ZipRecruiter

Log In

1

Temporary Data Scientist Machine Learning Jobs in Michigan

Stellantis

Data Scientist

Auburn Hills, MI · On-site

Your mission is to build and scale trusted data science products that power commercial ... machine learning use cases. * Develop defensible, well-documented methodologies that stand up to ...

Stellantis

Data Scientist

Auburn Hills, MI

Data scientists work closely with data engineers, analysts, and business teams to design analytics ... Strong grasp of machine learning algorithms like: * Regression (linear, logistic) * Causal ...

Auto-Owners Insurance

Data Scientist

Lansing, MI · On-site

Use statistical and machine learning techniques to build models that address business needs and ... Experience with common data science toolkits, such as R, Python, TensorFlow, SQL, Scikit-learn ...

Conclusive Marketing

Data Scientist

Detroit, MI

Data Scientist Role Summary OneMagnify's Data Scientists sit at the intersection of client strategy ... Use Databricks for large-scale data processing and machine learning workflows Translate ...

OneMagnify

Data Scientist

Detroit, MI · On-site

Data Scientist Role Summary OneMagnify's Data Scientists sit at the intersection of client strategy ... Use Databricks for large-scale data processing and machine learning workflows Translate ...

OneMagnify

Lead Data Scientist

Detroit, MI · On-site

You'll be joining our RXA Data Science team, a group dedicated to leveraging advanced analytics, predictive modeling, and machine learning to drive smarter marketing and business decisions. As Lead ...

Conclusive Marketing

Lead Data Scientist

Detroit, MI · On-site

You'll be joining our RXA Data Science team, a group dedicated to leveraging advanced analytics, predictive modeling, and machine learning to drive smarter marketing and business decisions. As Lead ...

FastTek

Machine Learning Engineer

Dearborn, MI

Machine Learning Engineer #1058742 Position Description: We are seeking an experienced AI Engineer ... This role combines expertise in Data Science, Software Engineering, and MLOps to deliver scalable ...

Domino's Pizza

Data Scientist

Ann Arbor, MI · On-site

Develops and implements basic statistical models and machine learning algorithms to address defined ... Qualifications Years Of Experience * 1 to 3 years of professional experience in data science or ...

Saanvi Technologies

Machine Learning Engineer 3

Dearborn, MI · On-site

$105K - $126K/yr

Machine Learning Engineering Engineer 3 Dearborn, MI W2 Position Description: We are seeking an ... This role combines expertise in Data Science, Software Engineering, and MLOps to deliver scalable ...

previous page

Showing results 41-60

All JobsData Scientist Machine Learning JobsTemporary Data Scientist Machine Learning JobsTemporary Data Scientist Machine Learning Jobs in Michigan

Temporary Data Scientist Machine Learning information

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

A Temporary Data Scientist specializing in Machine Learning is responsible for designing, building, and deploying machine learning models to analyze data and generate insights, but works on a contract or short-term basis. Their duties often include data preprocessing, model selection and validation, and communicating results to stakeholders. They may also be tasked with automating processes, cleaning large datasets, and collaborating with other teams to implement solutions. The temporary nature of the job means they often focus on specific projects or provide support during peak periods.

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

To thrive as a Temporary Data Scientist Machine Learning, you generally need a strong background in statistics, programming (Python or R), and experience with machine learning algorithms, often supported by a degree in computer science, mathematics, or a related field. Familiarity with data visualization tools (like Tableau), machine learning libraries (such as scikit-learn, TensorFlow, or PyTorch), and version control systems (e.g., Git) is typically required. Strong problem-solving abilities, adaptability, and effective communication are crucial soft skills for collaborating with teams and translating technical findings to stakeholders. These skills ensure that temporary data scientists can quickly contribute actionable insights, drive data-driven decisions, and add value within a limited time frame.

What are some typical projects or tasks a temporary data scientist specializing in machine learning might work on?

As a temporary Data Scientist focusing on machine learning, you can expect to work on short-term, high-impact projects such as building predictive models, cleaning and preparing data, or developing automated analytics solutions. You may be brought in to support ongoing initiatives, provide expertise for a specific project phase, or help accelerate a backlog of tasks. Collaboration is common, and you'll likely work closely with data engineers, business analysts, and domain experts to understand requirements and deliver actionable insights within tight deadlines. This role offers exposure to diverse datasets and tools, and is an excellent opportunity to rapidly expand your experience and network.

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

AspectTemporary Data Scientist Machine LearningTemporary Data Analyst
Required CredentialsBachelor's/Master's in Data Science, Computer Science, or related fields; knowledge of ML algorithmsBachelor's in Statistics, Mathematics, or related fields; proficiency in data analysis tools
Work EnvironmentProject-based, collaborative teams, tech-focused companiesBusiness units, reporting teams, data-driven departments
Employer & Industry UsageTech firms, finance, healthcare, e-commerceRetail, marketing, finance, consulting

Temporary Data Scientist Machine Learning roles focus on developing and deploying machine learning models, requiring advanced analytics skills. Temporary Data Analysts primarily interpret data, generate reports, and support decision-making. While both roles involve data handling, Data Scientists with ML expertise work on predictive modeling, whereas Data Analysts focus on descriptive analytics. The choice depends on the project needs and skill requirements.

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

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

What are popular job titles related to Temporary Data Scientist Machine Learning jobs in Michigan?

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

What job categories do people searching Temporary Data Scientist Machine Learning jobs in Michigan look for?

The top searched job categories for Temporary Data Scientist Machine Learning jobs in Michigan are:

What cities in Michigan are hiring for Temporary Data Scientist Machine Learning jobs?

Cities in Michigan with the most Temporary Data Scientist Machine Learning job openings:

Temporary Data Scientist Machine Learning jobs near you
Infographic showing various Temporary Data Scientist Machine Learning job openings in Michigan as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 12% Part Time, 4% Contract, and 1% Nights. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution.

Data Scientist

Stellantis

Auburn Hills, MI • On-site

Full-time

Re-posted 11 days ago

Stellantis rating

7.5

Company rating: 7.5 out of 10

Based on 131 frontline employees who took The Breakroom Quiz

14th of 45 rated automakers

Job description

Job Overview:
The Commercial Analytics group is looking for a Data Scientist to join our team. Your mission is to build and scale trusted data science products that power commercial recommendations while promoting data science best practices, actionable outputs and a high bar for model quality and reliability.
Data scientists work closely with data engineers, analysts, and business teams to design analytics solutions, implement advanced algorithms and evaluate the performance of use cases. Ideal candidates are self-motivated, inquisitive and creative, with a strong desire to solve real-world problems using data.
In this role, you will:
  • Collaborate with business stakeholders to identify high-impact opportunities for statistical and machine learning use cases.
  • Develop defensible, well-documented methodologies that stand up to executive scrutiny and support strategic decision-making.
  • Communicate complex results clearly to both technical and non-technical audiences.
  • Partner with data engineers to define and source relevant data features for modeling as well as drive adoption and a deep understanding of proper data usage.
  • Develop and validate models using techniques such as regression, optimization (linear programming, dynamic programming, etc.), gradient boosting, dimensionality reduction and neural networks.
  • Communicate findings and recommendations to non-technical audiences through clear visualizations and storytelling.
  • Contribute to the maintenance of models in production environments, ensuring scalability and performance.
  • Conduct peer code reviews and support best practices in model development and deployment.
  • Collaborate with both external and internal resources to support business requirements and key KPI measurement

Basic Qualifications:
  • Bachelor's degree in a quantitative discipline (e.g., Operations Research, Applied Mathematics, Optimization, Data Science or other quantitative field)
  • Automotive experience
  • Minimum of 5 years of experience in operations research, systems engineering, data science, or a related field
  • Proficiency in Python and SQL
  • Hands-on experience with big data and cloud platforms such as Databricks, Snowflake or Spark
  • Exposure to MLOps best practices, including model versioning, monitoring, and deployment pipelines
  • Strong grasp of mathematical concepts like:
    • Regression (linear, logistic)
    • Linear & Non-Linear Programming
    • Network Flow Models
    • Dynamic Programming
    • Simulation
    • Stochastic Optimization
    • Tree-based models (Random Forest, XGBoost, LightGBM)
    • Neural networks
    • Clustering and dimensionality reduction (e.g., LDA, PCA, Dynamic Time Warping)
  • Experience communicating optimization tradeoff and recommendations to executive stakeholders

What Stellantis employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

Apply