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About This Role
At Nestlé Health Science, we believe that nutrition, science, and wellness must merge, not collide. Here, we embrace the intrinsic connections of these three pillars, harnessing their collective strength to empower healthier lives. Our broad product portfolio includes renowned brands like Garden of Life®, Nature's Bounty®, Vital Proteins®, Orgain®, Nuun®, BOOST®, Carnation Breakfast Essentials®, Peptamen®, Compleat Organic Blends®, and more. We also have extensive pharmaceutical expertise, offering innovative medicines that aim to prevent, manage, and treat gastrointestinal and metabolic-related diseases.
At Nestlé Health Science, we bring our best for better lives. Our people are challenged to bring fresh, diverse views and make bold moves to empower healthier lives through nutrition. We know brilliant ideas can come from anyone, anywhere. Here we embrace the entrepreneurial spirit and collaborate with teams that champion focused and forward thinking. We are committed to fostering professional growth and celebrating the achievements of our people along the way. We offer dynamic career paths, robust development, opportunities to learn from talented colleagues around the globe, and benefits that support physical, financial, and emotional wellbeing.
Join us to innovate for impact and reimagine the future of health and nutrition for patients and consumers.
POSITION OVERVIEW: We are looking for a Senior Data Scientist to develop and deploy advanced analytics and AI solutions that drive business impact in Consumer Insights and Analytics team. As a Strategic Partner to Marketing and Sales teams, success in this role requires a strong analytical mindset, expertise in building machine learning models, optimizing performance, and integrating cutting-edge technologies to enhance decision-making and automation. KEY RESPONSIBILITIES:* Advanced Analytics & Insights: Develop and implement data science solutions, leveraging advanced analytics to drive data-driven decision-making, optimize business performance, and support Consumer Insights & Analytics priorities.
- Data Analysis & Modeling: Analyze large and complex datasets to identify patterns, build statistical and machine learning models, and solve critical business problems using big data technologies like Databricks, Azure ML, and Apache Spark, as well as strong data visualization for structured storytelling to drive impactful business decisions.
- Developing Advanced Models: Design deep learning and natural language processing models to analyze complex data across business processes.
- Model Monitoring & Performance Optimization: Continuously track, evaluate, and refine deployed models to maintain accuracy and relevance over time.
- AI Research & Evaluation: Research, test, and evaluate AI technologies to ensure the best tools and platforms are used for achieving business objectives.
- Enhancing Analytical Capabilities: Integrate the latest data science innovations into product solutions, enhancing data, analytical, and automation capabilities.
- Cross-Functional Collaboration: Work closely with cross-functional teams and stakeholders to provide expertise across omnichannel analytical projects and ensure alignment with business goals.
- Tool Development & Maintenance: Build, maintain, and improve analytics tools to enhance capabilities and accelerate speed to value.
EXPERIENCE AND EDUCATION REQUIREMENTS:* Bachelor’s, Master’s, or Ph.D. in Computer Science, Data Science, Mathematics, Physics, Operations Research, Statistics, or a related field.
- 5+ years of experience as a Data Scientist, Machine Learning Engineer, or Applied Scientist, specializing in computer vision, natural language processing, or data synthesis.
- Experience developing and deploying advanced analytical models and algorithms.
- Experience applying machine learning techniques, including Regression, Clustering, Probability Networks, Artificial Intelligence (AI), and Deep Learning.
- Hands-on programming experience in Python and R, leveraging advanced cognitive and machine learning methods to solve business challenges.
- Experience working with relational database and cloud computing technologies such as Databricks, Snowflake, AWS, Azure ML and AMC, Big Query.
- Experience in transforming raw data into meaningful features for machine learning models.
- Expertise in handling missing data, scaling, encoding categorical variables, and dimensionality reduction techniques.
- Hands on experience in designing and analyzing A/B tests and other experimental methodologies.
- Hands on experience in causal inference techniques to measure the impact of business decisions.
SKILLS:* Experience visualizing and presenting data-driven insights using Power BI and Tableau.
- Experience in developing recommendation engines for applications such as sales, customer targeting, or supply chain optimization.
- Experience in building and implementing optimization models to enhance decision-making processes.
- Familiarity with agile methodologies and rapid prototyping in data science projects.
- Strong knowledge of industry-standard tools and methodologies for model training, statistical analysis, and machine learning deployment.
- Experience working with CPG and ecommerce data sets, including retail analytics and advertising data, is a plus.
- Candidates that have experience working with AMC (Amazon Marketing Cloud) and Amazon advertising and sales data will be given priority consideration.
This position will be either a remote or hybrid role based on the selected candidate’s geographic location. Preference will be given to applicants who live within a commutable distance of Bridgewater, NJ.
The approximate pay range for this position is $130,000 to $160,000. 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 About Us | Nestlé Careers (nestlejobs.com).
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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: 413100
Salary Context
This $130K-$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 ($145K) sits 24% below the category median. Disclosed range: $130K 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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