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Data Science Jobs in Racine, WI (NOW HIRING)

About the Role In this role you will work with a data science team and cross-functional partners to solve business challenges and promote data-driven decision making with advanced data analysis and ...

Stay up to date with the latest trends and technologies in data science and machine learning. * Ability to work independently and collaborate as part of a team * Effective written and verbal ...

Junior Data Scientist

Milwaukee, WI · Hybrid

$68K - $93K/yr

Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, or a related field * Hands-on experience with Python and/or R - coursework, projects, internships, or personal work all ...

Bachelor's degree in Statistics, Data Science, Computer Science, Operations Research or a related field and 4 years in any job title involving statistical and machine learning modeling experience.

Senior Data Analyst

Milwaukee, WI · Hybrid

$84K - $106K/yr

Competitive Rates, Benefits, Free Daily Lunch When Onsite Role Summary This Sr. Data Analyst role involves serving as a data scientist supporting marketing efforts for our Fortune 500 client. The ...

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Data Science information

See Racine, WI salary details

$35.2K

$115.1K

$184.3K

How much do data science jobs pay per year?

As of Jun 9, 2026, the average yearly pay for data science in Racine, WI is $115,089.00, according to ZipRecruiter salary data. Most workers in this role earn between $92,400.00 and $127,500.00 per year, depending on experience, location, and employer.

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 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 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.

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 most commonly searched types of Data Science jobs in Racine, WI? The most popular types of Data Science jobs in Racine, WI are:
What are popular job titles related to Data Science jobs in Racine, WI? For Data Science jobs in Racine, WI, the most frequently searched job titles are:
What cities near Racine, WI are hiring for Data Science jobs? Cities near Racine, WI with the most Data Science job openings:
Data Scientist II (Remote)

Data Scientist II (Remote)

KOHLS

Menomonee Falls, WI • On-site, Remote

Other

Posted 3 days ago


Kohl's rating

5.8

Company rating: 5.8 out of 10

Based on 1,435 frontline employees who took The Breakroom Quiz

12th of 21 rated department stores


Job description

About the Role

In this role you will work with a data science team and cross-functional partners to solve business challenges and promote data-driven decision making with advanced data analysis and machine learning.

What You’ll Do

  • Lead exploratory data analysis to cull actionable insights

  • Collaborate with stakeholders to understand business requirements and translate them into technical solutions

  • Develop and implement statistical and machine learning models 

  • Fine-tune, optimize and ensure the scalability of models and algorithms

  • Aid in designing experiments to answer targeted questions

  • Identify and drive continuous improvement of key business metrics in an assigned business functional area

  • Drive adoption and usage of data science products and models

  • Translate data science outputs into business outcomes and value delivered

  • Mentor and guide junior data scientists, providing technical expertise and fostering a culture of continuous learning and development

  • Stay up to date on the latest trends and developments in data science and technology and identify implementation opportunities to support innovation at Kohl’s

  • Additional tasks may be assigned

Addendum

PERSONALIZATION & RECOMMENDATION SYSTEMS

Accountabilities

  • Design and support deployment of machine learning models to power personalized experiences across digital channels (e.g., homepage, PDP, cart, campaigns)

  • Build and optimize recommendation and ranking systems balancing relevance, discovery, and business objectives (e.g., conversion, revenue)

  • Develop multi-stage ranking approaches, including candidate generation and re-ranking

  • Address cold-start and long-tail challenges in large product catalogs

  • Partner with engineering to support real-time personalization and scalable deployment

Skills & Experience

  • Experience with recommendation systems, search, or ranking problems at scale of millions of customers and products

  • Experience in developing sequential, transformer models and utilizing LLM models in production

  • Understanding of collaborative filtering and learning-to-rank methods

  • Experience optimizing models for GPU / distributed training

  • Familiarity with large-scale datasets and production ML systems

  • Exposure to real-time or low-latency serving environments

  • Experience with vector search / ANN methods (e.g., FAISS, ScaNN) preferred

  • Experience with delivering end to end customized ML models in production environment

Required

  • Bachelor’s Degree in Data Science, Computer Science, Statistics, Applied Mathematics or equivalent quantitative field

  • 3+ years of progressively complex data science experience

  • Extensive experience developing and deploying state-of-the-art algorithms using machine learning, statistical and optimization methods 

  • Expert in using modern analytics tools, programming languages, and cloud platforms (Python, R, Spark, SQL, GCP, etc.)

  • Strong problem-solving skills with an emphasis on product development

  • Experience proposing rapid experiments to test the effectiveness of new strategies or initiatives and iterating quickly

  • Effective communication and collaboration skills at all levels 

Preferred

  • Master’s degree and/or Ph.D.

  • Retail and Logistics experience

  • Supply chain management

  • Marketing models


What Kohl's employees say

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Hours and flexibility

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