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Data Science Engineer Jobs in Minnesota (NOW HIRING)

Stakeholder & Business Partnership Partner closely with Product, Engineering, and Business stakeholders to shape problem definitions, prioritise initiatives, and drive adoption of data science ...

Data Science Tutor

Edina, MN · Remote

$18 - $40/hr

What We Look For In a Data Science Tutor * Advanced Subject Mastery: Deep knowledge of statistical ... programming, hypothesis testing, and communication of data-driven insights. Ability to explain ...

Data Science Tutor

Saint Paul, MN · Remote

$18 - $40/hr

What We Look For In a Data Science Tutor * Advanced Subject Mastery: Deep knowledge of statistical ... programming, hypothesis testing, and communication of data-driven insights. Ability to explain ...

Data Science Tutor

Minneapolis, MN · Remote

$18 - $40/hr

What We Look For In a Data Science Tutor * Advanced Subject Mastery: Deep knowledge of statistical ... programming, hypothesis testing, and communication of data-driven insights. Ability to explain ...

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 ...

Senior Data Scientist

Saint Paul, MN · On-site

$128K - $153K/yr

Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, or related quantitative field. * At least 5 plus years of experience in data science or related discipline. * Experience ...

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Data Science Engineer information

See Minnesota salary details

$43.6K

$127K

$173.8K

How much do data science engineer jobs pay per year?

As of Jul 5, 2026, the average yearly pay for data science engineer in Minnesota is $127,046.00, according to ZipRecruiter salary data. Most workers in this role earn between $112,100.00 and $134,700.00 per year, depending on experience, location, and employer.

What engineers make 500,000?

Senior data science engineers, machine learning engineers, and software engineers with extensive experience and advanced skills in areas like AI, big data, and cloud computing can earn salaries of $500,000 or more, especially in high-cost-of-living regions or within top tech companies. Achieving this level often requires advanced degrees, certifications, and a strong track record of impactful projects.

Is 30 too late for data science?

Data Science Engineers can enter the field at any age, including 30, as success depends on skills, experience, and continuous learning. Many professionals transition into data science later in their careers by acquiring relevant knowledge in programming, statistics, and tools like Python or R. Age is less important than demonstrated expertise and the ability to adapt to evolving technologies.

What are the key skills and qualifications needed to thrive in the Data Science Engineer position, and why are they important?

A Data Science Engineer should have a strong background in statistics, machine learning, programming (typically Python or R), and data engineering, often supported by a degree in computer science, engineering, or a related field. Familiarity with data processing frameworks (like Spark or Hadoop), cloud platforms (AWS, GCP, or Azure), and certifications in data science or cloud technology are highly valued. Excellent problem-solving skills, communication abilities, and collaboration are essential soft skills for working effectively in cross-functional teams. These competencies enable Data Science Engineers to build scalable data solutions, deliver actionable insights, and drive business impact.

What are the typical daily responsibilities of a Data Science Engineer?

Data Science Engineers typically spend their days designing and building data pipelines, preparing and cleaning large datasets, and developing machine learning models to solve business problems. They work closely with data scientists, software engineers, and business stakeholders to translate requirements into scalable technical solutions. Responsibilities also include deploying models to production, monitoring their performance, and iterating on solutions based on feedback. This role offers a dynamic mix of coding, data analysis, and teamwork, making each day varied and intellectually engaging.

What is a Data Science Engineer job?

A Data Science Engineer is a professional who bridges the gap between data science and software engineering. They focus on designing, building, and maintaining scalable data pipelines, infrastructure, and machine learning models for production use. Their role involves data preprocessing, model deployment, performance optimization, and integrating AI solutions into applications. They work closely with data scientists, software engineers, and DevOps teams to ensure efficient data workflows.

What does a data science engineer do?

A data science engineer designs, develops, and maintains data pipelines and infrastructure to support data analysis and machine learning models. They work with large datasets, use programming languages like Python or Scala, and often collaborate with data scientists and software engineers to ensure data quality and accessibility.

Is data science high paying?

Data science engineers typically earn high salaries due to their specialized skills in statistical analysis, programming, and machine learning. Salaries vary by experience, location, and industry, but data science roles are generally considered well-compensated within the tech field.
What are popular job titles related to Data Science Engineer jobs in Minnesota? For Data Science Engineer jobs in Minnesota, the most frequently searched job titles are:
What cities in Minnesota are hiring for Data Science Engineer jobs? Cities in Minnesota with the most Data Science Engineer job openings:
Infographic showing various Data Science Engineer job openings in Minnesota as of June 2026, with employment types broken down into 1% As Needed, 98% Full Time, and 1% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $127,046 per year, or $61.1 per hour.
Data Science Manager

Data Science Manager

Unilever

Minneapolis, MN • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 5 days ago


Unilever rating

7.4

Company rating: 7.4 out of 10

Based on 54 frontline employees who took The Breakroom Quiz

146th of 389 rated food and drinks producers


Job description

Data Science Manager
Unilever is one of the world's leading suppliers of Food, Home, and Personal Care products, operating in over 190 countries and reaching more than 2 billion consumers every day. Our portfolio includes iconic brands such as Dove, Knorr, Domestos, Hellmann's, Persil, Cif, Tresemme, Rexona, and Axe.
Guided by our purpose-to make sustainable living commonplace-we aim to grow our business while addressing the challenges of climate change and human development, enabling people everywhere to live well within the limits of the planet.
About Global Data & Technology (GDT)
Data Foundation within GDT exists to make Unilever data-intelligent, empowering critical business decisions through data, advanced analytics, and AI.
Our vision is a future-fit Unilever-an organisation where data underpins decision-making across all functions: accelerating innovation, strengthening brands, driving excellence in customer execution, enhancing consumer experiences through personalisation, and transforming internal operations for efficiency and scale.
About the Role
As a Data Science Manager within the Customer Development XOPS team in Data Foundation, you will be responsible for leading data science delivery across products and markets, shaping technical direction, and developing high-performing teams. This is a hands-on leadership role, combining technical depth with people management and strong business partnership.
You will own the end-to-end data science lifecycle for key initiatives-translating business problems into scalable analytics solutions, ensuring high-quality delivery, and embedding data science into decision-making at scale.
Key Responsibilities
Technical & Product Leadership
Own and shape the data science roadmap for products within our Customer Development (CD) portfolio, aligned to commercial priorities and business outcomes.
Lead the design, development, and deployment of diagnostic, predictive, and prescriptive analytics solutions, ensuring robustness, scalability, and interpretability.
Set modelling and analytical standards across the team, covering classical statistical methods, machine learning, and emerging Generative and Agentic AI capabilities.
Ensure solutions are industrialised and production-ready, leveraging cloud platforms (Azure, Databricks) and aligned with MLOps best practices.
People Leadership & Capability Building
Lead, coach, and develop a team of data scientists, supporting both technical growth and career progression.
Foster a culture of high ownership, continuous improvement, and learning, encouraging experimentation while maintaining delivery discipline.
Provide technical guidance and review, acting as a mentor and escalation point for complex analytical challenges.
Stakeholder & Business Partnership
Partner closely with Product, Engineering, and Business stakeholders to shape problem definitions, prioritise initiatives, and drive adoption of data science solutions.
Translate complex analytics into clear, actionable insights for senior stakeholders, supporting data-driven decision-making.
Balance short-term delivery with long-term capability building, ensuring alignment between local market needs and global product strategy.
Ways of Working & Continuous Improvement
Champion agile ways of working, enabling fast-paced experimentation and iterative delivery.
Identify opportunities to build reusable frameworks, scale solutions, and feed innovation into the global CD product pipeline.
About You
Experience & Expertise
Degree qualified in a relevant technical discipline (Data Science, Computer Science, Engineering, Mathematics, Statistics, Econometrics, or similar).
Proven experience leading data science teams working on complex analytics initiatives in a commercial or product-driven environment.
Strong background in data science modelling, with deep expertise in several of the following:
o Econometric modelling (Regression, Bayesian approaches) especially in the context of pricing and promotions in the CPG/Retail space
o Time series forecasting
o Simulation and optimisation tools
Experience designing and industrialising machine learning solutions at scale; exposure to agent-based AI systems is a strong advantage.
Advanced proficiency in Python, Spark, and modern analytics stacks; hands-on experience with Databricks and Azure.
Leadership & Mindset
Strong people leader with experience coaching and developing talent in multidisciplinary teams.
Excellent communicator, able to influence and align senior stakeholders through clear storytelling and data-driven narratives.
Strategic thinker with the ability to prioritise effectively and focus teams on what delivers the most value.
Comfortable operating in a fast-paced, global, and ambiguous environment, balancing delivery with longer-term vision.
High ethical standards in data usage, governance, and decision-making.
What We Offer
A leadership role shaping high-impact, global data science products.
The opportunity to build and lead teams working on cutting-edge analytics, AI, and cloud technologies.
A collaborative, inclusive culture that values ownership, learning, and innovation.
Direct contribution to Unilever's growth ambitions and sustainability purpose.

Pay: The pay range for this position is $103,000 to $154,400. Unilever takes into consideration a wide range of factors that are utilized in making compensation decisions including, but not limited to, skill sets, experience and training, licensure and certifications, qualifications and education, and other business and organizational needs.

Bonus: This position is bonus eligible.

Long-Term Incentive (LTI): This position is LTI eligible.

Benefits: Unilever employees are eligible to participate in our benefits plan. Should the employee choose to participate, they can choose from a range of benefits to include, but is not limited to, health insurance

(including prescription drug, dental, and vision coverage), retirement savings benefits, life insurance and disability benefits, parental leave, sick leave, paid vacation and holidays, as well as access to numerous voluntary benefits. Any coverages for health insurance and retirement benefits will be in accordance with the terms and conditions of the applicable plans and associated governing plan documents.

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At Unilever, inclusion is at the heart of everything we do. We welcome applicants from all walks of life and are committed to creating an environment where everyone can thrive/succeed. All applicants will receive fair and respectful consideration, and we actively support the growth and development of every employee.
Unilever is an Equal Opportunity Employer/Protected Veterans/Persons with Disabilities.
For more information on your federal rights, please seeKnow Your Rights: Workplace Discrimination is Illegal

Employment is subject to verification of pre-screening tests, which may include drug screening, background check, credit check and DMV check.

If you are an individual with a disability in need of assistance at any time during our recruitment process, please contact us atNA.Accommodations@unilever.com. Please note: This email is reserved for individuals with disabilities in need of assistance and is not a means of inquiry about positions or application statuses. The Protected Veterans or Individuals with Disabilities AAP narratives are available for inspection by any employee or applicant for employment Monday through Friday during normal business hours at establishment.


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