Data Scientist

In this role you will design and deploy machine learning models to solve complex business problems, manage the full model lifecycle, and build scalable data pipelines. Drive innovation through advanced analytics and AI, while translating insights into actional decisions and partnering across teams to embed solutions into business workflows.

Advanced Modeling & Machine Learning

• Design, develop, and deploy supervised and unsupervised machine learning models to solve complex business problems in underwriting, pricing, claims, and risk segmentation.
• Lead end-to-end model lifecycle management: problem framing, feature engineering, model selection, validation, deployment, and ongoing performance monitoring.
• Apply advanced statistical techniques to extract signals from complex, high-dimensional datasets.
• Develop and refine forecasting models to support actuarial, financial, and operational planning functions.

Data Engineering & MLOps

• Build and maintain scalable, reproducible data pipelines and feature stores that support modeling workflows.
• Architect and implement MLOps practices to ensure production reliability.
• Partner with data engineering teams to integrate model outputs into operational systems and downstream analytics workflows.
• Champion data quality, lineage, and governance across structured and unstructured datasets used in modeling.

Research, Experimentation & Innovation

• Design and execute rigorous experiments, translating results into actionable business recommendations with quantified impact.
• Evaluate and prototype emerging AI/ML techniques, including large language model (LLM) integrations, generative AI applications, and automated insight generation for applicability to insurance and financial services use cases.
• Stay current with the academic and industry literature; bring new methodologies into the team's toolkit where they create measurable advantage.

Communication & Cross-functional Partnership

• Translate complex model outputs and statistical findings into clear, actionable narratives for leadership, actuarial, underwriting, claims, and distribution partners.
• Collaborate with sales, underwriting, claims, and actuarial teams to embed models into decision workflows and ensure outputs are interpreted and applied correctly.
• Document model assumptions, governance processes, and validation results to maintain compliance with regulatory and audit standards., While performing general duties for this position, the employee is regularly required to sit, stand, and/or walk around (including the use of stairs). Other demands include the ability to openly communicate with others by talking, listening, comprehending, and reading; being able to lift light objects (<25 lbs); and using standard office equipment such as computers, printers, and phones. In addition, there is an occasional need to bend, twist, or squat down to open/close cabinets and reach for files or other standard office-type objects.

Do you have experience in SQL?, Do you have a Master's degree?, * Master's or Ph.D. in Statistics, Data Science, Computer Science, Mathematics, Operations Research, or a related quantitative field; Bachelor's considered with exceptional applied experience., * 4+ years of applied data science experience, with demonstrated delivery of production-grade ML models-ideally within insurance, financial services, or another regulated industry.

• Deep proficiency in Python (scikit-learn, XGBoost/LightGBM, TensorFlow/PyTorch, statsmodels) and SQL optimization across large-scale datasets.
• Hands-on experience with cloud data platforms (Snowflake, Databricks) and MLOps tooling (MLflow, Vertex AI, SageMaker, or equivalents).
• Track record of translating ambiguous business problems into structured, solvable data science problems with measurable outcomes.
• Strong understanding of actuarial principles, risk modeling, or loss reserving methodology is a significant differentiator.

Pulled from the full job description

• 401(k)
• Health insurance
• Paid time off
• Vision insurance
• Dental insurance
• Paid holidays, * Dynamic Environment: On-site role with a fast-paced and collaborative team culture. Results-driven office where your contributions make a real impact.
• Compensation: Competitive base pay and performance bonuses.
• Career Growth: Mentorship, growth tracks, and professional development.
• Benefits: Medical, dental, vision, 401k, paid holidays, PTO and more!

The office environment is fast-paced and collaborative. An employee must be willing and able to work their regularly assigned work schedule onsite, and in times of need, be able to work an extended schedule depending on company or departmental needs, project requirements, or customer demands.

At Orion180, we don't just stand out for what we do; we shine because of how we do it. Our mission is to deliver an exceptional insurance experience through innovative technology, unparalleled customer service, and a comprehensive suite of products. Our vision is to be the premier global provider of insurance solutions and redefine the insurance industry. Orion180 is proud to call three vibrant cities home. Our headquarters on Florida's stunning Space Coast in Melbourne offers an exceptional quality of life, with affordable housing, excellent transportation, and close proximity to major cities like Orlando, Tampa, and Miami. Our Irving, Texas, office places us in a dynamic center of culture, business, and entertainment, keeping us closely connected to innovation and growth. Our Draper, Utah, location, nestled in the heart of the Salt Lake Valley, connects us to a rapidly growing tech ecosystem and provides access to the outdoor lifestyle and natural beauty the region is known for. We have an exciting and impactful opportunity for a Data Scientist to join our growing analytics team in Irving, TX We believe analytics isn't a back-office function. It's not a reporting layer bolted onto the business after the real decisions get made. It's the foundation that tells you where risk actually lives, where growth is hiding, and where you're missing opportunities before it's too late to do anything about it. That belief is driving real investment in our people, platforms, and in how deeply data science is woven into the way we operate. From underwriting to distribution to customer experience, we're building a data culture where predictive intelligence drives action. It also means evolving how we think about the role of a data scientist. The expectation isn't just technical execution. It's curiosity. Ownership. The ability to step into ambiguity, frame the problem, engineer the solution, and drive the business toward outcomes that would have been invisible without you.