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Quantitative Data Engineer Jobs in Michigan (NOW HIRING)

DTE Energy

Expert Data Scientist

Detroit, MI · On-site

D degree with emphasis on coursework of a quantitative nature (e.g., Statistics, Computer Science, Engineering, Mathematics, Physics, Data Science, Econometrics, etc.) * Advanced business acumen and ...

DTE Energy

Expert Data Scientist

Detroit, MI

D degree with emphasis on coursework of a quantitative nature (e.g., Statistics, Computer Science, Engineering, Mathematics, Physics, Data Science, Econometrics, etc.) * Advanced business acumen and ...

Tech Tammina LLC

Data Analyst

Warren, MI · On-site

Bachelor's or Master's degree in Computer Science, Applied Mathematics, Statistics, Engineering, or a related quantitative field * Minimum 5 years of experience in data analytics, business ...

Americhem

Global Sustainability Leader

Grand Rapids, MI · On-site

$109K/yr

... engineered compounds and performance technologies. Our business is built around delivering ... Strong analytical skills and ability to interpret qualitative and quantitative data. * Excellent ...

General Motors

Staff Data Scientist

Warren, MI · On-site

$184K - $233K/yr

... quantitative fields * Bachelor's degree AND Ph.D. required in a technical discipline, such as ... Advanced programming skills for analytical model development, data pipelines, and reproducible ...

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Quantitative Data Engineer information

What are the key skills and qualifications needed to thrive as a quantitative data engineer, and why are they important?

To excel as a Quantitative Data Engineer, you need strong proficiency in programming (such as Python, R, or C++), advanced mathematical and statistical knowledge, and a relevant degree in computer science, mathematics, or a related field. Experience with big data tools (like Spark, Hadoop), cloud platforms, and data pipeline systems, as well as familiarity with financial data sets, is typically required. Analytical thinking, detail orientation, and effective problem-solving skills distinguish top performers in this role. These competencies are critical for efficiently transforming complex data into actionable insights and supporting robust quantitative models in data-driven environments.

What is a quantitative data engineer?

A Quantitative Data Engineer is a professional who designs, builds, and maintains data infrastructure that supports quantitative analysis, typically in finance or technology sectors. They work closely with quantitative analysts and data scientists to ensure efficient data pipelines, data quality, and high-performance systems for processing large datasets. Their responsibilities include developing ETL processes, optimizing databases, and implementing data models to support research and trading strategies. Strong programming skills, expertise in big data technologies, and knowledge of quantitative methods are essential for this role.

What is the difference between Quantitative Data Engineer vs Data Scientist?

AspectQuantitative Data EngineerData Scientist
Primary FocusBuilding data pipelines, data infrastructure, and ensuring data qualityAnalyzing data, creating models, and deriving insights
Skills & ToolsSQL, Python, Spark, ETL processes, data architectureStatistics, machine learning, Python/R, data visualization
CredentialsComputer science, engineering, or related degrees; certifications in data engineeringStatistics, data science, or related degrees; certifications in data analysis or machine learning
Work EnvironmentData engineering teams, data infrastructure projectsData analysis teams, research, and modeling projects

While both roles work closely with data, Quantitative Data Engineers focus on building and maintaining data systems, whereas Data Scientists analyze data to generate insights and models. They often collaborate but have distinct skill sets and responsibilities within data-driven organizations.

How does a quantitative data engineer typically collaborate with data scientists and quantitative analysts on projects?

Quantitative Data Engineers work closely with data scientists and quantitative analysts to design, build, and optimize data pipelines that support complex modeling and analytics. They are often responsible for ensuring data quality, scalability, and efficient data processing, enabling analysts to focus on developing models and extracting insights. Regular collaboration includes translating analytical requirements into technical solutions, troubleshooting data issues, and iterating on data infrastructure to support evolving project needs. This teamwork fosters an environment where technical and analytical expertise complement each other, leading to more robust and actionable results.
What are popular job titles related to Quantitative Data Engineer jobs in Michigan? For Quantitative Data Engineer jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Quantitative Data Engineer jobs in Michigan look for? The top searched job categories for Quantitative Data Engineer jobs in Michigan are:
What cities in Michigan are hiring for Quantitative Data Engineer jobs? Cities in Michigan with the most Quantitative Data Engineer job openings:
Quantitative Data Engineer jobs near you
Infographic showing various Quantitative Data Engineer job openings in Michigan as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 13% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Senior Machine Learning Engineer

Ascentt

Ann Arbor, MI • On-site

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

Job Summary:
Ascentt is building cutting-edge data analytics & AI/ML solutions for global automotive and manufacturing leaders. The role involves designing, developing, and deploying scalable machine learning models for real-world business problems using structured and unstructured data.
Responsibilities:
• Design, develop, and deploy scalable machine learning models for real-world business problems using structured and unstructured data.
• Analyze large datasets using PySpark and other distributed computing frameworks to extract insights and prepare features for ML pipelines.
• Apply a wide range of statistical, machine learning, and deep learning techniques, including but not limited to regression, classification, clustering, time-series forecasting, and NLP.
• Own end-to-end ML pipelines from data ingestion, preprocessing, training, validation, tuning, and deployment.
• Utilize Amazon SageMaker or similar platforms for building, training, and deploying models in a production-grade environment.
• Collaborate closely with data engineers, data scientists, and product teams to integrate models with business workflows.
• Monitor and improve model performance, scalability, and reliability in production.
• Contribute to setting up and maintaining the ML environment and tooling (including environment configuration, CI/CD pipelines for ML, model versioning, etc.).
Qualifications:
Required:
• 7+ years of experience in machine learning, data science, or related fields.
• Strong programming skills in Python with experience in ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).
• Hands-on experience with PySpark for big data processing and model development.
• Proficient in building models on large-scale datasets (terabytes to petabytes).
• Solid understanding of statistical analysis, probability, hypothesis testing, and experimental design.
• Experience with Amazon SageMaker (or similar cloud-based ML platforms).
• Strong knowledge of ML Ops practices including version control, model monitoring, and retraining strategies.
• Familiarity with containerization (Docker) and CI/CD practices for ML projects is a plus.
• Excellent communication skills and the ability to clearly explain complex concepts to non-technical stakeholders.
Preferred:
• Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative discipline.
• Experience with workflow orchestration tools (e.g., Airflow, Kubeflow).
• Prior experience in domains like Manufacturing, finance, healthcare, or e-commerce is a plus.
Company:
Ascentt is an AI, ML and Data Science solutions provider serving enterprise customers. Founded in 2007, the company is headquartered in Plano, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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