Interested in this Data Scientist role at Nestlé USA?
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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 a Sr. Data Scientist supporting Nestlé USA Digital, where you'll partner with the Supply Chain Planning team and the Analytics team to build the forecasting solutions that power how Nestlé USA plans and operates at scale. You'll design and deploy production-grade forecasting models, embed AI into how forecasts get delivered, and serve as a key liaison between the technical and functional sides of Enterprise Forecasting. You'll bring a strong AI mindset, curiosity about emerging technologies, and comfort operating in ambiguity as the forecasting landscape evolves.
- Design, develop, test, and deploy statistical and machine learning forecasting models in production on cloud-based analytics platforms, with a focus on demand pattern recognition, algorithm selection, outlier correction, and parameter optimization to drive forecast accuracy and bias reduction
- Build and maintain structured, programmatic forecasting processes that deliver at scale, leveraging ML Ops practices to automate weekly forecast generation and reduce manual effort
- Embed Agentic AI and GenAI capabilities into forecasting workflows to identify accuracy improvement opportunities, automate root cause analysis, and support insights generation and decision-making
- Continuously explore and apply emerging AI/ML techniques such as deep learning and probabilistic forecasting to improve forecast accuracy and scalability across a complex portfolio of SKUs, clusters, and data sources
- Serve as a liaison between technical and functional teams, partnering with Supply Chain Planning leads and cross-functional stakeholders across Marketing, Finance, and Sales to align business needs with system and process solutions
- Advance Enterprise Forecasting as part of the One Planning initiative and support Customer Order Fulfilment improvements, enabling standardized, scalable, and cross-functional forecasting solutions across the organization
- Deliver training and forecast office hours to Supply Chain Planning users to build analytics literacy, promote adoption, and increase enterprise value
- Create and enhance Power BI reports for forecast KPI monitoring and data validation, and present findings through data visualization and PowerPoint tailored to the audience
Requirements
- Bachelor's degree in statistics, mathematics, economics, business, or a related field
- 5+ years of experience in data science, forecasting, or advanced analytics, with hands-on experience in SQL, Python, or a similar language
- 5+ years of experience extracting, transforming, and analyzing large datasets, and creating data visualizations to communicate insights
- 3+ years of experience deploying and maintaining machine learning models in production, including regression, classification, time series modeling, and feature engineering
Other
- Master's degree in statistics, mathematics, economics, business, or a related discipline is preferred
- Experience with ML Ops practices, cloud and AI/ML platforms (Databricks, Snowflake, Microsoft Azure), GenAI solutions, and Microsoft Power BI is preferred
- Experience supporting enterprise-scale transformation or Supply Chain planning initiatives in an Agile environment 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 $110,000 to $160,000 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: 413481
Salary Context
This $110K-$160K range is below the median for Data Scientist roles in our dataset (median: $167K across 172 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 9,653 AI roles we're tracking, Data Scientist positions make up 5% 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 $190,000 based on 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($135K) sits 29% below the category median. Disclosed range: $110K to $160K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; 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% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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).
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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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