IT Data Scientist

COMPANY OVERVIEW:

Founded in 1989, United Automobile Insurance Company is an innovative and established organization looking for an IT Data Scientist to join our team. Family-owned, UAIC, is one of the largest privately held property and casualty insurance companies in the United States. The key to our growth and success is a commitment to providing quality service to our agents and customers, by providing disciplined underwriting, and strategic claims handling.

Our ongoing efforts to invest substantial resources in personnel and technology are the foundation of our promise of assuring maximum efficiency and specialized operations in the industry. We are proud that our independent agents and customers have recognized our dedication by making UAIC the market leader in every state where we conduct business.

SCOPE:

As an IT Data Scientist at UAIC, you will work on applying machine learning and AI techniques to real-world business problems spanning claims, operations, marketing, and product strategy. You’ll explore data deeply, develop and validate predictive models, and contribute to the implementation of intelligent systems that enhance decision-making and operational efficiency.
This role is ideal for candidates with strong modeling, AI/ML, and data storytelling skills who are excited to apply them in a collaborative, evolving environment.

P&C Insurance domain experience is preferred. This position offers primarily remote work arrangement, allowing the ideal candidate to work from their preferred location within the Southeast region.

DUTIES :

• Work on machine learning and statistical modeling projects, including:
• Claims predictions (e.g., reserving, triage, litigation risk)
• Customer lifecycle modeling (e.g., retention, segmentation, profitability)
• Agent performance analysis
• Product/pricing insights
• Collaborate with business and analytics stakeholders to identify, frame, and prioritize modeling opportunities.
• Perform feature engineering, data exploration, and hypothesis testing using large, structured datasets.
• Build and evaluate models using appropriate AI/ML methods, ensuring rigor, interpretability, and business value.
• Work with engineering teams to integrate models into production environments.
• Support model monitoring, validation, and documentation for continuous improvement.
• Communicate findings clearly through visualization and narrative to both technical and business audiences.

EDUCATION:

• Bachelor’s or Master’s in Statistics, Computer Science, Data Science, Engineering, or related field;
  Ph.D. is a plus.

SKILLS & EXPERIENCE:

• 5+ years of experience in applied data science or machine learning roles.

• Strong programming skills in Python or R.
• Experience with AI/ML frameworks such as PyTorch or Tensor Flow.
• Solid knowledge of statistical methods and model evaluation metrics, tools like SAS.
• Familiarity with SQL and working with large-scale data on platforms like Azure, AWS, or GCP.
• Experience with ML lifecycle tools (e.g., MLFlow, DVC, Azure ML Studio) is a plus.
• Strong communication and collaboration skills.
• Understanding P&C insurance datasets is helpful but not necessary.

BENEFITS:

• 401(k) Retirement Savings Plan with employer match.
• Comprehensive Medical, Prescription Drug, Vision, and Dental Insurance
• Paid Time Off, Holidays, and Leave programs.
• Flexible spending accounts
• Basic Life Insurance and Voluntary Life/ADD
• Voluntary Short Term and Long-Term Disability

UAIC is an Equal Opportunity Employer and is committed to the principle of equal employment opportunity for all employees. All employment decisions at UAIC are based on business needs, job requirements, and individual qualifications, without regard to race, color, religion, or belief, family or parental status, or any other status protected by the laws or regulations in the locations where we operate.

UAIC participates in the E-Verify program to confirm the employment eligibility of all newly hired employees. For more information about E-Verify, please visithttps://(Use the "Apply for this Job" box below). .

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