterprise scale solutions with emerging technologies, advanced data and automation capabilities, or machine learning, AI, GenAI, LLMs, and agentic workflows that have strong adoption and proven business impact.
- Experience with advanced analytics and/or digital transformation projects preferred.
- Knowledge of various programming languages (R, Python, SQL) to develop and apply analytic solutions — sufficient to credibly review technical designs and evaluate trade-offs, even if not coding day-to-day
- Experience implementing Agile development methodologies and principles.
- Experience working with business stakeholders and senior leaders in advising on and delivering data analytics information and technology solutions to drive business value.
- Experience and understanding of a variety of analytical processes, including but not limited to data governance, data integration, quality, and measurement.
- Experience building a data and analytics organizational strategy and managing multifunctional teams of advanced data modelers, data architects, data scientists, etc.
- Experience working with large data sets from multiple sources and implementing visualization capabilities leveraging mainstream commercial software platforms.
- Experience exploring, implementing, and deploying advanced capabilities, including cognitive technology, ML/AI, automation, and digital user engagement techniques.
- A proven track record of developing actionable and profitable strategies that also drive business growth.
Salary: $190,000.00 to $240,000.00 per year
Bonus Eligibility: Yes
The pay range provided is a good faith estimate for the position at the time of posting. Final compensation may vary based on factors including but not limited to knowledge, skills and abilities as well as geographic location. It is not typical for an individual to be hired at top of range.
Nestlé offers performance-based incentives and a competitive total rewards package, which includes a 401k with company match, healthcare coverage and a broad range of other benefits. Incentives and/or benefit packages may vary depending on the position. Learn more at https://www.nestlejobs.com/nestle-in-the-us-benefits
It is our business imperative to remain a very inclusive workplace.
To our veterans and separated service members, you're at the forefront of our minds as we recruit top talent to join Nestlé. The skills you've gained while serving our country, such as flexibility, agility, and leadership, are much like the skills that will make you successful in this role. In addition, with our commitment to an inclusive work environment, we recognize the exceptional engagement and innovation displayed by individuals with disabilities. Nestlé seeks such skilled and qualified individuals to share our mission where you’ll join a cohort of others who have chosen to call Nestlé home.
The Nestlé Companies are equal employment opportunity employers. All applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status or any other characteristic protected by applicable law. Prior to the next step in the recruiting process, we welcome you to inform us confidentially if you may require any special accommodations in order to participate fully in our recruitment experience. Contact us at [email protected] or please dial 711 and provide this number to the operator: 1-800-321-6467.
This position is not eligible for Visa Sponsorship.
Review our applicant privacy notice before applying at https://www.nestlejobs.com/privacy.
Job Requisition: 421468
Salary Context
This $190K-$240K range is above the 75th percentile 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 Nestlé USA, 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. Disclosed range: $190K 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.
Nestlé USA AI Hiring
Nestlé USA has 4 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Arlington, VA, US, Bridgewater, NJ, US. Compensation range: $160K - $240K.
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
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