Applied Scientist II
Core
Build and improve machine learning and GenAI models that power underwriting decisions, risk selection, and pricing for cyber insurance.
Role type
Applied Scientist II (ML/GenAI)
Builds
Production ML and GenAI models for cyber risk underwriting
Domain
Insurance (Cyber/P&C) + Machine Learning
Deliverable
production ML models
Required skills
Python, SQL, supervised/unsupervised learning, statistical analysis (regression, inference, time-series), model deployment, experiment design (A/B tests), LLMs/prompt engineering, gradient-boosted trees, deep learning
Preferred skills
GenAI for document understanding, MLOps tools (Airflow, MLflow), causal inference, cyber insurance data experience
Technologies
PyTorch, TensorFlow, XGBoost, LightGBM, scikit-learn, Airflow, Prefect, MLflow, SageMaker
Responsibilities
Build and advance sensitive ML/GenAI models for underwriting; drive end-to-end ML projects from framing to monitoring; design ML pipelines for preprocessing and training; apply SOTA ML/GenAI workflows to improve accuracy; own model quality via metrics and diagnostics; survey recent research advances; collaborate with underwriters and product teams; communicate methods and results to stakeholders.
Seniority
Mid-Senior, hands-on IC