Data Scientist, P&C Insurance

Upland Capital Group•Dallas, TX

1d•$110,000 - $165,000•Remote

About The Position

Upland Capital Group, Inc. is a specialty property/casualty insurer seeking a Data Scientist to join its Risk, Analytics, & Data (RAD) team. This is a remote, individual contributor role focused on statistical modeling, programming, and deploying/maintaining models in production. The company emphasizes a blend of traditional underwriting with modern analytics and technology, fostering a growth mindset among its employees. The RAD team operates in a cloud-based, containerized environment on Azure, aiming for highly automated processes for model development, testing, and deployment to enhance insights for actuarial, underwriting, and claims functions. The role involves creative problem-solving and close collaboration with stakeholders across the organization, with a flexible approach to model development. The Data Scientist will work across the full model lifecycle, from analysis and development to deployment, monitoring, and maintenance, with opportunities for skill development and ownership in a small, growing team.

Requirements

2–5+ years of technical experience in a data science, actuarial, analytics, or predictive modeling role.
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.

Nice To Haves

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.

Responsibilities

Perform analyses and build models to support decision-making for actuarial, underwriting, claims, and other functions.
Carry models through deployment and into production.
Translate business requirements from 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 a 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 for underwriting, claims, and operational use cases.
Contribute to a strong team culture by participating actively in code review and pairing, sharing knowledge, and providing/seeking feedback.
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 with Data and Model Engineering and with underwriting, claims, and business stakeholders.