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How much do entry level data scientist supply chain jobs pay per year?

As of Aug 18, 2026, the average yearly pay for entry level data scientist supply chain in the United States is $165,018.00, according to ZipRecruiter salary data. Most workers in this role earn between $133,500.00 and $170,000.00 per year, depending on experience, location, and employer.

What does an entry level data scientist supply chain do?

An Entry Level Data Scientist in Supply Chain analyzes data related to inventory, logistics, procurement, and demand forecasting to help companies optimize their supply chain operations. They use statistical methods, machine learning, and data visualization tools to uncover trends, identify inefficiencies, and support decision-making. Their work often involves cleaning and preparing data, building predictive models, and communicating insights to stakeholders to improve processes and reduce costs.

What are the key skills and qualifications needed to thrive as an entry level data scientist supply chain?

To thrive as an Entry Level Data Scientist in Supply Chain, you need a solid foundation in statistics, data analysis, and supply chain fundamentals, typically supported by a degree in data science, statistics, engineering, or a related field. Proficiency in programming languages like Python or R, experience with SQL databases, and familiarity with data visualization tools such as Tableau or Power BI are commonly required. Strong problem-solving abilities, effective communication, and teamwork skills help translate data insights into actionable supply chain improvements. These skills are vital for optimizing operations, identifying cost-saving opportunities, and supporting data-driven decision-making in complex supply chain environments.

What are some typical projects an entry level data scientist supply chain might work on?

As an entry level data scientist in supply chain, you can expect to work on projects like demand forecasting, inventory optimization, and logistics network analysis. Your daily tasks may include cleaning and analyzing large datasets, building predictive models, and generating actionable insights to improve efficiency. You'll often collaborate with supply chain analysts, operations managers, and IT teams to implement your solutions and present findings to stakeholders. This collaborative environment offers valuable exposure to both technical and business aspects of supply chain management.

Can I get an entry level data scientist supply chain job with no experience?

Entry level data scientist supply chain roles typically require some knowledge of data analysis, programming (such as Python or R), and familiarity with supply chain concepts. While prior experience is not always mandatory, having relevant coursework, certifications, or internships can improve chances of securing such positions.

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What are the most commonly searched types of Data Scientist Supply Chain jobs?

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States with the most job openings for Entry Level Data Scientist Supply Chain jobs include:

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Entry Level Data Scientist Supply Chain jobs near you
Infographic showing various Entry Level Data Scientist Supply Chain job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $165,018 per year, or $79.3 per hour.

Data Scientist (Remote)

KOHLS

Menomonee Falls, WI • On-site

Other

Re-posted 29 days ago

Kohl's rating

5.8

Company rating: 5.8 out of 10

Based on 1,465 frontline employees who took The Breakroom Quiz

13th 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

  • Assist in cleaning, preprocessing and analyzing large datasets to uncover trends, patterns and correlations

  • Conduct exploratory data analysis to cull actionable insights using analytical rigor and statistical methods

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

  • Develop and implement statistical and machine learning models to solve business problems within a cross-functional team

  • Collaborate with senior data scientists to fine-tune, optimize and ensure the scalability of models and algorithms

  • Document projects, including business objectives, data gathering and processing, leading approaches, final algorithm, detailed set of results and analytical metrics

  • Identify and drive continuous improvement of key business metrics within the balanced team

  • Remain current on the latest trends and developments in data science and technology through self-learning and training opportunities

  • Additional tasks may be assigned

Addendum

DECISION SCIENCE 

Accountabilities

  • Begin to understand business challenges and their conversion into optimization problems, focusing on defining objectives and adhering to business constraints such as budget limitations and sell-through rates

  • Contribute to large-scale optimization and statistical analysis in web analytics, forecasting, supply chain management, pricing and inventory management

  • Contribute to the tuning of models by adjusting objective functions, constraints, etc.

Skills & Experience

  • Experience using commercial or open-source optimization tools such as Gurobi, Pyomo, CPLEX, etc

  • BS in Operations Research, Data Science, Computer Science, Machine Learning, Applied Mathematics, or equivalent quantitative field


 

What Skills You Have

Required

  • Experience developing state-of-the-art algorithms using machine learning, statistical and optimization methods to power various aspects of highly complex business models and deliver value

  • Experience using modern analytics tools, programming languages, and cloud platforms such as 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 iterate quickly based on results

  • Effective communication and collaboration skills

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

  • 2+ years of progressively complex data science or analytics experience

Preferred

  • Master’s degree

  • Retail experience

  • Supply chain management

  • Marketing models

  • Logistics experience

What Kohl's employees say

Pay

Benefits

Hours and flexibility

Workplace

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