Supply Chain Data Scientist

Conagra Brands•Chicago, IL

1d•$63,000 - $93,000•Hybrid

About The Position

Reporting to the Manager of Data Science, you will help build advanced data and analytics products using data science and machine learning to support the end‑to‑end supply chain. You will bring creativity and technical expertise to assess business challenges and enable the development of transformative data solutions. You will partner closely with team members to hypothesize, design, develop, and continuously improve data science products that advance data‑driven decision‑making and automation.

Requirements

Bachelor’s degree
One or more years of experience using Structured Query Language
One or more years of experience developing and applying operational research models, data mining applications, or advanced analytics solutions
Coding experience in Python or R programming languages with interest in learning additional languages
Experience applying machine learning techniques such as clustering, time series analysis, classification, regression, anomaly detection, simulation, and optimization
Experience drawing insights and solving problems using large and diverse data sets
Demonstrated problem solving in complex environments with innovative solutions and actionable outcomes

Nice To Haves

Experience working with large‑scale data processing tools such as Hadoop, Spark, or MapReduce is preferred

Responsibilities

Develop and deploy data mining and advanced analytics solutions to monitor, benchmark, and enhance business outcomes, including identifying improvement opportunities and performing root‑cause analysis
Develop and deploy predictive models, simulation models, and other advanced analytics solutions to support data‑driven decisions and business goals
Apply business knowledge to continuously enhance analytical models through new features and improvements
Partner with cross‑functional stakeholders to align on assumptions, identify opportunities, and deliver scalable solutions
Collaborate with peers to establish standards, promote best practices, drive solution innovation, and identify future analytical needs while staying current with industry trends
Identify relationships and trends through statistical analysis and implement machine learning techniques for prediction, forecasting, and classification
Advance analytics maturity and strengthen a data‑driven culture across the organization