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Senior Insurance Data Analytics Jobs in Michigan

Sr. Data Analyst-Materials Management

Farmington Hills, MI · On-site

$84K - $106K/yr

What you can look forward to as Senior Data Analyst- Materials Management: * Lead data analysis using Microsoft Access, Excel, and other tools to support corporate initiatives and datadriven decision ...

Data Analyst Senior

Royal Oak, MI · Hybrid

$80K - $101K/yr

Possesses knowledge of workflows in multiple business areas to support data development, analytics ... Optional identity theft protection, home and auto insurance * Traditional and Roth retirement ...

Data Analyst Senior

Royal Oak, MI · On-site

$80K - $101K/yr

Possesses knowledge of workflows in multiple business areas to support data development, analytics ... Optional identity theft protection, home and auto insurance * Traditional and Roth retirement ...

Industry/Sector Not Applicable Specialism Data, Analytics & AI Management Level Senior Associate & Summary The Opportunity As a Data Engineer - Senior Associate, you will focus on designing and ...

Industry/Sector Not Applicable Specialism Data, Analytics & AI Management Level Senior Associate & Summary The Opportunity As a Data Engineer - Senior Associate, you will focus on designing and ...

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Showing results 1-20

Senior Insurance Data Analytics information

What are the key skills and qualifications needed to thrive as a Senior Insurance Data Analytics professional, and why are they important?

To thrive as a Senior Insurance Data Analytics professional, you need a strong background in statistics, data analysis, and domain knowledge of insurance, often supported by a degree in mathematics, statistics, or a related field. Expertise in data analytics tools such as SQL, Python, R, and experience with business intelligence platforms like Tableau or Power BI are typically required. Strong problem-solving skills, attention to detail, and the ability to communicate complex insights clearly set top performers apart in this role. These skills are crucial for driving data-driven decision-making, identifying business opportunities, and improving risk assessment and operational efficiency within insurance organizations.

What does a Senior Insurance Data Analytics professional do?

A Senior Insurance Data Analytics professional analyzes large datasets to help insurance companies make informed decisions about risk, pricing, claims, and customer behavior. They use statistical methods, data modeling, and business intelligence tools to uncover trends and insights that can improve operational efficiency and profitability. In addition to interpreting complex data, they often collaborate with other departments to develop data-driven strategies and may oversee or mentor junior analysts within the team.

What is the difference between Senior Insurance Data Analytics vs Insurance Data Analyst?

AspectSenior Insurance Data AnalyticsInsurance Data Analyst
Required CredentialsBachelor's or Master's in Data Science, Statistics, or related field; often with experience in insurance analyticsBachelor's in related field; entry to mid-level experience
Work EnvironmentSenior roles often involve leadership, project management, and strategic planning within insurance companiesFocus on data collection, analysis, and reporting under supervision or team guidance
Employer & Industry UsageUsed across insurance firms, especially in analytics, underwriting, and actuarial departmentsCommonly employed in insurance companies, focusing on data processing and reporting

Senior Insurance Data Analytics professionals typically have more experience, advanced skills, and leadership responsibilities compared to Insurance Data Analysts. While both roles require strong analytical skills and familiarity with insurance data, seniors often oversee projects, develop strategies, and mentor junior staff, whereas analysts focus on data analysis and reporting tasks.

What are some common challenges faced by Senior Insurance Data Analytics professionals when working with large and complex datasets?

Senior Insurance Data Analytics professionals often encounter challenges such as integrating data from multiple legacy systems, ensuring data quality and accuracy, and managing sensitive information in compliance with regulations. Additionally, translating complex analytical findings into actionable insights for non-technical stakeholders can be demanding. Overcoming these challenges requires strong technical skills, clear communication, and close collaboration with IT, underwriting, and actuarial teams.
What are the most commonly searched types of Insurance Data Analytics jobs in Michigan? The most popular types of Insurance Data Analytics jobs in Michigan are:
What job categories do people searching Senior Insurance Data Analytics jobs in Michigan look for? The top searched job categories for Senior Insurance Data Analytics jobs in Michigan are:
What cities in Michigan are hiring for Senior Insurance Data Analytics jobs? Cities in Michigan with the most Senior Insurance Data Analytics job openings:

Sr IT Director- Data Analytics & AI, AFM, Commercial, Engineering & EV

Dana Incorporated

Novi, MI • On-site

Full-time

Posted 29 days ago


Dana Incorporated rating

5.7

Company rating: 5.7 out of 10

Based on 75 frontline employees who took The Breakroom Quiz

390th of 418 rated machine equipment manufacturers


Job description

Job Purpose
The Sr IT Director- Data Analytics & AI, AFM, Commercial, Engineering & EV is a senior leadership role responsible for defining and executing the company's enterprise-wide data strategy, with a strong focus on Master Data Management (MDM), data governance, and AI-driven transformation across Aftermarket (AFM) and Commercial domains.
This role leads the end-to-end data value chain-from master data integrity and governance to advanced analytics and AI-ensuring that enterprise data is trusted, unified, and actionable. The Sr. Director will partner closely with business, digital, and engineering leaders to embed data and AI into core commercial and operational processes, driving measurable outcomes in revenue growth, customer experience, and operational performance.
Job Duties and Responsibilities
Enterprise Data & AI Strategy Leadership
• Define and lead the enterprise data strategy, anchored in MDM, data governance, analytics, and AI, aligned to AFM and Commercial growth priorities.
• Establish a multi-year roadmap spanning master data, data platforms, analytics, and AI/GenAI capabilities.
• Act as a strategic advisor to executive leadership, shaping how data and AI drive competitive advantage, revenue, and operational excellence.
• Build and lead a high-performing global organization across MDM, data engineering, governance, analytics, and data science.
Master Data Management (MDM) & Data Governance
• Own and institutionalize enterprise MDM strategy and platforms across core domains (Customer, Product, Supplier, Pricing, Assets).
• Establish data ownership, stewardship models, and domain accountability across AFM and Commercial.
• Drive data standardization, harmonization, and lifecycle management to enable consistent reporting and AI readiness.
• Lead enterprise-wide data governance frameworks, including policies, quality management, lineage, and metadata.
• Ensure compliance with regulatory, privacy, cybersecurity, and intellectual property standards.
• Define and track data quality KPIs and drive continuous improvement across business domains.
Data Platforms & Architecture
• Own the strategy and evolution of modern data platforms, including lakehouse architectures, real-time data pipelines, and semantic data layers.
• Ensure platforms are AI-ready, scalable, secure, and optimized for cost and performance.
• Partner with Enterprise Architecture and Cybersecurity to enforce standards, data models, and integration patterns.
• Enable seamless integration of ERP, CRM, supply chain, and engineering data into unified data products.
Analytics, AI & Advanced Capabilities
• Define and scale a portfolio of high-impact analytics and AI use cases, including:
o Commercial performance, pricing, and margin optimization
o Aftermarket demand forecasting and parts optimization
o Customer insights and segmentation
o Predictive maintenance and service optimization
o AI-enabled anomaly detection and operational intelligence
o Generative AI for commercial insights, automation, and decision support
• Lead the end-to-end AI lifecycle (ideation to production) with strong MLOps and governance practices.
• Establish a product-based data & analytics operating model, delivering reusable, scalable data products and AI capabilities.
AFM & Commercial Business Alignment
• Partner with AFM and Commercial leaders to translate business strategy into data, MDM, and AI solutions.
• Ensure master and transactional data enable core commercial processes including quoting, pricing, forecasting, and customer engagement.
• Drive use of data and AI to enhance revenue growth, profitability, and customer experience.
• Act as the primary data and AI leader for AFM and Commercial transformation initiatives.
Education and Qualifications
Required
• Bachelor's degree in Computer Science, Engineering, Data, or related field (Master's preferred).
• 12-15+ years of experience in enterprise data, MDM, analytics, and AI leadership roles.
• Proven track record leading enterprise-scale MDM and data transformation programs.
• Deep expertise in data governance, master data domains, and modern data architectures.
• Experience delivering AI/analytics solutions with measurable commercial impact.
• Strong leadership experience managing global, cross-functional teams and transformation programs.
Preferred
• Hands-on experience with MDM tools/platforms, data quality frameworks, and metadata management.
• Familiarity with AI/ML, MLOps, and GenAI applications in commercial or industrial settings.
• Experience in manufacturing, aftermarket, or asset-intensive industries.
• Exposure to OT/IT convergence and engineering data ecosystems.
• Strong executive presence with ability to influence at C-suite level.
Measures of Success
• Business value delivered through data, MDM, and AI initiatives (revenue, margin, cost, productivity)
• Enterprise data quality, consistency, and governance maturity
• Adoption and impact of analytics and AI in AFM and Commercial operations
• Speed and scalability of data product and AI delivery
• Effectiveness of MDM in enabling enterprise-wide insights and processes
Join our team of 28,000 problem solvers who are fostering a culture of innovation by leveraging the diverse perspectives of our global team. We believe in facing challenges head-on by finding opportunity and uncovering possibility, where roadblocks and barriers become targets instead of obstacles. We are One Dana with limitless opportunity.
Our Values
  • Value Others
  • Inspire Innovation
  • Grow Responsibly
  • Win Together

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