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Data Science Jobs in Buffalo, MN (NOW HIRING)

Daikin Applied

Data Engineer III

Plymouth, MN · On-site

$119K - $143K/yr

Bachelor's degree in Data Analytics, Data Science, Computer Science, Applied Mathematics, Engineering, or related field * 5+ years (or 3+ years with a masters) of professional experience in analytics ...

Daikinapplied

Data Engineer III

Minneapolis, MN

$90K - $153K/yr

Bachelor's degree in Data Analytics, Data Science, Computer Science, Applied Mathematics, Engineering, or related field * 5+ years (or 3+ years with a masters) of professional experience in analytics ...

Boston Scientific

Data Product Manager

Maple Grove, MN · On-site

Boston Scientific was recognized as a Glassdoor Best Place to Work in 2026, ranking No. 15 on the ... The Data Product Manager will define and lead the vision, strategy, and roadmap for enterprise data ...

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Showing results 21-40

All JobsData Science JobsData Science Jobs in Buffalo, MN

Data Science information

See Buffalo, MN salary details

$39.5K

$129.2K

$206.9K

How much do data science jobs pay per year?

As of Sep 8, 2026, the average yearly pay for data science in Buffalo, MN is $129,218.00, according to ZipRecruiter salary data. Most workers in this role earn between $103,700.00 and $143,200.00 per year, depending on experience, location, and employer.

What is data science?

Data science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It combines skills from statistics, computer science, and domain expertise to analyze and interpret complex data sets. Data scientists work with large amounts of data to identify patterns, make predictions, and help organizations make data-driven decisions.

What does a data scientist do?

As a Data Scientist, you are qualified to work in such diverse fields as research and development, politics, advertising and marketing, technology, healthcare, government, and higher education as well as multiple others. In general, your duties and responsibilities will be to compile and analyze relevant statistics and turn those numbers into algorithms that reveal insights that can be used by other researchers in their areas of study. Data Science can reveal things like consumer buying habits or the likelihood of success for a course of action. Other duties might vary, depending on your unique field of specialty. Related areas in which a Data Scientist might wish to focus include work as a Data Analyst, Machine Learning Engineer, and Project Manager.

What are the key skills and qualifications needed to thrive as a data scientist, and why are they important?

To thrive as a Data Scientist, you need a strong background in statistics, programming (often Python or R), and data analysis, usually supported by a degree in a quantitative field. Familiarity with machine learning libraries (like scikit-learn or TensorFlow), big data tools (such as Hadoop or Spark), and data visualization platforms is typically required. Critical thinking, problem-solving, and effective communication are vital soft skills for translating complex data insights into actionable business strategies. These skills and qualities are essential for extracting value from data, driving informed decisions, and effectively collaborating with multidisciplinary teams.

What are some common challenges faced by data scientists when working with real-world datasets?

Data scientists often encounter challenges such as missing or inconsistent data, unstructured formats, and noisy information in real-world datasets. Cleaning and preprocessing data to ensure its quality can be time-consuming but is critical for building accurate models. Additionally, data scientists may work closely with domain experts and other team members to better understand the data's context and ensure their analyses align with business objectives. Overcoming these challenges requires strong problem-solving skills and effective collaboration within cross-functional teams.

What is the difference between Data Science vs Data Analyst?

AspectData ScienceData Analyst
Required skillsStatistics, programming (Python, R), machine learningData visualization, SQL, basic statistics
Work environmentDeveloping models, predictive analytics, researchReporting, data cleaning, descriptive analysis
Tools usedPython, R, Jupyter, TensorFlowExcel, SQL, Tableau, Power BI
Industry usageTech, finance, healthcare, e-commerceRetail, marketing, finance, healthcare

Data Science and Data Analyst roles often overlap but differ mainly in scope. Data Scientists focus on building predictive models and advanced analytics, requiring programming and machine learning skills. Data Analysts primarily handle data cleaning, reporting, and visualization. Both roles are essential in data-driven industries, but Data Science is more technical and research-oriented, while Data Analysis emphasizes interpreting data for business insights.

Is a data scientist in high demand?

Data scientists are in high demand across many industries due to the increasing reliance on data-driven decision making. The role requires skills in programming, statistics, and machine learning, and job growth is expected to continue as organizations expand their data capabilities.

What jobs can a data scientist do?

A data scientist can work in roles such as data analyst, machine learning engineer, data engineer, or business intelligence analyst. These roles involve analyzing large datasets, developing predictive models, and using tools like Python, R, and SQL to support decision-making across various industries.
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What cities near Buffalo, MN are hiring for Data Science jobs?

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Data Science jobs near you
Infographic showing various Data Science job openings in Buffalo, MN as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 17% Part Time, and 2% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $129,218 per year, or $62.1 per hour.

Sr. Data Science/Machine Learning Builder _ Minnetonka, MN (Day1 Onsite) _ 12+ Years Experience

StarTechs Inc.

Minnetonka, MN • On-site

Other

Posted 8 days ago

Job description

Requirement details:

Job Title: Sr. Data Science/Machine Learning Builder

Location: Minnetonka, MN (Day1 Onsite)

Duration: 12+ months

Job Description:

12+ Years of Experience

Job Description:

Client seeks a highly skilled Data Science / Machine Learning Builder to drive advanced analytics, anomaly detection, predictive modeling, and production-grade AI solutions across Claims and Payment Integrity, Customer Service, and Technology workflows.

This role emphasizes hands-on delivery of scalable, secure, and maintainable ML systems in a healthcare context, with a focus on multimodal approaches, deep learning, and integration into enterprise workflows.

The ideal candidate will possess strong technical ownership, production mindset, and the ability to translate complex business problems into robust analytical solutions.

The position is based in Minnetonka, Minnesota, with hybrid work expectations and a preference for local talent.

Roles and Responsibilities:

Data Science / Machine Learning Builder, Production AI Systems Developer, Design, build, deploy, and operate production AI, machine learning, and analytical systems with a focus on claims integrity, customer service, and technology workflows.

Develop and integrate multimodal systems combining structured data, NLP, embeddings, deep learning, and generative AI for enhanced decision-making and output explainability.

Engineer robust feature pipelines, population/target definitions, model evaluation frameworks, and scoring architectures with strong emphasis on explainability, monitoring, and drift detection.

Collaborate with MLOps, data engineering, and platform teams to ensure CI/CD, observability, security, compliance, and auditability of deployed models and services.

Own operational support for ML systems, including troubleshooting, root-cause analysis, and continuous improvement in production environments.

Apply modern software engineering practices including source control, automated testing, infrastructure as code, containers, and deployment automation.

Conduct data discovery, curation, and integration work, including diagnosing and resolving pipeline issues where necessary.

Work closely with AI/Automation and business teams to design end-to-end solutions that deliver measurable operational and business outcomes.

Demonstrate autonomy in ambiguous environments, make sound technical tradeoffs, and deliver high-velocity, reviewable work.

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