Data Scientist, P&C Insurance - Remote

Data Scientist, P&C Insurance - Remote

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
• 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
• 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, 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
• 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)

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.