Data Scientist Careers at General Mills: Salary, Skills, Resume Tips & How to Get Hired
Data Scientists help organizations turn large volumes of data into better business decisions. From forecasting consumer demand and optimizing supply chains to building machine learning models and identifying growth opportunities, these professionals play an increasingly important role across nearly every industry.
The General Mills Data Scientist position previously featured on WorkinVirtual may no longer be accepting applications. Instead of preserving an expired listing, this guide will help you prepare for similar Data Scientist opportunities at General Mills and other companies hiring experienced analytics and machine learning professionals.
Career Snapshot
About General Mills Careers
General Mills is one of the world’s largest food manufacturers, operating globally with well-known consumer brands across breakfast cereals, snacks, baking products, yogurt, frozen meals, and pet food. The company’s Digital & Technology teams build advanced analytics platforms that help improve manufacturing, forecasting, logistics, marketing, pricing, and customer experiences.
Technology professionals commonly work alongside supply chain experts, marketers, finance teams, product managers, software engineers, and business leaders to solve large-scale analytical challenges using modern data science techniques.
Official Careers Page:
What Does a Data Scientist Do?
Data Scientists combine statistics, programming, machine learning, and business knowledge to solve real-world problems. They gather data from multiple sources, clean and prepare datasets, develop predictive models, evaluate performance, communicate findings, and help organizations make evidence-based decisions.
In large consumer brands like General Mills, projects often include:
- Demand forecasting
- Supply chain optimization
- Marketing analytics
- Pricing optimization
- Recommendation systems
- Consumer behavior analysis
- Inventory forecasting
- Production optimization
- Machine learning deployment
- Executive dashboards
Core Responsibilities
- Analyze structured and unstructured datasets.
- Develop machine learning models.
- Build predictive analytics solutions.
- Create data visualizations and dashboards.
- Engineer features for improved model performance.
- Collaborate with business stakeholders.
- Present technical findings to non-technical audiences.
- Evaluate new data sources.
- Monitor model accuracy and performance.
- Support long-term analytics strategy.
Technical Skills Employers Look For
- Python
- SQL
- R
- Machine Learning
- Statistics
- Feature Engineering
- Predictive Modeling
- Data Visualization
- Tableau
- Power BI
- BigQuery
- Snowflake
- Databricks
- Apache Spark
- TensorFlow or PyTorch
- Cloud Platforms (AWS, Azure, Google Cloud)
- Git
- Experiment Design
- A/B Testing
- Business Analytics
Recommended Certifications
- Google Professional Data Engineer
- AWS Machine Learning Specialty
- Microsoft Azure AI Engineer
- Databricks Certified Data Scientist
- TensorFlow Developer Certificate
- IBM Data Science Professional Certificate
Salary Expectations
The previous General Mills posting referenced compensation of approximately $135,000–$178,000 annually. Current salaries vary depending on experience, technical expertise, location, leadership responsibilities, and specialization.
- Junior Data Scientist: $90,000–$120,000
- Data Scientist: $120,000–$155,000
- Senior Data Scientist: $145,000–$185,000
- Lead Data Scientist: $180,000–$230,000+
Resume Tips That Help You Get Interviews
- Show measurable business impact instead of listing responsibilities.
- Highlight machine learning projects you’ve delivered.
- Include GitHub, Kaggle, or portfolio projects where appropriate.
- Mention programming languages and cloud platforms clearly.
- Quantify improvements using percentages, revenue, cost savings, or efficiency gains.
- Tailor keywords to every application.
- Show collaboration with business stakeholders.
- Keep formatting clean and ATS-friendly.
Interview Preparation
Expect interviews covering statistics, programming, business communication, and machine learning.
- Explain a machine learning project you built.
- How do you select features?
- Describe bias-variance tradeoffs.
- When would you choose XGBoost over Linear Regression?
- How do you explain analytics to executives?
- How do you evaluate model performance?
- Describe a difficult stakeholder you worked with.
- Tell us about a failed project and what you learned.
Similar Jobs to Search
- Senior Data Scientist
- Applied Data Scientist
- Machine Learning Engineer
- AI Scientist
- Decision Scientist
- Analytics Scientist
- Marketing Data Scientist
- Supply Chain Data Scientist
- Consumer Analytics Scientist
- Business Intelligence Scientist
Continue Your Data Science Job Search
If you’re preparing for your next Data Scientist opportunity, browse our latest Remote Jobs, explore Companies Hiring, search the Jobs Board, or Upload Your Resume so employers can find your profile.
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