Senior Data Scientist

Cargill is committed to providing food and agricultural solutions to nourish the world in a safe, responsible, and sustainable way. Sitting at the heart of the supply chain, we partner with farmers and customers to source, make and deliver products that are vital for living.


Our 155,000 team members innovate with purpose, providing customers with life's essentials so businesses can grow, communities prosper, and consumers live well. With over 160 years of experience as a family company, we look ahead while remaining true to our values. We put people first. We reach higher. We do the right thing-today and for generations to come.

Develop complex predictive and optimization models and forecasting solutions from conception to implementation using applied Artificial Intelligence/Machine Learning (AI/ML) platforms, such as Amazon SageMaker, SAP IBP, and a custom time series forecasting framework.


Specific job duties include:


(1) develop complex proofs of concept, minimum viable products, and fully deployable forecasting solutions within the global food supply chain domain, including regression analysis, time series models, and probabilistic models;


(2) lead the design and enhancement of new modeling features and algorithmic capabilities for designated AI/ML platforms, supporting end-to-end supply chain and demand planning optimization;


(3) provide technical leadership and thought partnership across use cases, including demand forecasting for food products such as protein, salt, cocoa, and oils across various global geographies;


(4) manage data science initiatives in food supply chain planning, including scoping, development, deployment, and monitoring of machine learning models using Machine Learning Operations (MLOps) best practices;


(5) mentor junior team members and serve as a technical resource for cross-functional stakeholders while aligning project work with strategic business objectives;


(6) collaborate with cross-functional teams including product owners, engineers, data scientists, and supply chain planners to deliver scalable, production-ready solutions;


(7) assess completeness and reliability of global supply chain data using reconciliation logic, anomaly detection, and validation frameworks;


(8) conduct data mining and audit analytics to uncover demand signals, seasonal patterns, and historical trends;


(9) apply statistical modeling, machine learning, and natural language processing (NLP) to derive insights from structured and unstructured datasets;


(10) develop and deploy forecasting and optimization models to support global demand prediction, inventory alignment, and production planning;


(11) clean, transform, and manipulate supply chain data using programming languages and statistical tools, such as Python, R, and SAS;


(12) build performance dashboards and visualizations using Tableau, Power BI, and Excel to communicate insights to technical and business stakeholders;


(13) design and implement a scalable, AI/ML-driven time series forecasting framework using AWS infrastructure, Amazon SageMaker, and sktime libraries to deliver automated and accurate forecasts;


(14) incorporate key components such as exploratory data analysis (EDA), outlier correction, stationarity testing, changepoint detection, clustering, Fourier-based seasonality analysis, preprocessing, and feature engineering;


(15) develop infrastructure for parallelized model deployment, automated retraining, and performance monitoring with integrated version control and model tracking;


(16) integrate the forecasting framework with enterprise-wide Integrated Business Planning (IBP) systems to enable dynamic infrastructure tuning or generation of standalone forecasts;


(17) follow design principles including reproducibility, modularity, measurability, scalability, discoverability, and extensibility to ensure long-term adaptability, efficiency, and trust;


(18) use Python and R prototyping languages and Java programming language. Uses the following tools and technologies: Amazon SageMaker and AWS; time series modeling and statistical libraries including sktime, prophet, ARIMA, ETS, Croston, ThetaForecaster, AutoETS, AutoARIMA, and ExponentialSmoothing; machine learning libraries including xgboost, lightgbm, scikit-learn, and optuna; distribution fitting and changepoint detection tools such as fitter, ruptures, and pwlf; advanced feature engineering using FourierFeatures, HolidayFeatures, DateTimeFeatures, and WindowSummarizer; data processing libraries including pandas, numpy, scipy, and statsmodels; visualization tools.

Full time employment, Monday - Friday, 40 hours per week, $148,700.24 per year.

At Cargill we put people first. As part of your overall rewards, we offer a comprehensive benefit program including medical and/or other benefits dependent on the position offered and hours worked. Visit: to learn more (subject to certain collective bargaining agreements for Union positions).

MINIMUM REQUIREMENTS:

This position requires a Bachelor's degree or equivalent in Data Science, Electronic Engineering, Business Analytics, or a related field, and 5 years related (progressive, post-baccalaureate) experience in a data science or data analyst related occupation.

Must also have 24 months of experience with each of the following:

• Using AI/ML, including in creating regression analysis, time series, and probabilistic models.
• Using Python and R prototyping languages and Java programming language.
• Creating data performance reporting and visualization templates using Tableau and Excel.
• Working with predictive models for supply chain solutions.
• Using forecasting timeseries, ARIMA, Prophet, or DeepAR.

Employer will accept experience gained concurrently.

Equal Opportunity Employer, including Disability/Vet.