Senior Data Scientist — AI Analytics Leader

General Mills, Inc; Minneapolis (MN); On-site; USD 108,900 - 181,700; Full time

Job summary

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

This role blends building models and influencing teams, delivering timely insights, and defining data science workflows.

Qualifications

• 5+ years in data science or related field.
• Bachelor's degree in DS, CS, Engineering, Math, Statistics, or related field.
• Strong ML foundation and analytical problem solving.
• Proficient in Python and SQL.
• Experience with complex databases and modern data science tooling.
• Excellent communication and stakeholder management.

Responsibilities

• Identify, prioritize, and deliver high-impact AI/ML use cases with stakeholders.
• Design, prototype, and scale analytics and AI solutions using best-in-class tools.
• Frame business problems as measurable data science opportunities.
• Build production-ready, reusable models delivering actionable insights.
• Work on semi-structured data: framing, curation, model development, evaluation.
• Monitor model performance and incorporate feedback for improvement.
• Define and standardize data science workflows and reusable assets.
• Communicate recommendations clearly to partners.
• Collaborate and support other data scientists to strengthen the team.

Skills

Data science experience

Python

SQL

Cloud (GCP)

Data quality

Education

Bachelor's degree (DS/CS/Engineering/Math/Stats)

Master's degree preferred

Tools

Databases

Job description

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

This role blends building models and influencing teams, delivering timely insights, and defining data science workflows.