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About This Role
COMPANY OVERVIEW
We exist to make food the world loves. But we do more than that. Our company is a place that prioritizes being a force for good, a place to expand learning, explore new perspectives and reimagine new possibilities, every day. We look for people who want to bring their best — bold thinkers with big hearts who challenge one another and grow together. Because becoming the undisputed leader in food means surrounding ourselves with people who are hungry for what’s next.
At General Mills, data helps us make better decisions and create food people love. As a Senior Data Scientist, you will work on challenging, large-scale projects with significant business impact. You will challenge historical assumptions, translate business needs into analytical solutions, and recommend new ways to drive action through data-driven insights.
In this role, you will apply expertise in optimization, simulation, machine learning, and information retrieval to design, prototype, and build next-generation advanced analytics engines and services. You will collaborate with product owners, business partners, AI engineers, and other Data Scientists to define technical problem statements, test hypotheses, and develop efficient, accurate analytical models that support business decisions.
What You’ll Do
- Partner with data science leadership, AI engineering, business partners, and product owners to develop optimization models using leading tools and technologies.
- Provide data science leadership within a product team through strong partnership, actionable insights, and strategic and tactical technical roadmap recommendations.
- Design and develop modeling frameworks and architectures for deterministic and combinatorial optimization, advanced machine learning, and other complex business problems.
- Build production-ready, reusable, large-scale or scalable operations research models that deliver real-time insights and align with General Mills technology standards.
- Evaluate emerging technologies and recommend solutions that balance effort, quality, scalability, and business value.
- Monitor and improve model performance, optimality, recommendation quality, and other relevant metrics, incorporating feedback from business partners.
- Apply critical thinking to frame business needs as statistical or analytical problems and develop solutions that balance technical completeness, complexity, and timely value delivery.
- Clearly communicate the why and how behind recommendations to build trust and understanding in analytical solutions.
- Advocate for and educate others on the value of data-driven decision-making and analytical methods.
- Identify and develop long-term data science processes, frameworks, tools, and standards.
Collaborate with other Data Scientists by asking questions, engaging in shared problem-solving, and actively soliciting and providing feedback. *
Required Qualification s
- 5+ years of experience working in data science, decision sciences, operations research, or a closely related quantitative field; 3+ years may be considered for candidates with a PhD.
- Master’s or PhD degree in operations research, industrial engineering, computer science, or a closely related quantitative field.
- Strong proficiency in SQL and Python, with a solid understanding of complex databases.
- Experience with large-scale optimization, machine learning, and data visualization.
- Strong quantitative and analytical judgment, including the ability to identify questionable data, investigate root causes, and develop practical solutions.
Strong communication and collaboration skills, with the ability to understand customer needs and deliver solutions aligned with business priorities. *
Preferred Qualifications
Applied experience in business operations, commercial functions, or a related business area.
*
Additional Considerations
- Applicants must be currently authorized to work in the U.S. General Mills will not sponsor applicants for a U.S. work visa.
SALARY RANGE
The salary range for this position is:
$110,800 - $184,800 Annual At General Mills we strive for each employee's pay at any point in their career to reflect their experiences performance and skills for their current role. The salary range for this role represents the numerous factors considered in the hiring decisions including, but not limited to, educations, skills, work experience, certifications, etc. As such, pay for the successful candidate(s) could fall anywhere within the stated range.
Beyond base salary, General Mills offers a competitive Total Rewards package focusing on your overall well-being. We are proud to offer a foundation of health benefits, retirement and financial wellbeing, time off programs, wellbeing support and perks. Benefits may vary by role, country, region, union status, and other employment status factors. You may also be eligible to participate in an annual incentive program. An incentive award, if any, depends on various factors, including, individual and organizational performance.
ELIGIBILITY
Applicants must meet a minimum 18-year age qualification.
EQUAL OPPORTUNITY EMPLOYER (EOE)
General Mills is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, creed, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, marital or familial status, status regarding public assistance, or membership or activity in a local human rights commission.
REASONABLE ACCOMMODATION REQUEST
If you need to request an accommodation during the application or hiring process, please fill out our online accommodation request form by following this link: Accommodation Request .
Salary Context
This $110K-$184K range is below the median for Data Scientist roles in our dataset (median: $161K across 138 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At General Mills, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,503 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($147K) sits 22% below the category median. Disclosed range: $110K to $184K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
General Mills AI Hiring
General Mills has 2 open AI roles right now. They're hiring across Data Scientist, MLOps Engineer. Based in Minneapolis, MN, US. Compensation range: $184K - $184K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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