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Intern Data Scientist Machine Learning Jobs in Wisconsin

Following the machine learning lifecycle, the data scientist should be able to convert the results into actionable product recommendations to present internally and externally. What you'll be doing:

Following the machine learning lifecycle, the data scientist should be able to convert the results into actionable product recommendations to present internally and externally. What you'll be doing:

The Data Scientist serves as a solution developer within the North America Regional Product ... Details Making an Impact Design and create experimental analytical and machine learning solutions ...

The Data Scientist serves as a solution developer within the North America Regional Product ... Details Making an Impact Design and create experimental analytical and machine learning solutions ...

The Data Scientist serves as a solution developer within the North America Regional Product ... Details Making an Impact Design and create experimental analytical and machine learning solutions ...

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

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Intern Data Scientist Machine Learning information

What does an intern data scientist machine learning do?

An Intern Data Scientist in Machine Learning assists in analyzing large datasets, building predictive models, and extracting insights to support business decisions. They often work under the guidance of experienced data scientists to clean data, implement machine learning algorithms, and evaluate model performance. Their responsibilities may also include data visualization and reporting findings to team members. This role provides hands-on experience with real-world data science problems and tools, helping interns develop essential technical and analytical skills.

What types of projects and responsibilities can an intern data scientist machine learning expect to work on?

As an Intern Data Scientist focused on Machine Learning, you will often assist in tasks such as data cleaning, feature engineering, and developing or testing machine learning models under the supervision of senior team members. You may also be involved in exploratory data analysis and help interpret model results to provide actionable insights. Interns typically collaborate closely with data engineers, analysts, and software developers, gaining exposure to end-to-end machine learning pipelines. This hands-on experience provides valuable learning opportunities and helps build the foundational skills needed for future roles in data science.

What are the key skills and qualifications needed to thrive as an intern data scientist machine learning, and why are they important?

To thrive as an Intern Data Scientist (Machine Learning), you need a solid understanding of statistics, programming skills (typically in Python or R), and foundational knowledge of machine learning algorithms, often supported by coursework or relevant projects. Familiarity with tools like scikit-learn, TensorFlow, Jupyter notebooks, and version control systems (e.g., Git) is commonly expected. Strong analytical thinking, curiosity, and effective communication skills help you interpret data insights and work collaboratively within a team. These abilities are crucial for translating data into actionable solutions and contributing to impactful machine learning projects.

What is the difference between Intern Data Scientist Machine Learning vs Intern Data Analyst?

AspectIntern Data Scientist Machine LearningIntern Data Analyst
Required SkillsBasic programming, statistics, machine learning conceptsData analysis, Excel, SQL, visualization tools
Work EnvironmentResearch-focused, model development, algorithm testingData cleaning, reporting, dashboard creation
Common Industry UsageTech, finance, healthcareRetail, marketing, finance

Intern Data Scientist Machine Learning roles focus on developing and testing machine learning models, requiring knowledge of algorithms and programming. Intern Data Analyst positions emphasize data cleaning, analysis, and visualization. Both roles are entry-level but differ in technical depth and project focus, catering to different career paths within data-driven industries.

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:

What are popular job titles related to Intern Data Scientist Machine Learning jobs in Wisconsin?

For Intern Data Scientist Machine Learning jobs in Wisconsin, the most frequently searched job titles are:

What job categories do people searching Intern Data Scientist Machine Learning jobs in Wisconsin look for?

The top searched job categories for Intern Data Scientist Machine Learning jobs in Wisconsin are:

What cities in Wisconsin are hiring for Intern Data Scientist Machine Learning jobs?

Cities in Wisconsin with the most Intern Data Scientist Machine Learning job openings:

Infographic showing various Intern Data Scientist Machine Learning job openings in Wisconsin as of July 2026, with employment types broken down into 1% As Needed, 84% Full Time, 12% Part Time, 1% Temporary, and 2% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution.

Data Scientist Lead

Milwaukee, WI • On-site

Worldpay, Inc.
Technology, Communication and Media • 10K+ employees

Full-time

Posted 15 days ago


Key responsibilities

  • Lead the design, development, validation, deployment, and monitoring of advanced analytics, machine learning, and AI solutions that drive measurable business outcomes.

  • Analyze and prepare data for modeling by assembling datasets from standard and novel data sources, and incorporate them into analytical solutions.

  • Communicate analytical findings through presentations, dashboards, visualizations, and self-service tools to support decision-making.


Job description

Job Description

Are you curious, motivated, and forward-thinking? At FIS you'll have the opportunity to work on some of the most challenging and relevant issues in financial services and technology. Our talented people empower us, and we believe in being part of a team that is open, collaborative, entrepreneurial, passionate and above all fun.

About the role:

As a key member of the Data Science team, the data scientist will deploy data-driven exploratory analysis as well as predictive models and AI solutions to solve business problems across the financial services industry, particularly in Risk, Fraud, Marketing, and Portfolio Management. Following the machine learning lifecycle, the data scientist should be able to convert the results into actionable product recommendations to present internally and externally.

What you'll be doing:

  • Lead the design, development, validation, deployment, and monitoring of advanced analytics, machine learning, and AI solutions that drive measurable business outcomes.
  • Leverage expertise in data structures and algorithms to analyze and prepare data for modeling, assembling datasets from both standard and novel data sources and incorporate them into end-to-end analytical solutions.
  • Apply advanced machine learning, predictive analytics, natural language processing (NLP), and emerging AI techniques (GenAI, Agentic etc.) to solve complex business problems across the payments and financial services ecosystem.
  • Design and execute experiments, hypothesis testing frameworks, and statistical analyses to evaluate business strategies, product enhancements, and operational improvements.
  • Establish and promote best practices in data science, machine learning, feature engineering, experimentation, model governance, and MLOps throughout the organization.
  • Communicate complex analytical findings through compelling storytelling, executive-ready presentations, dashboards, visualizations and self-service analytics tools. that drive informed decision-making.
  • Stay current on industry trends in machine learning, AI, Generative AI, and financial services analytics; bring relevant innovations to the team.

What you bring:

  • Master's degree or higher in Mathematics, Computer Science, Engineering, Operations Research, Statistics, or a related quantitative discipline.
  • 5+ years of experience developing and deploying end-to-end machine learning, predictive analytics, and data science solutions within the Payments, Banking, or Financial Services industry.
  • Strong proficiency in Python and SQL; experience with big data technologies such as Spark, PySpark, a plus.
  • Hands-on experience with data wrangling, feature engineering, and model development using libraries such as Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, or Plotly.
  • Demonstrated experience building and deploying machine learning models in a production or near-production environment.
  • Proficiency with data visualization and business intelligence tools (e.g., Tableau or equivalent).
  • Strong analytical thinking and problem-solving skills; ability to translate ambiguous business problems into rigorous analytical frameworks.
  • Ability to work collaboratively across product, engineering, and business teams.

Nice to have:

  • Experience within the Payments, Banking, or Financial Services industry.
  • Hands-on experience with the Databricks platform, including MLflow, Model Registry, collaborative notebooks, and MLOps workflows.
  • Experience deploying cloud-native machine learning solutions, particularly within AWS environments.
  • Working familiarity with emerging advancements in Transformer Models and Agentic AI technologies.
  • Knowledge of model governance, regulatory compliance, and MLOps best practices within regulated financial services environments.

What we offer you:

A career at FIS is more than just a job. It's the chance to shape the future of fintech. At FIS, we offer you:

  • A voice in the future of fintech
  • Always-on learning and development
  • Collaborative work environment
  • Opportunities to give back
  • Competitive salary and benefits


Privacy Statement

FIS is committed to protecting the privacy and security of all personal information that we process in order to provide services to our clients. For specific information on how FIS protects personal information online, please see the Online Privacy Notice.

EEOC Statement

FIS is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, marital status, genetic information, national origin, disability, veteran status, and other protected characteristics. The EEO is the Law poster is available here supplement document available here


For positions located in the US, the following conditions apply. If you are made a conditional offer of employment, you will be required to undergo a drug test. ADA Disclaimer: In developing this job description care was taken to include all competencies needed to successfully perform in this position. However, for Americans with Disabilities Act (ADA) purposes, the essential functions of the job may or may not have been described for purposes of ADA reasonable accommodation. All reasonable accommodation requests will be reviewed and evaluated on a case-by-case basis.

Sourcing Model

Recruitment at FIS works primarily on a direct sourcing model; a relatively small portion of our hiring is through recruitment agencies. FIS does not accept resumes from recruitment agencies which are not on the preferred supplier list and is not responsible for any related fees for resumes submitted to job postings, our employees, or any other part of our company.

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