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Intern Data Science Insurance Jobs in Plano, TX (NOW HIRING)

Crowe LLP

Machine Learning Intern

Dallas, TX · On-site

$27 - $42/hr

Role Summary The data science (DS) internship at Crowe follows the firmwide calendar, approximately ... To that end, there are three major components with which an intern should expect to engage.

Crowe LLP

Machine Learning Intern

Plano, TX · On-site

$27 - $42/hr

Role Summary The data science (DS) internship at Crowe follows the firmwide calendar, approximately ... To that end, there are three major components with which an intern should expect to engage.

State Farm

Data Scientist

Richardson, TX · Hybrid

Develops models that support State Farm's insurance pricing and underwriting decisions Data Science at State Farm : As a Data Scientist at State Farm, you will serve as a subject matter expert and ...

Dlapiper

Data Scientist

Dallas, TX · On-site +1

Prioritizes, scopes and manages data science projects for internal stakeholders and clients ... DLA Piper offers a comprehensive benefits package, including medical, dental, and vision insurance ...

Ad hoc Research

Software Engineer Intern

Plano, TX

Software Engineer Intern Position Overview: We are seeking talented and driven individuals to join ... Collaborate with data scientists and AI engineers to implement machine learning models and AI ...

Kemper

Data Scientist

Dallas, TX · On-site +1

... specialized insurers. Our success is a direct reflection of the talented and diverse people who ... Data Science is a driver of significant competitive advantage for Kemper and is critical to the ...

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How much do intern data science insurance jobs pay per hour?

As of Sep 4, 2026, the average hourly pay for intern data science insurance in Plano, TX is $21.54, according to ZipRecruiter salary data. Most workers in this role earn between $16.59 and $23.46 per hour, depending on experience, location, and employer.

What does an intern data science insurance do?

An Intern Data Science Insurance assists data scientists and analysts in the insurance industry by collecting, cleaning, and analyzing data to help solve business problems. Their tasks often include working with large datasets, building predictive models, and creating visualizations to support risk assessment, fraud detection, and pricing strategies. They also collaborate with other departments to understand insurance processes and contribute to the development of data-driven solutions. This role offers hands-on experience with industry tools and methodologies, preparing interns for a career in data science within the insurance sector.

What types of projects can an intern data science expect to work on within the insurance industry?

As a Data Science Intern in the insurance sector, you can expect to work on projects such as analyzing customer data to identify risk factors, developing predictive models for claims, or assisting in the automation of underwriting processes. These projects often involve collaboration with actuarial, underwriting, and IT teams, providing interns with exposure to different facets of the business. You'll likely use tools like Python, R, and SQL while working with real datasets, and your contributions may directly impact decision-making and operational efficiency. This hands-on experience is valuable for understanding the practical applications of data science in a highly regulated and data-driven industry.

What are the key skills and qualifications needed to thrive as an intern data science insurance, and why are they important?

To thrive as an Intern Data Science in Insurance, you need foundational knowledge in statistics, data analysis, and programming (often with Python or R), typically supported by coursework in data science or related fields. Familiarity with data visualization tools (such as Tableau or Power BI), SQL databases, and machine learning libraries is often expected. Strong analytical thinking, attention to detail, and effective communication help you interpret data insights and collaborate with cross-functional teams. These skills are crucial for extracting actionable insights from complex insurance data and supporting data-driven decision-making within the industry.

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Intern Data Science Insurance jobs near you

Data Scientist, P&C Insurance - Remote

UPLAND CAPITAL GROUP INC

Dallas, TX • On-site, Remote

$110K - $165K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 14 days ago

Key responsibilities

  • Perform analyses and build models to support decision-making for actuarial, underwriting, claims, and other functions across the organization

  • Translate business requirements into actionable data science projects and see them through to completion

  • Deploy, monitor, and maintain models in a containerized Azure environment, applying MLOps principles

Job description

Data Scientist, P&C Insurance - Remote
Upland Capital Group, Inc. is an AM Best rated "A-" VIII specialty property/casualty insurer headquartered in Dallas, Texas. Through its wholly owned insurance carrier, Upland Specialty Insurance Company, the company markets, underwrites and services specialty insurance products in select markets to include excess transportation, construction casualty, excess casualty, primary general liability, excess public entity, professional liability errors and omissions as well as excess cyber liability.
We focus on "old school" underwriting as a craft, add "new school" analytics and technology, and encourage a gritty, growth mindset among people called "we entrepreneurs."
As an Excess and Surplus (E&S) carrier, we face unique and interesting challenges every day. We are looking for a Data Scientist to join our Risk, Analytics, & Data (RAD) team.
Primary Function:
The Data Scientist role requires a strong statistical and programming foundation, hands-on experience building models, and a genuine interest in how models are deployed and maintained in production. This is an individual contributor role where you can make a real impact from day one, and we are open to a range of experience levels: you might be an early-career data scientist with a strong foundation and high potential, or a seasoned practitioner who already owns the full model lifecycle. Title and responsibilities will be calibrated to your experience. The role will report to the Director of Data Science.
At our Risk, Analytics, & Data (RAD) team, we focus on Actuarial, Data Science, Data and Model Engineering, and Enterprise Risk Management functions. Our Actuarial and Data Science model environment and architecture is containerized, and we are cloud based, running on Azure. Our vision is to build highly automated and efficient processes to build, test, and deploy our models and products for enhancing actuarial, underwriting and claim insights with timely and relevant data-driven analytics and technology. We look to create models that require creative problem solving and a close collaboration with stakeholders across the organization, not limited by a 'one size fits all' mindset. In this role, you will work across the full model lifecycle - from analysis and model development through deployment, monitoring, and maintenance in production. As a member of a small, growing team, your scope will grow with you: you'll find both the support to develop new skills and the room to take ownership quickly.
Duties and Responsibilities:
As a Data Scientist at Upland, you will perform analyses and build models to support decision-making for actuarial, underwriting, claims, and other functions across the organization, and learn to carry those models through deployment and into production. You will be encouraged to try new things that push Upland forward and expand your own skillset. As a member of a small, growing team, you will have unusual visibility into how models drive business decisions and broad exposure across the model lifecycle. Responsibilities will include:
  • Translate business requirements from different stakeholders into actionable data science projects
  • Drive projects forward, take ownership of project deliverables, and see assigned work through to completion
  • Curate modeling datasets using internal and external data sources
  • Build, test, and validate statistical and machine learning models using appropriate techniques, grounded in sound statistical reasoning
  • Clearly explain results and recommendations to technical and business stakeholders
  • Establish and follow strong engineering practices - code review, reproducibility, experiment tracking, and model documentation
  • Deploy, monitor, and maintain models in our containerized Azure environment, applying MLOps principles, including - version control, automated testing, CI/CD, model versioning, and drift detection
  • Develop AI-powered tools and applications, including LLM-based solutions (e.g., automating aspects of the modeling process, surfacing insights from model output) for underwriting, claims, and operational use cases
  • Contribute to a strong team culture by participating actively in code review and pairing, sharing what you learn, and both providing and seeking feedback to/from team members
  • Research, learn, test, and apply new techniques to advance the company's statistical modeling/MLOps/AI engineering capabilities
  • Build strong partnerships within RAD and across the organization, working hand-in-hand with Data and Model Engineering and with our underwriting, claims, and business stakeholders to understand their needs and deliver solutions that create real value

Experience, Education, Special Skills Required:
  • 2-5+ years of technical experience in a data science, actuarial, analytics, or predictive modeling role, with hands-on experience deploying models or analytics tools to production
  • Strong statistical foundation and analytical skills - able to select, build, validate, and interpret models rigorously, and explain the results clearly
  • Proficiency in programming languages such as Python, R, and/or SQL, with the ability to write production-quality code
  • Strong knowledge of a variety of modeling techniques and the ability and interest to learn new techniques quickly (e.g., Regression, Classification, Bayesian Modeling, Natural Language Processing, Price Optimization, etc.)
  • Experience applying software engineering and MLOps principles - e.g., Git-based workflows, containerization (Docker), CI/CD, model versioning, and monitoring
  • Experience with cloud environments (e.g., Azure, AWS) for model development and deployment
  • Practical experience building with LLMs and generative AI - e.g., building and using skills, model APIs, prompt-based tools, agent workflows, etc.
  • Self-starter, quick learner, and creative problem solver that thrives in a flexible, fast-paced, and remote work environment

Preferred Experience, Education, and Skills:
  • P&C insurance domain knowledge, particularly commercial lines and E&S products
  • Experience in the end-to-end model creation and deployment process to improve product, pricing, reserving, underwriting, and claims in P&C insurance
  • Bachelor's or Master's degree in Mathematics, Statistics, Data Science, Actuarial Science, Computer Science, or related quantitative field
  • Experience with a fully containerized model architecture and ML platforms (e.g., Azure ML, MLflow, SageMaker)
  • Experience with non-relational (NoSQL) databases and modern data platforms (e.g., Snowflake)
  • Experience with visualization tools such as Power BI, Shiny, Streamlit, etc.

Disclosures:
Pay Estimate: $ 110,000 - $165,000
Other compensation: annual incentive program
Benefits: health insurance including FSA and HSA options and free access to Teladoc, vision, dental, disability and life insurance, parental leave, responsible time off (unlimited vacation days without an accrual system), paid sick time as required by law, 401(k), tuition reimbursement and employee assistance program.

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