Sr Predictive Modeling Scientist

Orion180 Insurance Services•Irving, TX

5d•Onsite

About The Position

At Orion180, we believe the future of insurance belongs to organizations that can transform data into a deeper understanding of risk. Predictive modeling isn't a support function—it's a core strategic capability that helps us decide where to grow, how to price risk, how to optimize our portfolio, and how to create sustainable competitive advantage. As a Senior Predictive Modeling Scientist, you will play a pivotal role in helping Orion180 build and continuously refine its own view of risk. You'll develop advanced statistical and machine learning models that influence underwriting strategy, ratemaking, portfolio management, catastrophe risk assessment, claims forecasting, and business planning. Your work will help transform vast amounts of data into actionable insights that drive smarter decisions across the enterprise. This is a unique opportunity to join a growing organization where predictive modeling has direct visibility and measurable business impact. You'll collaborate with leaders across underwriting, actuarial, claims, and risk management while helping shape the next generation of analytical capabilities that support Orion180's expansion into new markets and products. Success in this role requires more than technical expertise. We're looking for someone who is curious, innovative, and comfortable tackling complex, ambiguous problems. Someone who can combine rigorous statistical thinking with practical business judgment, communicate insights effectively, and translate advanced analytics into strategies that improve performance and strengthen our understanding of risk. If you're passionate about machine learning, predictive analytics, forecasting, and developing models that directly influence business outcomes, we invite you to help shape the future of insurance at Orion180. The Senior Predictive Modeling Scientist will be a key member of Enterprise Risk Management, leading Orion180’s predictive modeling initiatives as we expand into new markets and evolve our product offerings. This role is both strategic and hands-on, ideal for someone passionate about leveraging advanced analytics to drive measurable business impact across underwriting, pricing, claims, catastrophe modeling, and portfolio optimization.

Requirements

Master’s degree in Statistics, Data Science, Mathematics, Computer Science, Actuarial Science, or a related quantitative field
Demonstrated predictive modeling experience, including 4+ years of experience for candidates holding a Master's
Demonstrated experience developing predictive models.
Proficiency in Python or R, plus SQL.
Strong communication skills with the ability to explain complex modeling concepts to underwriting, actuarial, and leadership stakeholders.

Nice To Haves

PhD in a related quantitative field (Statistics, Data Science, Mathematics, Computer Science, Actuarial Science)
At least 1 year of relevant predictive modeling experience acquired after completion of a PhD program.
Knowledge of pricing models, GLMs, and regulatory considerations.
Experience working in cloud environments (AWS, Azure, or GCP).
Experience with MLOps tools (MLflow, SageMaker, Vertex AI).
Background in GIS, geospatial modeling, or catastrophe modeling (wildfire, flood, wind).
Experience with time-series forecasting for claims, exposure, or weather-driven perils.
Experience building scalable data pipelines and automated model monitoring.
Familiarity with insurance model governance and documentation standards.

Responsibilities

Design, implement, and deploy statistical and machine-learning models (including time-series forecasting) to support underwriting, claims, pricing, and risk segmentation.
Partner with underwriting, claims, actuarial, and analytics teams to ensure models align with business objectives and regulatory requirements.
Create dashboards and visualizations that translate complex model outputs into actionable insights for decision-makers.
Maintain documentation, version control, and compliance with regulatory and internal standards.
Stay current with industry best practices, emerging modeling techniques, and new technologies.
Build reproducible, scalable data pipelines and automate model monitoring, validation, and reporting processes.