Data Scientist

Orion180•Irving, TX

32d•Onsite

About The Position

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.

Requirements

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.

Nice To Haves

Strong understanding of actuarial principles, risk modeling, or loss reserving methodology is a significant differentiator.

Responsibilities

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.
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.
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.
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.