
10,000+ employees
Founded 1928
💼 Consulting
📣 Marketing
📦 Logistics
Consulting • Marketing • Logistics
General Mills is a leading global food company that has been dedicated to making food for over 150 years. They create a wide array of beloved products, ranging from cereals and snacks to meals that bring joy to families around the world. Driven by a commitment to sustainability and community impact, General Mills focuses on responsible sourcing and healthful food offerings, ensuring that they uphold high standards in food safety and ethics.
🕒 October 9
❄️ Minnesota – Remote
💵 $110.8k - $184.8k / year
⏰ Full Time
🟠 Senior
📊 Data Scientist
🦅 H1B Visa Sponsor
👻 Ghost score 0%
Where this job comes from
Posted on General Mills's careers page, not through a job board
First seen by Remote Rocketship October 9

10,000+ employees
Founded 1928
💼 Consulting
📣 Marketing
📦 Logistics
Consulting • Marketing • Logistics
General Mills is a leading global food company that has been dedicated to making food for over 150 years. They create a wide array of beloved products, ranging from cereals and snacks to meals that bring joy to families around the world. Driven by a commitment to sustainability and community impact, General Mills focuses on responsible sourcing and healthful food offerings, ensuring that they uphold high standards in food safety and ethics.
• Work on challenging, large-scale data science projects with significant business impact • Challenge historical assumptions and translate business needs into analytical solutions • Apply optimization, simulation, machine learning, and information retrieval expertise to design, prototype, and build advanced analytics engines and services • Collaborate with product owners, business partners, AI engineers, and other Data Scientists • Define technical problem statements, test hypotheses, and develop efficient, accurate analytical models • Develop optimization models using leading tools and technologies • Provide data science leadership within a product team • Design and develop modeling frameworks and architectures for deterministic and combinatorial optimization, advanced machine learning, and complex business problems • Build production-ready, reusable, large-scale or scalable operations research models delivering real-time insights • Evaluate emerging technologies and recommend solutions balancing effort, quality, scalability, and business value • Monitor and improve model performance, optimality, recommendation quality, and relevant metrics • Frame business needs as statistical or analytical problems and develop practical solutions • Communicate the rationale behind recommendations to build trust in analytical solutions • Advocate for and educate others on data-driven decision-making and analytical methods • Identify and develop long-term data science processes, frameworks, tools, and standards • Collaborate with other Data Scientists through shared problem-solving and feedback
• 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 • Solid understanding of complex databases • Experience with large-scale optimization, machine learning, and data visualization • Strong quantitative and analytical judgment • Ability to identify questionable data, investigate root causes, and develop practical solutions • Strong communication and collaboration skills • Ability to understand customer needs and deliver solutions aligned with business priorities • Applicants must be currently authorized to work in the U.S. • General Mills will not sponsor applicants for a U.S. work visa • Minimum age qualification of 18 years
• Health benefits • Retirement and financial wellbeing benefits • Time off programs • Wellbeing support and perks • Eligibility to participate in an annual incentive program, depending on individual and organizational performance
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