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Scientific Machine Learning Jobs in Minnesota (NOW HIRING)

General Mills

Senior Data Scientist

Minneapolis, MN · On-site +1

$108K - $181K/yr

Strong foundation in machine learning, statistical thinking, and/or analytical problem-solving ... Master's degree in Data Science, Computer Science, Engineering, Mathematics, Statistics, or a ...

New

General Mills

Senior Data Scientist

Minneapolis, MN · On-site

$108K - $181K/yr

Strong foundation in machine learning, statistical thinking, and/or analytical problem-solving ... Master's degree in Data Science, Computer Science, Engineering, Mathematics, Statistics, or a ...

New

Tactile Medical

Data Scientist

Minneapolis, MN · On-site

The Marketing Data Scientist will analyze data to build predictive models that inform commercial ... machine learning and statistical methodologies • Translating validated models into forward ...

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Scientific Machine Learning information

What is scientific machine learning?

Scientific machine learning (SciML) is an interdisciplinary field that combines principles from machine learning and scientific computing to solve complex scientific and engineering problems. It involves developing algorithms and models that can learn from data and physical laws, such as differential equations, to make predictions, optimize systems, or gain insights into phenomena. SciML is widely used in areas like physics, biology, climate science, and engineering, enabling researchers to accelerate simulations and make data-driven discoveries. The field often leverages both traditional numerical methods and modern machine learning techniques, making it a rapidly evolving area of research.

What are some common challenges faced by professionals in Scientific Machine Learning, and how can they be addressed?

Professionals in Scientific Machine Learning often encounter challenges such as integrating domain-specific scientific knowledge with machine learning models, managing large and complex datasets, and ensuring that models are interpretable and physically consistent. Collaboration with domain experts and interdisciplinary teams is essential to bridge knowledge gaps and validate results. To address these challenges, it is helpful to invest time in understanding the underlying scientific principles, keep up-to-date with advancements in both machine learning and scientific fields, and utilize specialized tools and frameworks designed for scientific data.

What are the key skills and qualifications needed to thrive as a Scientific Machine Learning professional, and why are they important?

To thrive as a Scientific Machine Learning professional, you need a strong background in mathematics, statistics, programming (often Python), and domain-specific scientific knowledge, typically with a graduate degree in a STEM field. Proficiency in machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools (like NumPy, SciPy), and experience with high-performance computing are commonly required. Critical thinking, problem-solving, and collaborative communication are vital soft skills for designing experiments and interpreting complex data. These skills ensure robust, reproducible results and the ability to bridge scientific inquiry with advanced computational methods.

What is the difference between Scientific Machine Learning vs Data Scientist?

AspectScientific Machine LearningData Scientist
Required credentialsAdvanced degrees in CS, ML, or related fields; knowledge of scientific computingDegree in CS, statistics, or related fields; strong analytical skills
Work environmentResearch labs, academia, industry R&D teamsBusiness analytics, tech companies, consulting firms
Industry usageResearch, scientific computing, engineering simulationsBusiness insights, predictive modeling, data analysis

Scientific Machine Learning focuses on integrating scientific knowledge with machine learning techniques for research and engineering applications. Data Scientists analyze data to extract insights and build predictive models for business or operational purposes. While both roles require strong technical skills, Scientific Machine Learning emphasizes scientific computing and domain-specific modeling, whereas Data Scientists focus on data analysis and visualization.

What are popular job titles related to Scientific Machine Learning jobs in Minnesota? For Scientific Machine Learning jobs in Minnesota, the most frequently searched job titles are:
What job categories do people searching Scientific Machine Learning jobs in Minnesota look for? The top searched job categories for Scientific Machine Learning jobs in Minnesota are:
What cities in Minnesota are hiring for Scientific Machine Learning jobs? Cities in Minnesota with the most Scientific Machine Learning job openings:
Scientific Machine Learning jobs near you
Senior Director, Applied AI & Data Science - Hybrid in MN

Senior Director, Applied AI & Data Science - Hybrid in MN

UnitedHealth Group

Minnetonka, MN • On-site

Full-time

Retirement

Posted 3 days ago

New

UnitedHealth Group rating

7.6

Company rating: 7.6 out of 10

Based on 145 frontline employees who took The Breakroom Quiz

191st of 882 rated healthcare providers

Job description

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together.
The Senior Director, Applied AI & Data Science is a strategic business and technology leader responsible for identifying, shaping, and delivering AI-driven transformations across the Medicare & Retirement business. This role partners closely with senior business leaders to uncover high-impact opportunities where AI [Data Science, Machine learning and Agentic AI ] can materially improve cost, operational efficiency, and member outcomes, and to translate those opportunities into funded, enterprise-scale initiatives.
This leader curates and prioritizes a portfolio of AI use cases, defines clear value propositions and success metrics, and builds executive-ready proposals grounded in business value, risk, and feasibility. The role requires strong executive presence to influence investment decisions, secure funding, and champion initiatives from concept through approval and scaled delivery.
This position follows a hybrid schedule with four in-office days per week.
Primary Responsibilities:
  • Define and lead AI/ML strategy for Medicare & Retirement, driving end-to-end AI enabled software delivery aligned to business transformation with measurable outcomes
  • Partners with business leaders to identify and shape high impact opportunities where AI can drive transformative outcomes. Develops executive ready use cases with clear value propositions and presents them to senior leadership to secure alignment and funding
  • Requires strong business acumen, executive presence, and the ability to position AI as a lever for transformation-not just technology
  • Own end-to-end lifecycle delivery of Software Engineering - from problem framing and design through development, deployment, scaling, and optimization of production grade Data Science, Machine learning and Agentic AI solutions
  • Provide end-to-end technical leadership for building AI-native applications integrating AI/ML within core application architecture, data strategy, model development, and develop core products, ensuring scalable, secure, and resilient production-ready systems
  • Embed responsible and secure AI practices across the delivery lifecycle, including governance, fairness, transparency, compliance, and adherence while adhering to enterprise security standards
  • Build and lead high performing greenfield AI engineering teams, fostering a culture of innovation, continuous learning, team mentorship and engineering excellence
  • Partner cross-functionally across product, finance, compliance, infrastructure, security, and business leaders to deliver integrated, enterprise-aligned AI solution
  • Communicate AI strategy, delivery progress, and business impact with clarity and executive presence to senior leadership and board level stakeholders

You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in.
Required Qualifications:
  • 15+ years in IT industry with Master's degree in Statistics, Computer Science, Mathematics, Data Science, or a related discipline
  • 8+ years of enterprise leadership experience defining strategy, architecture, and delivery across multiple teams
  • 4+ years in Senior Director / VP level positions, leading largescale applied AI/ML engineering organizations (30 to100+ team consisting of engineers, data scientists, and ML engineers, across shores) delivering Data Science, Machine learning and Agentic AI solutions end-to-end production software platforms
  • Proven success building and operating AI enabled software products, from full stack application development to AI/ML integrated solution with $15M+ annual budgets in healthcare or similarly regulated environments
  • Proven solid business acumen with ability to link AI investments to ROI, risk, and strategic outcomes
  • Demonstrated executive communication skills and ability to influence funding and prioritization decisions
  • Ability to work a Hybrid schedule in Minnetonka, MN

Preferred Qualifications:
  • Deep domain expertise in healthcare and regulatory frameworks
  • Solid understanding of Medicare regulatory needs including data governance, privacy, and HIPAA compliance
  • Proficiency in solving combinatorial optimization challenges through AI, and integrating them into mainstream solutions
  • Demonstrated success in scaling GenAI and enterprise-grade ML capabilities across large enterprises

Pay is based on several factors including but not limited to local labor markets, education, work experience, certifications, etc. In addition to your salary, we offer benefits such as, a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements). No matter where or when you begin a career with us, you'll find a far-reaching choice of benefits and incentives. The salary for this role will range from $159,300 - $273,200 annually based on full-time employment. We comply with all minimum wage laws as applicable.
At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone-of every race, gender, sexuality, age, location and income-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.
Diversity creates a healthier atmosphere: UnitedHealth Group is an Equal Employment Opportunity/Affirmative Action employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, national origin, protected veteran status, disability status, sexual orientation, gender identity or expression, marital status, genetic information, or any other characteristic protected by law.
UnitedHealth Group is a drug - free workplace. Candidates are required to pass a drug test before beginning employment.

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