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Remote Insurance Data Analytics Jobs in Michigan

Canopy

Senior Data Analyst

Detroit, MI · On-site +1

$96K - $132K/yr

As a Senior Data Analyst reporting to the Team Manager of Core AI and Data, you will spearhead the ... Life insurance and accidental death & dismemberment insurance Compensation Range Compensation may ...

CSAA

Develops and analyzes data related to complex actuarial functions * Evaluates analytical work done ... Develops and analyzes new methodologies for insurance rating and loss reserving Required Experience ...

CSAA

$39.50 - $54.50/hr

... Insurance Group (CSAA IG), a AAA insurer, is one of the leading personal lines property and ... Partner with data experts in center of excellence to validate feasibility of proposals. * With ...

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Remote Insurance Data Analytics information

What is the difference between Remote Insurance Data Analytics vs Remote Insurance Underwriter?

AspectRemote Insurance Data AnalyticsRemote Insurance Underwriter
Required CredentialsBachelor's in Data Science, Statistics, or related field; often certifications in data analysis or analyticsBachelor's in Business, Finance, or related; often requires insurance licensing or certifications
Work EnvironmentPrimarily data analysis, modeling, and reporting; often collaborative with IT and actuarial teamsAssessing risks, reviewing applications, making underwriting decisions; involves communication with agents and clients
Employer & Industry UsageUsed across insurance companies, reinsurers, and brokers for data-driven decision makingUsed by insurance carriers to evaluate and approve policies

Remote Insurance Data Analytics focuses on analyzing insurance data to inform business decisions, while Remote Insurance Underwriters evaluate individual insurance applications to determine coverage. Both roles are essential in the insurance industry but differ in daily tasks and required skills.

What is remote insurance data analytics?

Remote insurance data analytics is the practice of analyzing insurance-related data, such as claims, risk assessments, and customer information, from a location outside of a traditional office setting. Professionals in this field use statistical methods, data mining, and machine learning tools to identify patterns, detect fraud, and help insurance companies make data-driven decisions. This remote role often requires proficiency in data analysis tools like SQL, Python, or R, and a strong understanding of insurance industry concepts. Remote insurance data analysts collaborate with teams virtually to provide insights and support business strategies, making it a flexible career option.

How do remote insurance data analytics professionals typically collaborate with cross-functional teams to drive business insights?

Remote Insurance Data Analytics professionals often work closely with underwriters, actuaries, claims managers, and IT teams to gather data requirements, interpret findings, and implement data-driven solutions. Collaboration usually happens through virtual meetings, collaborative dashboards, and project management tools to ensure clear communication and alignment on objectives. This cross-functional approach helps identify trends, optimize risk assessments, and support strategic decision-making within the organization. Building strong relationships with team members across departments is key to successfully translating analytical results into actionable business strategies.

What are the key skills and qualifications needed to thrive as a remote insurance data analytics professional?

To excel in Remote Insurance Data Analytics, you need strong analytical skills, a background in statistics or mathematics, and typically a degree in data science, actuarial science, or a related field. Familiarity with data analysis tools like SQL, Python, R, and specialized insurance analytics platforms such as SAS or Tableau, as well as relevant certifications, is highly valuable. Attention to detail, problem-solving abilities, and effective communication set candidates apart in this role. These skills are crucial for transforming complex insurance data into actionable insights that drive informed business decisions and risk assessments.
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 are popular job titles related to Remote Insurance Data Analytics jobs in Michigan? For Remote Insurance Data Analytics jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Remote Insurance Data Analytics jobs in Michigan look for? The top searched job categories for Remote Insurance Data Analytics jobs in Michigan are:
What cities in Michigan are hiring for Remote Insurance Data Analytics jobs? Cities in Michigan with the most Remote Insurance Data Analytics job openings:
Remote Insurance Data Analytics jobs near you
Infographic showing various Remote Insurance Data Analytics job openings in Michigan as of August 2026, with employment types broken down into 4% Internship, 68% Full Time, 11% Part Time, and 17% Contract. Highlights an 100% Remote job distribution.

Senior Data Analyst

Canopy

Detroit, MI • On-site, Remote

$96K - $132K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

This job post has expired 1 day ago. Applications are no longer accepted.

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Job description

Join Canopy, a Ford-backed company, at the forefront of engineering advanced threat detection and deterrence products specifically designed for vehicles. Our mission is to eliminate vehicle crime and enhance mobility through cutting-edge consumer hardware, aftermarket connectivity, and AI-driven security solutions. As part of our team, you'll be at the forefront of innovation, helping to solve one of today's most pressing challenges with cutting-edge solutions.
As a Senior Data Analyst reporting to the Team Manager of Core AI and Data, you will spearhead the design, development, and implementation of cutting-edge machine learning models and algorithms to address intricate business challenges. Your role will involve analyzing large datasets to identify meaningful patterns, trends, and relationships, using a combination of statistical methods, machine learning techniques, and data visualization tools. Additionally, you will be responsible for developing and validating approaches that ensure accuracy, robustness, and scalability for production deployment.
Responsibilities:
  • Lead the design, development, and implementation of advanced machine learning models and algorithms to solve complex business problems.
  • Collaborate with cross-functional teams, including product managers, engineers, and business stakeholders, to identify data-driven opportunities and translate them into actionable insights.
  • Analyze large datasets to extract meaningful patterns, trends, and relationships, leveraging statistical methods, machine learning techniques, and data visualization tools.
  • Develop and validate approaches, ensuring accuracy, robustness, and scalability for deployment in production environments.
  • Communicate findings and recommendations to both technical and non-technical audiences through detailed reports, presentations, and data visualizations.
  • Mentor and provide guidance to junior data scientists and analysts, fostering a culture of continuous learning and innovation.

Requirements
  • Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
  • 5+ years of experience in data science, with a strong background in statistical analysis, machine learning, and predictive modeling.
  • Proficiency in programming languages such as Python.
  • Strong expertise in SQL and experience working with large-scale databases and data processing frameworks.
  • Demonstrated ability to work with complex data sets, including data cleaning, transformation, and feature engineering.
  • Experience with data visualization tools such as Tableau, Grafana, or Matplotlib to present insights and results effectively.
  • Strong problem-solving skills, with the ability to work independently and manage multiple projects simultaneously.
  • Excellent communication skills, both written and verbal, with the ability to convey complex technical concepts to non-technical stakeholders.

Preferred Qualifications:
  • Master's or Ph.D. in Data Science, Computer Science, Statistics, or a related field.
  • Experience with deep learning techniques and frameworks such as TensorFlow, or PyTorch.
  • Familiarity with cloud computing platforms like AWS, Azure, or Google Cloud.
  • Experience where data science has driven significant business outcomes.
  • Strong understanding of A/B testing, experimental design, and causal inference techniques.
  • Experience with MLOps practices, including model deployment, monitoring, and lifecycle management in production environments.
  • Reside within the Detroit area or nearby, with the ability to work in a hybrid environment and regularly commute to our Detroit office as needed.

Benefits
  • Comprehensive medical benefits coverage, dental plans and vision coverage.
  • Health care and dependent care spending accounts.
  • Employee and Family Assistance Program (EAP).
  • Employee discount programs.
  • Retirement plan with a generous company match.
  • Generous Paid Time Off, Sick, and Holidays
  • Family Leave (Maternity, Paternity)
  • Short- and long-term disability
  • Life insurance and accidental death & dismemberment insurance

Compensation Range
Compensation may vary depending on skills and experience.
Base Salary: $96,900 - $132,300
Diversity, Equity and Inclusion: At Canopy, we're on a mission to end theft from vehicles and revolutionize vehicle security by building cutting-edge technology. We will achieve this by prioritizing individuals and staying attuned to the evolving needs of our people, users, and industry trends. We foster a workplace culture that embraces diversity and authenticity, enabling us to flourish as a team of exceptional individuals working towards a common purpose. We gain a deeper understanding of our users' experiences by continuously improving our skills and expanding our knowledge. A more diverse, equitable, and inclusive Canopy leads to greater innovation and success.
Equal Opportunity: Canopy does not discriminate on the basis of race, sex, color, religion, age, national origin, marital status, disability, veteran status, genetic information, sexual orientation, gender identity or any other reason prohibited by law in provision of employment opportunities and benefits.

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