Contractual Computer Science Statistics Jobs in Michigan

We are seeking a strategic and hands-on Data Engineer to support Purchasing and Finance Analytics and Programs within our North America Data & AI team. Data engineering is the practice of making the appropriate data available to various data consumers (including data scientists, data and business analysts, citizen integrators, and line-of-business users). It is a discipline that involves collaboration across business and IT units. 

In addition to creating and maintaining an optimal pipeline architecture, typical duties and responsibilities for a Data Engineer position may include: 

The ideal candidate combines strong analytical skills with practical experience building scalable analytics, models, and data products in enterprise environments. You will be part of a talented team of data scientists, engineers, driving predictive analytics and early detection of emerging warranty trends using vast datasets across the enterprise. 

 Key Responsibilities: 

• Assembling large, complex sets of data that meet non-functional and functional business requirements 
• Design, implement, and optimize end-to-end data pipelines for ingesting, processing, and transforming large volumes of structured and unstructured data. 
• Develop robust ETL (Extract, Transform, Load) process to integrate data from various sources. 
• Identifying, designing and implementing internal process improvements including re-designing infrastructure for greater scalability, optimizing data delivery, and automating manual processes 
• Building required infrastructure for optimal extraction, transformation and loading of data from various data sources using AWS, Azure, DB2 and SQL technologies 
• Building scalable tables to provide actionable insight into key business performance metrics including operational efficiency and customer acquisition 
• Working with stakeholders including the Data Product teams to support their data infrastructure needs while assisting with data-related technical issues 
• Design and maintain data models, schemas, and database structures to support analytical and operational use cases. 
• Optimize data storage and retrieval mechanisms for performance and scalability. 
• Lead and coordinate cross-functional AI programs from concept to deployment, ensuring alignment with business goals and timelines. 
• Collaborate with other data scientists, engineers, and business stakeholders to define and prioritize program objectives. 
• Apply statistical analysis and machine learning techniques to solve business and operational problems. 
• Partner with business stakeholders to understand requirements and translate them into analytical solutions. 
• Translate business needs into actionable AI use cases and technical requirements 
• Build and deploy predictive models to forecast warranty claims, failure rates, and cost trends. 
• Ensure data quality, lineage, documentation, and compliance with governance requirements 
• Create dashboards and analytical outputs that drive insight adoption and operational impact 
• Collaborate with business data engineers, and platform teams on scalability, performance, and best practices 

Basic Qualifications

• Bachelor’s or in Data Science, Statistics, Engineering, Computer Science, or related field. 
• Minimum 3 years' experience as Data Scientist, Advanced Analyst, or similar role 
• Strong proficiency in Python, SQL, PySpark and visualization tools (e.g., Power BI, Foundry Workshop). 
• Solid understanding of statistics, exploratory data analysis, and applied machine learning. 
• Experience working with large, complex datasets in enterprise environments 
• Ability to communicate analytical findings clearly to technical and non‑technical audiences. 
• Proven experience delivering end‑to‑end analytics or data science solutions into production. 
• Experience with one or two data and cloud platforms (e.g., Palantir Foundry. Snowflake, Databricks AWS, Azure, GCP). 
• Strong communication and stakeholder engagement skills. 

Preferred Qualifications 

• Familiarity with data modeling, semantic layers, and enterprise data platforms. 
• Industry experience in automotive and manufacturing 
• Exposure to MLOps concepts, model deployment, or monitoring 
• Hands-on experience with Palantir Foundry, Snowflake Intelligence 
• Master’s degree in Data Science, Statistics, Engineering, Computer Science, or related field.