Senior Data Scientist: Scale AI Solutions

General Mills; Minneapolis (MN); On-site; USD 111,000 - 185,000; Full time

Benefits offered by this job

Health benefits

Retirement plan

Time off programs

Wellbeing support

Job summary

General Mills is hiring a Senior Data Scientist to shape advanced analytics and AI solutions across the enterprise. You will partner with stakeholders and technical teams to identify opportunities, develop scalable ML models, and build reusable frameworks that standardize data science work.

This role emphasizes building and influencing—creating production-ready models while guiding teams to smarter, data-driven decisions with strong collaboration across the organization.

Qualifications

• 5+ years of experience in data science, decision sciences, or related quantitative field.
• Bachelor's degree in Data Science, Computer Science, Engineering, Mathematics, Statistics, or related field.
• Strong foundation in ML, statistics, and analytical problem-solving.
• Strong proficiency in Python and SQL.
• Experience with complex databases and modern data science tooling.
• Ability to spot data quality issues and improve solutions.
• Strong communication and stakeholder management skills.

Responsibilities

• Identify high-impact AI and ML use cases with stakeholders.
• Design, prototype, and scale analytics and AI solutions.
• Translate business problems into measurable data science opportunities.
• Build production-ready, reusable models for timely insights.
• Work with semi-structured data across data curation and model development.
• Monitor model performance and incorporate stakeholder feedback.

Skills

Data science

Python

SQL

Cloud (GCP)

Communication

Problem solving

Education

Bachelor's degree in Data Science, CS, Engineering, Maths, Statistics

Master's degree preferred

Tools

GCP

Job description

General Mills is hiring a Senior Data Scientist to shape advanced analytics and AI solutions across the enterprise. You will partner with stakeholders and technical teams to identify opportunities, develop scalable ML models, and build reusable frameworks that standardize data science work.

This role emphasizes building and influencing—creating production-ready models while guiding teams to smarter, data-driven decisions with strong collaboration across the organization.