Data Scientist

Nestle Operational Services Worldwide SA•St. Louis, MO

1d•$88,000 - $123,000

About The Position

Digital Transformation is at the center of all that we do. With data-driven innovators passionate about making a difference in the lives of pets, we create cutting-edge digital business models that solve complex problems. You'll be at the forefront of digital advancements as they happen in real time, as well as ensure we remain a leader and innovator in the pet care category. Have a hand in making a lasting impact within our long-standing history. As a Data Scientist at Nestlé Purina, you'll build and deploy the machine learning and optimization models that drive real business value across functions such as Manufacturing, Marketing, Supply Chain, Finance, and Consumer Insights. At the core of this role is traditional machine learning and optimization, building the models and optimization engines that power some of our highest-value business decisions. Working closely with cross-functional teams, you'll translate business problems into scalable analytical solutions, automate insights, and support enterprise decision-making.

Requirements

Bachelor's degree from an accredited institution in Data Science, Computer Science, Statistics, Engineering, Mathematics, or a related field
3+ years of professional experience in data science, analytics, or machine learning, including developing optimization or predictive models
3+ years of experience in Python (e.g., scikit-learn, pandas, PySpark, and NumPy) and SQL for data manipulation and analysis
1+ years of experience working with cloud computing platforms (e.g., Azure, AWS, or GCP) and DevOps/CI-CD practices

Nice To Haves

Master's degree in Data Science, Computer Science, Statistics, Mathematics, or a related field is preferred
Experience with MLOps tools (e.g., MLflow, Azure ML, SageMaker, or Kubeflow) and model deployment in a production environment is preferred
Experience working with Databricks, Snowflake, or distributed data processing environments is preferred
Experience building GenAI-enabled applications such as RAG, copilots, or intelligent assistants is preferred
Familiarity with LLM orchestration frameworks (e.g., LangChain or Semantic Kernel), prompt engineering, and vector databases is preferred
Familiarity with responsible AI practices including bias detection, explainability, and governance is preferred

Responsibilities

Develop and deploy machine learning, statistical, and optimization models that solve business problems and improve operational performance
Support the development and maintenance of optimization engines that drive high-value business decisions
Collaborate with product owners, data engineers, and business stakeholders to translate business needs into scalable analytical solutions
Perform data wrangling, feature engineering, exploratory data analysis, and model evaluation using Python and SQL
Design and maintain reusable analytics workflows and automated pipelines for production-grade deployment
Support GenAI-enabled applications including retrieval augmented generation (RAG), copilots, and intelligent assistants
Contribute to AIOps/MLOps practices including model versioning, monitoring, continuous integration and continuous deployment pipelines, and responsible AI processes