ZipRecruiter

Log In

2

Entry Level Data Science Jobs in Michigan (NOW HIRING)

Stellantis

Data Analyst / Front End

Auburn Hills, MI · On-site

Collaborate with data engineers, data scientists, and UX partners as required​ * Apply feedback ... Self-motivated, resourceful, and organized in managing complex work Employment Type Entry Level ...

Global Educational Excellence

6-8 Science Teacher

Redford, MI · On-site

$43K - $56K/yr

Academy Principal Minimum Experience: Entry Level Purpose: Provide effective secondary school ... data to inform instructional decisions; * the ability and desire to design and utilize formative ...

KODE Labs

Operations Analyst (Entry-Level)

Detroit, MI · On-site

... Data Science, or a similarly analytical field 2. Strong analytical foundation with the ability to work through messy data and produce clear conclusions 3. Understand and/or curiosity of basic ...

Actalent

Entry Level Recruiter | Sales Trainee

Troy, MI · On-site

$195K/yr

Building trusted relationships with our network of engineering and sciences consultants under our ... data) * Performance-based incentives * Quarterly bonuses * All-expenses-paid annual trip for top ...

next page

Showing results 1-20

All JobsData Science JobsEntry Level Data Science JobsEntry Level Data Science Jobs in Michigan

Entry Level Data Science information

See Michigan salary details

$9

$16

$23

How much do entry level data science jobs pay per hour?

As of Jul 8, 2026, the average hourly pay for entry level data science in Michigan is $16.61, according to ZipRecruiter salary data. Most workers in this role earn between $14.04 and $18.65 per hour, depending on experience, location, and employer.

Is 40 too late for data science?

Entry level data science roles are open to candidates of all ages, including those starting a career at 40 or older. Success depends on acquiring relevant skills such as programming, statistics, and data analysis, often through online courses or certifications, regardless of age.

What are entry level data science jobs?

Entry level data science jobs are positions designed for individuals who are starting their careers in the field of data science, often requiring minimal professional experience. These roles typically involve working with data collection, cleaning, and analysis, as well as assisting more senior data scientists with projects. Entry level data scientists are expected to have a foundational understanding of statistics, programming (often in Python or R), and basic machine learning concepts. They may work in various industries, helping organizations gain insights from data to support decision-making.

How do I become a data scientist with no experience?

To become an entry-level data scientist with no experience, focus on building foundational skills in programming languages like Python or R, and learn data analysis and visualization tools such as SQL and Tableau. Completing online courses, working on personal projects, and participating in competitions like Kaggle can demonstrate your abilities and help you gain practical experience. Earning relevant certifications and creating a strong portfolio can improve your chances of entering the field.

What is the 80 20 rule in data science?

In data science, the 80/20 rule, also known as Pareto principle, suggests that roughly 80% of results come from 20% of the efforts or features. Entry level data scientists often focus on identifying the most impactful variables or tasks to optimize model performance and efficiency.

What types of projects or tasks can I expect to work on as an entry-level data scientist?

As an entry-level data scientist, you'll typically work on tasks such as data cleaning, exploratory data analysis, and supporting the development of predictive models. You may also assist in preparing datasets, generating reports, and visualizing data for stakeholders. Collaboration with more senior data scientists and cross-functional teams like engineering or business analysts is common, giving you opportunities to learn and grow your technical and communication skills. These foundational projects are essential for building your expertise and preparing for more complex responsibilities as you advance in your career.

What are the key skills and qualifications needed to thrive as an Entry Level Data Scientist, and why are they important?

To thrive as an Entry Level Data Scientist, you need a strong background in statistics, programming (often Python or R), and data analysis, typically supported by a relevant degree such as computer science, mathematics, or statistics. Familiarity with technical tools like SQL databases, data visualization software (e.g., Tableau), and machine learning libraries (such as scikit-learn or TensorFlow) is commonly expected. Curiosity, problem-solving ability, and effective communication help you interpret data insights and collaborate with diverse teams. These skills ensure you can extract meaningful insights from data, contribute to data-driven decision-making, and grow within the analytics field.

What is the difference between Entry Level Data Science vs Data Analyst?

AspectEntry Level Data ScienceData Analyst
Required CredentialsBachelor's in CS, Statistics, or related field; some certificationsBachelor's in Business, Statistics, or related field; certifications optional
Work EnvironmentTech companies, startups, research labsBusiness, marketing, finance sectors
Employer & Industry UsageData-driven roles in tech and researchBusiness insights, reporting, and visualization
Common Search & ComparisonYesYes

Entry Level Data Science and Data Analyst roles often share similar educational backgrounds and work environments. However, data scientists typically focus on building models and advanced analytics, while data analysts concentrate on interpreting data and creating reports. Both roles are essential in data-driven organizations, but they differ in technical complexity and scope.

Can I get a data scientist job with no experience?

Entry-level data science positions often require some knowledge of programming languages like Python or R, and familiarity with data analysis tools. While prior experience is not always mandatory, demonstrating relevant skills through projects, certifications, or coursework can improve your chances of securing an entry-level role.
What are the most commonly searched types of Data Science jobs in Michigan? The most popular types of Data Science jobs in Michigan are:
What are popular job titles related to Entry Level Data Science jobs in Michigan? For Entry Level Data Science jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Entry Level Data Science jobs in Michigan look for? The top searched job categories for Entry Level Data Science jobs in Michigan are:
What cities in Michigan are hiring for Entry Level Data Science jobs? Cities in Michigan with the most Entry Level Data Science job openings:
Entry Level Data Science jobs near you
Infographic showing various Entry Level Data Science job openings in Michigan as of July 2026, with employment types broken down into 100% Full Time. Highlights an 84% In-person, and 16% Hybrid job distribution, with an average salary of $34,541 per year, or $16.6 per hour.
Associate Data Engineer - Client Innovation Center (Entry Level)

Associate Data Engineer - Client Innovation Center (Entry Level)

IBM

Lansing, MI • On-site

$14.50 - $19/hr

Full-time

Posted 29 days ago

IBM rating

7.9

Company rating: 7.9 out of 10

Based on 75 frontline employees who took The Breakroom Quiz

105th of 204 rated software companies

Job description

Job Summary:
IBM Consulting Client Innovation Centers (CICs) are environments where technologists build real solutions for clients. The Associate Data Engineer role is entry-level, focusing on supporting the development and maintenance of data pipelines and platforms while collaborating with experienced practitioners.
Responsibilities:
• Support the development and maintenance of data pipelines used for analytics, reporting, and machine learning
• Assist with extracting, transforming, and loading (ETL/ELT) data from multiple sources into data platforms
• Contribute to data cleansing, validation, and transformation activities using Python and SQL
• Help prepare datasets for downstream consumption by analytics and data science teams
• Support batch and, where applicable, near-real-time data processing workflows under guidance
• Collaborate with data engineers, data scientists, and other team members in Agile delivery environments
• Build data engineering skills through training, mentorship, and hands-on delivery experience
• Work with functional and technical team members to help integrate data solutions into client business environments
Qualifications:
Required:
• Strong foundation in computer science fundamentals, including data structures and algorithms
• Strong analytical and problem-solving skills with attention to data quality and reliability
• Comfortable working onsite in a collaborative, team-based environment
• Ability to work effectively in a technology-driven consulting environment where tools, platforms, and client needs evolve over time
• Strong analytical and problem-solving skills, with the ability to approach complex tasks using structured, logical thinking
• Ability to learn new systems and technologies quickly and apply them in a delivery setting
• Proficiency in Python (preferred) or another programming language used for data processing
• Hands-on experience using data manipulation tools such as pandas, NumPy, and SQL, gained through coursework, labs, projects, or internships
• Ability to write clear, maintainable code for data transformation and processing tasks
• Understanding of ETL/ELT concepts and how data moves from source systems to consumption layers
• Familiarity with relational databases and SQL for querying and data manipulation
• Basic understanding of data modeling concepts such as schemas, normalization, or dimensional models
• Exposure to cloud-based data or analytics platforms (e.g., AWS, Azure, or Google Cloud) through coursework, labs, or projects
• Familiarity with core cloud data services such as object storage, databases, or analytics services
• Ability to translate business or functional requirements into technical solutions, with guidance from senior team members
• Comfortable working onsite in a collaborative, team-based environment
• Strong willingness to learn, accept feedback, and continuously improve
• Familiarity with generative AI concepts, including basic modeling approaches, responsible use, and ethical considerations, gained through coursework, projects, or self-study
Preferred:
• Master's Degree
• Exposure to distributed data processing tools such as Apache Spark or PySpark
• Familiarity with modern data warehouse technologies (e.g., Snowflake, Redshift, BigQuery)
• Exposure to streaming or event-based data concepts
• Familiarity with version control tools such as Git
• Basic awareness of how data engineering supports machine learning workflows
Company:
IBM provides technology and consulting, including software, infrastructure systems, and cloud-based solutions. Founded in 1911, the company is headquartered in Armonk, USA, with a team of 10001+ employees. The company is currently Late Stage.

What IBM employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

IBM logo

About IBM

Sourced by ZipRecruiter

At IBM, work is more than a job - it's a calling: To build. To design. To code. To consult. To think along with clients and sell. To make markets. To invent. To collaborate. Not just to do something better, but to attempt things you've never thought possible. Are you ready to lead in this new era of technology and solve some of the world's most challenging problems? If so, lets talk.

Industry

It services

Company size

10,000+ Employees

Headquarters location

Armonk, NY, US

Year founded

1911

Social media

FacebookTwitter

View All IBM Jobs

Apply