Lead the design, build, and validation of quantitative models (descriptive, predictive, optimization/simulation) for underwriting automation and other business functions.
Develop and deploy ML solutions that accelerate quote-to-bind processes, automate risk assessment, and enable straight-through processing for standard risks.
Create sophisticated risk and customer segmentation strategies; build real-time scoring engines to support instant decisioning.
Pull, clean, and manage datasets; design models using advanced statistical and machine learning concepts (e.g., regression, GLMs, decision trees, ensemble modeling etc.).
Own business outcomes (OKRs and KPIs) and communicate results and insights to business leaders and stakeholders.
Perform research and development on new algorithms, modeling concepts, and technical tools; recommend adoption of new approaches into our analytics toolkit.

Requirements

5-7 years as Data Scientist or related technical role in a delivery oriented environment required.
Experience within the insurance industry and insurance data preferred.
5-7 years of experience building and deploying machine learning models in production environments with proven track record of building models that impact business KPIs
Advanced proficiency in Python and ML frameworks (scikit-learn, XGBoost, TensorFlow, PyTorch).
Expert proficiency in data management programming (SQL) and real-time data processing pipelines.
Experience integrating and working with third-party data APIs and ETL pipelines.
Strong written and oral communication skills; ability to present to senior executives and non-technical stakeholders.
Well-organized and able to lead projects from a technical standpoint.
Experience leading projects using CRISP-DM or similar frameworks.
Experience with insurance data and industry-specific modeling.
Knowledge of AI-powered development tools (e.g., GitHub Copilot) for accelerated model development.
Proven expertise in processing automation and workflow optimization using ML/AI.
Mastery of advanced statistical and machine learning concepts, including regression, GLMs, decision trees, ensemble modeling, clustering, and SVM.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

machine learningPythonscikit-learnXGBoostTensorFlowPyTorchSQLdata managementstatistical modelingdata processing

communication skillsproject leadershiporganizational skillspresentation skills