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Senior Insurance Data Analytics Jobs in Connecticut

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Senior Insurance Data Analytics information

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

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 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 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 the most commonly searched types of Insurance Data Analytics jobs in Connecticut?

The most popular types of Insurance Data Analytics jobs in Connecticut are:

What cities in Connecticut are hiring for Senior Insurance Data Analytics jobs?

Cities in Connecticut with the most Senior Insurance Data Analytics job openings:

Senior Insurance Data Analytics jobs near you

Senior Product Manager - Data Analytics & Cloud Platform

Neshent Technologies

East Berlin, CT • On-site

$124K - $164K/yr

Full-time

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

Job description

Roles & Responsibilities
  • Own the vision, strategy, roadmap, and lifecycle of enterprise data products.
  • Serve as the Product Owner and subject matter expert for Cloud Analytics initiatives.
  • Partner with business stakeholders, engineering teams, and leadership to define product requirements and priorities.
  • Manage and prioritize the product backlog, ensuring alignment with business objectives.
  • Create and refine user stories, acceptance criteria, and sprint priorities for Agile delivery teams.
  • Drive the design, implementation, enhancement, and sustainment of data products.
  • Collaborate with Scrum teams to deliver high-quality analytics and data integration solutions.
  • Act as the voice of customers and business stakeholders, ensuring product decisions deliver maximum value.
  • Monitor product performance, identify improvement opportunities, and drive continuous innovation.
  • Facilitate Agile ceremonies and ensure successful execution of Scrum and SAFe practices.
  • Communicate product roadmap, progress, risks, and outcomes to leadership and key stakeholders.
  • Support continuous improvement initiatives and promote best practices in Agile product management.
Required Technical/Functional Skills
  • 10+ years of experience in Product Management, Product Ownership, or Business Analysis within enterprise IT environments.
  • Proven experience managing Cloud Analytics, Data Analytics, or Data Platform products.
  • Strong understanding of data analytics, data science, and data integration concepts.
  • Experience defining product vision, strategy, roadmap, and execution for enterprise data products.
  • Hands-on experience with Agile methodologies, including Scrum and SAFe.
  • Strong experience managing product backlogs, user stories, acceptance criteria, and release planning.
  • Proficiency with Jira, Confluence, Microsoft Visio, Whiteboard, and PowerPoint.
  • Excellent stakeholder management, communication, analytical, and problem-solving skills.
Preferred Skills
  • Experience working in Claims, Insurance, Consumer, or Leave Management domains.
  • Knowledge of Cloud Analytics platforms, Data Warehousing, and Data Integration solutions.
  • Experience collaborating with cross-functional engineering, analytics, and business teams.
  • Ability to translate business needs into scalable data product solutions.

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