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Principal Data ScientistGeneral Mills • Minneapolis, Minnesota, United States
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Principal Data Scientist

General Mills
  • US
    Minneapolis, Minnesota, United States
  • US
    Minneapolis, Minnesota, United States
Postuler Maintenant

À propos

OVERVIEW The Principal Data Scientist position is a senior technical leader who strategizes enterprise-grade AI solutions, spanning agentic AI, NLP, optimization & machine learning, to unlock measurable value across the Supply Chain and aligned domains. By working as a strategic partner to Supply Chain and cross-functional leaders in Product and Engineering, this role translates complex business requirements into rigorously framed analytical problems and robust, production-grade decisioning systems. The Principal Data Scientist shapes and governs the end-to-end AI architecture and strategic roadmap on a variety of AI platforms, ensuring AI capabilities are secure, scalable, and aligned with General Mills technology strategy. They elevate the broader Data Science community through technical mentorship and leadership in AI/ML best practices that accelerate high-quality solution delivery and responsible AI adoption.
KEY ACCOUNTABILITIES
Lead, design, and execute novel, end-to-end AI solutions and systems that help business partners achieve strategic objectives through advanced analytics, modeling, and optimization, with a primary focus on complex Supply Chain decisioning.
Partner with data science leadership, engineering, AI platform teams, and business stakeholders to define, prioritize, and deliver production-grade AI/ML products and services, leveraging best-in‑class tools, frameworks, and cloud‑native architectures on GCP.
Provide technical leadership through strong business partnership, challenging assumptions, offering alternate architectural patterns, and making informed trade‑offs between complexity, performance, cost, and long‑term maintainability.
Lead the reference architecture, design, and implementation of LLMs, NLP, and computer vision‑driven solutions, owning patterns for problem framing, data curation, model lifecycle, and integration with core enterprise platforms and applications.
Provide technical oversight across core data science methodologies—including statistical, machine learning, and optimization approaches, ensuring method selection, validation, and implementation are rigorous, fit‑for‑purpose, and consistent with AI standards.
Partner closely with AI Leadership, ML Engineering, and business stakeholders to define and evolve the architecture for agentic AI and retrieval‑augmented systems, establishing standards, guardrails, and reusable components.
Own the creation and operationalization of production‑ready, scalable AI platforms, services, and models that provide real‑time or near‑real‑time insights and decisions, fully aligned with General Mills technical standards for security, reliability, observability, and lifecycle management.
Provide technical leadership for analytical solution design and experimentation through hypothesis‑driven approaches, robust evaluation strategies, and clear error taxonomies, with strong documentation and governance to ensure transparency, reproducibility, and reuse across capabilities.
Serve as a key member of the Data Science leadership team, shaping technical strategy, multi‑year capability roadmaps, architectural standards, and operating practices that scale AI impact across the enterprise.
Coach and develop data scientists and adjacent talent through deep technical reviews and mentoring on advanced AI concepts, domain best practices, and effective use of shared platforms and patterns.
Champion Responsible AI by ensuring privacy, security, and governance compliance; proactively identifying and reducing model risks and embedding responsible AI principles into architecture, processes, and user experiences.
Act as an internal and external thought leader on AI strategy, architecture, and data science, representing the Digital and Technology organization in forums, communities of practice, and key stakeholder engagements.
MINIMUM QUALIFICATIONS
10+ years of experience in data science / applied analytics, with ownership of end‑to‑end solutions from problem framing through production and measurable business impact with at least 3 years in a Principal, Lead, or equivalent senior technical level.
Advanced degree in a quantitative field (Data Science, Computer Science, Engineering, Statistics, Math, Operations Research, or related).
Strong expertise in core data science methodologies (statistical modeling, machine learning, optimization) and their practical application to complex business problems.
Hands‑on experience architecting and deploying scalable AI/ML solutions on a major cloud platform (preferably GCP).
Proven track record of building and operating production‑grade models and decisioning systems at scale, including monitoring, performance management, and lifecycle governance.
Demonstrated technical leadership setting technical direction, establishing standards, and influencing architecture and platform decisions.
Experience leading complex AI/ML programs across multiple teams, with strong grasp of project/program management fundamentals (roadmaps, prioritization, risk/dependency management).
Experience with unstructured data and advanced AI (e.g., LLMs, NLP, computer vision) integrated into business workflows and applications.
Strong communication skills, with the ability to clearly explain analytical concepts, results, and trade‑offs to both technical and non‑technical stakeholders.
Proficiency with modern data science engineering practices: version control, code review, testing, CI/CD for models, and agile delivery.
Demonstrated ability to mentor and develop other data scientists, leading by example on modeling rigor, experimentation, and documentation.
Proven ability to stay current on evolving AI/ML technologies and to anticipate, evaluate, and advocate for appropriate adoption within the enterprise.
PREFERRED QUALIFICATIONS
Deep experience applying data science and AI to Supply Chain domains (e.g., planning, logistics, manufacturing, sourcing).
Experience leading solutions that combine traditional modeling (predictive/prescriptive analytics, optimization) with newer paradigms (LLMs, agentic AI, RAG) in production.
Exposure to large‑scale data processing and modern stack components.
Evidence of thought leadership in data science (e.g., internal forums, publications, or open‑source contributions).
ADDITIONAL CONSIDERATIONS
International relocation or international remote working arrangements (outside of the US) will not be considered.
Applicants for this position must be currently authorized to work in the United States on a full‑time basis. General Mills will not sponsor applicants for this position for work visas.
SALARY RANGE The salary range for this position is $146,900.00 - $245,000.00 / Annually. 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.
REASONABLE ACCOMMODATION REQUEST If you need to request an accommodation during the application or hiring process, please fill out our online accommodations request form by following this link:
Accommodations Request.
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  • Minneapolis, Minnesota, United States

Compétences linguistiques

  • English
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