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Senior Data Scientist Machine Learning Jobs in Wisconsin

WI · On-site

$140 - $190/hr

This role manages Senior Data Engineer resources, establishes data engineering standards, oversees ... Data Science teams in the delivery of predictive analytics, machine learning, or AI-driven ...

New

WI · On-site

$120 - $150/hr

Apply advanced statistical methods and machine learning techniques to solve complex business ... Proven experience as a Data Scientist in a fast-paced and data-driven environment. * Expertise in ...

New

Polco is hiring a Data Scientist! Polco runs some of the most important local government ... machine learning models that show communities where their key metrics are headed), and causal ...

New

Management Information Systems, Computer and Information Science, Systems Engineering, Mathematics ... Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS ...

Showing results 41-60

Senior Data Scientist Machine Learning information

See Wisconsin salary details

$91.8K

$125.1K

$147K

How much do senior data scientist machine learning jobs pay per year?

As of Sep 4, 2026, the average yearly pay for senior data scientist machine learning in Wisconsin is $125,062.00, according to ZipRecruiter salary data. Most workers in this role earn between $108,206.00 and $138,339.00 per year, depending on experience, location, and employer.

What does a senior data scientist specializing in machine learning do?

A Senior Data Scientist in Machine Learning leads the development, implementation, and optimization of advanced statistical and machine learning models to solve business problems. They analyze large, complex datasets, design predictive algorithms, and collaborate with cross-functional teams to integrate models into production systems. Additionally, they mentor junior data scientists, contribute to setting technical strategy, and often communicate findings to stakeholders to drive data-driven decision-making.

What are the key skills and qualifications needed to thrive as a senior data scientist in machine learning?

To thrive as a Senior Data Scientist in Machine Learning, you need advanced expertise in statistics, programming (Python or R), and machine learning algorithms, typically backed by a relevant degree (such as in computer science or mathematics) and several years of experience. Familiarity with tools like TensorFlow, PyTorch, scikit-learn, and cloud platforms (AWS, GCP, or Azure), as well as experience with big data technologies, is essential. Strong problem-solving, communication, and project leadership skills help drive impactful solutions and foster collaboration across teams. These skills ensure the successful design, deployment, and scaling of machine learning models that deliver business value.

How does a senior data scientist specializing in machine learning typically collaborate with cross-functional teams?

Senior Data Scientists in Machine Learning often work closely with product managers, software engineers, and business analysts to understand project goals and translate them into actionable data solutions. They are responsible for communicating complex technical concepts to non-technical stakeholders, ensuring that ML models align with business objectives. Collaboration frequently involves participating in regular strategy meetings, reviewing data pipelines with engineering teams, and providing insights that guide product development. This cross-disciplinary teamwork is essential for successfully deploying machine learning models into production environments.

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

AspectSenior Data Scientist Machine LearningData Scientist
Required CredentialsMaster's or PhD in CS, Statistics, or related field; experience with ML frameworksBachelor's or Master's in relevant field; foundational knowledge of data analysis
Work EnvironmentAdvanced analytics teams, R&D, product developmentData analysis teams, business intelligence, reporting
Employer & Industry UsageTech companies, finance, healthcare, e-commerceSimilar industries, often entry to mid-level roles

The main difference is that Senior Data Scientist Machine Learning roles require more experience, advanced skills in ML frameworks, and often involve leading projects. Data Scientists typically focus on data analysis and reporting with less emphasis on complex ML models. Senior roles also tend to involve mentorship and strategic input.

What are the most commonly searched types of Data Scientist Machine Learning jobs in Wisconsin?

The most popular types of Data Scientist Machine Learning jobs in Wisconsin are:

Infographic showing various Senior Data Scientist Machine Learning job openings in Wisconsin as of June 2026, with employment types broken down into 74% Full Time, 24% Part Time, and 2% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $125,062 per year, or $60.1 per hour.

$140 - $190/hr

Other

Medical, Dental, Vision, Retirement

Posted 3 days ago

New


Key responsibilities

  • Lead, coach, and develop Senior Data Engineers, managing workload priorities and delivery expectations.

  • Establish and maintain enterprise standards for data modeling, ETL/ELT development, and data integration patterns.

  • Oversee the design, development, testing, deployment, and optimization of data pipelines, models, and integrations.


Lake Michigan Credit Union rating

8.0

Company rating: 8.0 out of 10

Based on 28 frontline employees who took The Breakroom Quiz


Job description

Primary Location: Grand Rapids Employee Status: Full-Time Workplace Type: Hybrid

Who we are: At LMCU, you'll find more than just a job - discover a fulfilling career where your contributions truly matter. Join our talented team at Lake Michigan Credit Union and discover the difference an employer who puts people first can make in your career and life.

About this position: The Manager, Data Engineering leads the Data Engineering function responsible for designing, building, maintaining, and optimizing enterprise data models, pipelines, and integrations that support business needs and enable self-service analytics across LMCU. This role manages Senior Data Engineer resources, establishes data engineering standards, oversees delivery and operational support, and ensures data solutions are scalable, reliable, secure, and aligned to business priorities.

What you’ll do:
  • Lead, coach, and develop Senior Data Engineers, including managing workload priorities, sprint commitments, performance feedback, career development, hiring, and day-to-day delivery expectations.
  • Establish and maintain enterprise standards for data modeling, ETL/ELT development, orchestration, integration patterns, Microsoft Fabric/OneLake architecture, and reusable data products.
  • Oversee the design, development, testing, deployment, maintenance, and optimization of data pipelines, curated data models, integrations, and data migrations across core banking, digital, operational, and analytics platforms.
  • Ensure data pipelines and models are reliable, secure, performant, well-documented, and supportable through effective monitoring, data quality controls, lineage, and issue-resolution processes.
  • Partner with Business Intelligence, Data Governance, Application Development, Infrastructure, vendors, and business stakeholders to translate business needs into scalable technical solutions.
  • Enable trusted self-service analytics by delivering reliable, accessible, and well-governed data products that support reporting, analytics, and informed decision-making.
  • Adhere to and champion our core values of curious minds, collaborative hearts, and continuous excellence.
What you’ll bring:
  • 8+ years of progressive experience in data engineering, analytics engineering, data architecture, data warehousing, or data platform development, including experience leading technical resources, delivery workstreams, or project teams.
  • Bachelor’s degree in computer science, information systems, information technology, data management, data analytics, engineering, or a related field; significant relevant experience may be considered in lieu of a degree.
  • Hands‑on experience with Microsoft Fabric, OneLake, Data Factory, notebooks, lakehouse and warehouse workloads, SQL Server, and modern cloud data platforms.
  • Strong knowledge of ETL/ELT design and orchestration, dimensional modeling, star and snowflake schemas, Kimball/Inmon concepts, and medallion architecture.
  • Proficiency with SQL and Python, including data pipeline testing, observability, monitoring, and troubleshooting.
  • Experience with Azure DevOps/Git, CI/CD practices, and modern development and deployment processes.
  • Knowledge of data quality controls, metadata management, data lineage, governance, and documentation best practices.
  • Experience integrating core banking, digital banking, and other operational data sources into enterprise data platforms.
  • Experience working within Agile delivery environments, including ServiceNow or Jira intake, prioritization, and stakeholder communication.
  • Relevant certifications in Microsoft Fabric, Azure, cloud data platforms, data engineering, Agile/Scrum, leadership, or project management are preferred.
  • experience.
  • Ability to work effectively within established priorities, standards, and processes while demonstrating strong execution and follow-through.
Preferred Qualifications:
  • Experience partnering with or leading Data Science teams in the delivery of predictive analytics, machine learning, or AI-driven solutions.
  • Familiarity with the data science lifecycle, including model development, model deployment (MLOps), monitoring, and governance.
  • Experience building platforms, pipelines, and infrastructure that enable Data Scientists to develop, test, and operationalize models at scale.
  • Knowledge of modern AI, machine learning, and generative AI technologies and their integration into enterprise data platforms.
  • Demonstrated ability to bridge Data Engineering, Business Intelligence, and Data Science disciplines to deliver business outcomes.
What you’ll get:
  • All Employees: weekly pay and retirement savings options.
  • Full-Time Employees: comprehensive health coverage including medical (with prescription), dental, vision, HSA match, paid parental leave, and tuition reimbursement.

LMCU is an Equal Opportunity Employer Lake Michigan Credit Union is fundamentally different than a bank, both in our structure and mission. Our goal has always been to do what is best for our members. This approach guides every decision we make and every service we offer. As a credit union, our goal isn’t to generate profits for shareholders, it’s to improve the lives of our members. LMCU is consistently rated in the top ten in the nation for Return of the Member by Callahan & Associates, meaning we give back more to our members in the form of higher rates when they save and lower rates when they borrow. At LMCU, the emphasis on delivering real value to our members has been job one, since day one. As one of the largest credit unions in the nation for several years running, LMCU has earned many awards and accolades such as inclusion as one of the 101 Best and Brightest Companies to Work For Nationally and being recognized by Forbes as a Great Place to Work. As an employee, you'll enjoy a stimulating, professional atmosphere, supported by the latest technology, training, and development. We recognize that a healthy balance between work, home life, and play is essential to our employees and our success. We offer a friendly, respectful and inclusive work environment, fun employee activities and rewarding community involvement opportunities. You’ll love working here at Lake Michigan Credit Union!

LMCU is an Equal Opportunity Employer

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