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Senior Insurance Data Analytics Jobs in Arizona (NOW HIRING)

This position reports to the Senior Manager of Data Analytics and participates in a team environment. The Senior Data Analyst provides support of all organizational data management functions.

This position reports to the Senior Manager of Data Analytics and participates in a team environment. The Senior Data Analyst provides support of all organizational data management functions.

PW Growth Strategy Analytics is growing! We're opening a new role to support some of the most ... senior leader stakeholders to implement. * Develops, owns and manages recurring analytic or ...

This role partners with senior leaders to elevate data-driven business strategy and support achievement of financial objectives. What You Will Do: Revenue & Operational Analytics: * Lead subscription ...

This role partners with senior leaders to elevate data-driven business strategy and support achievement of financial objectives. What You Will Do: Revenue & Operational Analytics: * Lead subscription ...

Showing results 41-60

Senior Insurance Data Analytics information

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 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 Arizona? The most popular types of Insurance Data Analytics jobs in Arizona are:
What cities in Arizona are hiring for Senior Insurance Data Analytics jobs? Cities in Arizona with the most Senior Insurance Data Analytics job openings:

Data Scientist / Data Scientist, Senior

Socket.dev

Phoenix, AZ โ€ข On-site

$120 - $180/hr

Other

Posted 4 days ago


Job description

Date: Jun 15, 2026

Location: PHOENIX, AZ, US, 85004-3903

Company: APS

Our present and future success depends on the creative and dedicated people of our company who demonstrate the principles outlined in the APS Promise: Design for Tomorrow, Empower Each Other and Succeed Together.

Summary

Data Scientist / Data Scientist, Senior

Are you a Data Scientist / Data Scientist, Senior ready to make a big impact at scale? We're looking for a highly skilled Data Scientist / Data Scientist, Senior to lead the design and deployment of production-grade machinelearning systems in a complex enterprise environment. Youโ€™ll own the full MLOps lifecycleโ€”from prototyping tomonitoringโ€”and architect solutions that power intelligent, real-time decision-making across critical businessfunctions.
This is a high-visibility role where youโ€™ll collaborate with cross-functional teams, influence architecture, and helpdefine best practices that shape the future of ML at scale.

What Youโ€™ll Do:
  • Lead MLOps Initiatives: Design, build, deploy, and monitor end-to-end ML solutions that are scalable,reliable, and secure.
  • Architect for Scale & Speed: Build applications optimized for low latency on high-volume data pipelinesand streaming environments.
  • Advise & Innovate: Act as a thought partner to data scientists and engineering leaders, bringing deepdomain expertise in ML model design and infrastructure.
  • Collaborate Cross-Functionally: Work with enterprise architects, product teams, and data scientists todeliver real-world business value.
  • Own Quality & Governance: Establish and maintain best practices for ML lifecycle management, includingCI/CD, monitoring, testing, and documentation.
Youโ€™ll Be a Great Fit If You Have:
  • Held a Machine Learning Engineer or MLOps role in a large-scale enterprise environment.
  • Deep experience with modern ML models, cloud-native data platforms, and orchestration tools (e.g.,Kubeflow, SageMaker, MLflow).
  • Proven ability todesign scalable ML architecturesfor streaming and batch use cases.
  • A mindset formentorship and technical leadership, with the ability to guide teams on best practices inproduction ML.

*Sponsorship for U.S. work authorization is not available for this position, now or in the future.

Minimum Requirements Data Scientist
  • BS degree in Data Science, Computer Science, Information Sciences, Mathematics, Engineering or related field
  • *PLUS minimum four(4) years directly related data analytics, data science, predictive modeling, machine learning, statistical modeling and/or user experience role
  • *OR advanced degree and two (2) years directly related experience.
  • Possesses a combination of strong analytical and problem-solving skills and programming knowledge, or an equivalent combination of education and experience with demonstrated comparable knowledge and abilities.
Data Scientist, Senior
  • BS degree in Data Science, Computer Science, Information Sciences, Mathematics, Engineering or related field
  • *PLUS minimum six (6) years directly related data analytics, data science, predictive modeling, machine learning, statistical modeling and/or user experience role
  • *OR advanced degree and four (4) years directly related experience.
  • Possesses a combination of strong analytical and problem-solving skills and programming knowledge, or an equivalent combination of education and experience with demonstrated comparable knowledge and abilities.
  • Preferred Special Skills, Knowledge or Qualifications:
  • Masters or Doctorate degrees in related fields.
  • Knowledge/experience in utility industry and business functions.
  • Certification in Data Science and/or predictive analytics
  • A high level of proficiency in commonly used programming languages and tools like R Programming, Python and SQL.
  • Strong communication, presentation and writing skills.
  • Must be able to lead teams in evaluations and implementation of solutions.
  • Must be able to work with key internal and external stakeholders and all levels of management.
Major Accountabilities

1) Collaboration with customers and partners:

  • Consult with stakeholders and subject matter experts to understand business needs and operations, goals and objectives and key drivers for performance.
  • Work closely with the business units to complete data analytics efforts. Build and maintain strong working relationships with customers, partners and vendors.

2) Data requirements and preparation:

  • Identify available and relevant data and the data sources.
  • Collaborate with SMEs, data stewards and architects for data collection, preparation, integration, quality, exploration and retention.
  • Gather data, formulate cluster or nodes and establish performance checks on the large data models.
  • Design and implementation of solutions including data acquisition, storage, transformation, and analysis

3) Modeling and Deployment:

  • Design, develop and deploy innovative models. Provide insights from predictive statistical modeling activities. Test theories by creating models and experimenting with data.
  • Design models, algorithms and visualizations that help distill insights from huge volumes of chaotic data.
  • Modeling complex problems, discovering insights and identifying opportunities through the use of statistical, algorithmic, mining and visualization techniques using existing or new front-end reporting & analytics tools.
  • Play key role in turning data into critical information and knowledge that can be used to make sound organizational decisions.
  • Propose innovative ways to look at problems by using data mining approaches and validate findings using experimental and iterative approaches.
  • Understand data transforming platforms and technologies and maintain a knowledge of discipline maturity.

4) Present results, provide recommendations and lead analytics efforts:

  • Present findings to the business in a way that can be easily understood by business counterparts.
  • Make recommendations based on business requirements and knowledge of industry best practices.
  • Make technical decisions on advanced analytics initiatives.

5) Programming and Coding

  • Utilize programming language, such as R, Python, SQL, .net, Java or C++ to evoke the data from data source and model
  • Familiarity with Cloud structure and building, utilizing cloud technologies
  • Performing data acquisition using JSon, SQL, ODBC, JScript, or API for Big Data extracts
  • Transform and utilize streaming data with programming languages such as: KAFKA, SQL, Spark, and/or Azure

6) Mentoring and coaching junior staff as necessary

Export Compliance / EEO Statement

This position may require access to and/or use of information subject to control under the Department of Energy's Part 810 Regulations (10 CFR Part 810), the Export Administration Regulations (EAR) (15 CFR Parts 730 through 774), or the International Traffic in Arms Regulations (ITAR) (22 CFR Chapter I, Subchapter M Part 120) (collectively, 'U.S. Export Control Laws'). Therefore, some positions may require applicants to be a U.S. person, which is defined as a U.S. Citizen, a U.S. Lawful Permanent Resident (i.e. 'Green Card Holder'), a Political Asylee, or a Refugee under the U.S. Export Control Laws. All applicants will be required to confirm their U.S. person or non-US person status. All information collected in this regard will only be used to ensure compliance with U.S. Export Control Laws, and will be used in full compliance with all applicable laws prohibiting discrimination on the basis of national origin and other factors. For positions at Palo Verde Nuclear Generating Stations (PVNGS) all openings will require applicants to be a U.S. person.
Pinnacle West Capital Corporation and its subsidiaries and affiliates ('Pinnacle West') maintain a continuing policy of nondiscrimination in employment. It is our policy to provide equal opportunity in all phases of the employment process and in compliance with applicable federal, state, and local laws and regulations. This policy of nondiscrimination shall include, but not be limited to, recruiting, hiring, promoting, compensating, reassigning, demoting, transferring, laying off, recalling, terminating employment, and training for all positions without regard to race, color, religion, disability, age, national origin, gender, gender identity, sexual orientation, marital status, protected veteran status, or any other classification or characteristic protected by law.
For more information on applicable equal employment regulations, please refer to EEO is the Law poster. Federal law requires all employers to verify the identity and employment eligibility of every person hired to work in the United States, refer to E-Verify poster. View the employee rights and responsibilities under the Family and Medical Leave Act (FMLA). Arizona Public Service is a smoke free workplace.

Home based:Home based employees primarily work from their home offices and come into an APS facility on an as-needed basis.

  • *Employees are expected to reside in Arizona (or New Mexico for Four Corners-based employees).*
  • *Working from a home office requires adequate technology and an appropriate ergonomic set up.*
  • *Role types are subject to change based on business need.*

Job Segment: Nuclear, Energy

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