Senior Data Scientist - AI for Health (Remote/Hybrid)

Nestlé; Bridgewater Township (NJ); Hybrid; USD 130,000 - 160,000; Full time

Benefits offered by this job

401k with company match

Healthcare coverage

Performance-based incentives

Job summary

Nestlé Health Science is seeking a Senior Data Scientist to develop and deploy AI-driven analytics within the Consumer Insights and Analytics team. The role partners with Marketing and Sales to translate data into actionable business decisions and automation.

You will build advanced models, apply NLP and deep learning, and continuously monitor model performance while collaborating with cross-functional teams.

Qualifications

• 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.

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.

Skills

Power BI

Tableau

Python

R

Databricks

Azure ML

Apache Spark

NLP

Deep Learning

A/B testing

Causal inference

AMC

Amazon Marketing Cloud

Education

Bachelor’s/Master’s/Ph.D. in Computer Science/Data Science/Mathematics/Statistics

Tools

Databricks

Snowflake

AWS

Azure ML

BigQuery

Amazon Marketing Cloud (AMC)

Job description

Nestlé Health Science is seeking a Senior Data Scientist to develop and deploy AI-driven analytics within the Consumer Insights and Analytics team. The role partners with Marketing and Sales to translate data into actionable business decisions and automation.

You will build advanced models, apply NLP and deep learning, and continuously monitor model performance while collaborating with cross-functional teams.