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About This Role
As the digital arm for the world's largest food and beverage company, Nestlé IT \& Digital Americas harnesses the power of data, analytics, and innovative technology to deliver transformative solutions and business resiliency for Nestlé businesses and iconic brands from Purina® to Nescafé®. We’re innovators, strategic collaborators, value multipliers, and digital business leaders committed to delivering results that create meaningful experiences and drive value, enabling Nestlé to win in the marketplace. By joining, you become part of a 150\-year legacy and a global team of 270,000\+. We invest in our people—their growth, development, and sense of belonging. This shared unity and purpose is what motivates individuals to join, stay, and advance their careers within Nestlé.
*This position is not eligible for Visa Sponsorship.*
Position Summary
Join Nestlé IT \& Digital as an Expert AI Data Scientist supporting Nestlé USA (NUSA). You'll sit at the seam between business and technology, framing ambiguous business problems and then owning the AI solution end\-to\-end, from problem definition through adoption. This is a hybrid role, equal parts product manager and technical engineer. You won't just build models; you'll direct AI agents, build the verification and workflow scaffolding that lets a product team ship reliably, and leave behind reusable assets that make the next project easier. Success is measured by durable impact and adoption, and as agents and the surrounding tooling keep reinventing themselves, you'll help shape how NUSA scales AI responsibly.
- Serve as a thought leader on emerging AI, GenAI, and agentic technologies, translating breakthrough capabilities into scalable applications aligned with Nestlé's business priorities and enterprise value
- Partner with stakeholders to translate ambiguous business needs into clear, precise specifications, surfacing data requirements, tradeoffs, and failure modes, and act as an internal consultant who scopes problems, defines success criteria, and drives alignment across the lifecycle
- Own AI applications end\-to\-end, from use\-case concept and experimental design through industrialization, deployment, and adoption, staying accountable across the full lifecycle rather than a single phase
- Orchestrate AI agents across parallel streams of work, turning repeated tasks into reusable workflows the broader team can leverage, and incorporate agents into new and existing solutions as the technology landscape evolves
- Design evaluation frameworks, conformance checks, and monitoring that validate AI output against defined specifications before rollout, including detection of silent errors, and sustain solutions in production through agent\-assisted diagnosis of data\-quality and pipeline issues
- Strengthen the team's collective capability by documenting decisions, learnings, and incidents in shared resources, and coach business users and teammates to elevate performance across projects
- Partner across global and regional IT \& Digital teams to co\-create and scale AI solutions, navigating platform roadmaps and enterprise processes to secure approval for needed features and capabilities
- Build solutions with security and resilience in mind, protecting sensitive data and safeguarding the critical business processes they touch, and anticipating failure and misuse so AI systems remain safe, trustworthy, and reliable in production
Requirements
- Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, Data Science, or a related field
- 7\+ years of experience partnering with cross\-functional stakeholders to translate business needs into technical solutions and drive adoption
- 5\+ years with a demonstrated track record of building and shipping AI/ML solutions that real users adopted
- 3\+ years of experience deploying and scaling solutions at enterprise scale, including MLOps and cloud platforms such as Databricks and Snowflake
- 3\+ years of experience delivering production solutions with emerging technologies, advanced data and automation capabilities, or machine learning, with a recent focus on AI, GenAI, LLMs, agentic workflows
Other
- Master's or PhD in Computer Science, Engineering, Mathematics, Statistics, Data Science, or a related discipline is preferred
- Experience directing AI agents or agentic workflows to do substantive work, not just single\-prompt usage, is preferred
- Experience driving AI concepts from idea to enterprise\-scale adoption, ideally in consumer products, CPG, Supply Chain, or Commercial environments is preferred
- Strong hands\-on engineering fluency in Python (or similar) for building, deploying, and integrating AI solutions, including modern LLM and agent patterns such as prompt interaction, tool/function calling, retrieval, and handling non\-determinism, is preferred
*Don't meet all the qualifications listed under "Other"? These are preferred, but not required. When you apply for a role with Nestlé, we ensure that individual confidentiality is held to the highest regard. We are intentional about creating an inclusive workplace for everyone. We consider our associates our most valuable assets. Please apply for full consideration.*
The approximate pay range for this position is $140,000 to $196,000 per year. Please note that 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. 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://nestlejobs.com/nestle\-in\-the\-us.
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: 413470
Salary Context
This $140K-$196K range is above the median for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Nestlé USA, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($168K) sits 13% below the category median. Disclosed range: $140K to $196K.
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.
Nestlé USA AI Hiring
Nestlé USA has 1 open AI role right now. They're hiring across Data Scientist. Based in Arlington, VA, US. Compensation range: $196K - $196K.
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 Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
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).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
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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