Applied Scientist II

Responsibilities

• Build and advance our most sensitive and business-critical ML and GenAI models that power underwriting decisions and risk selection.
• Drive and execute ML projects end-to-end: problem framing, data exploration, feature engineering, model design, prototyping, offline/online evaluation, deployment, and monitoring.
• Design and implement ML pipelines for data preprocessing, feature engineering, model training, hyperparameter tuning, and model evaluation, enabling rapid and reproducible experimentation.
• Apply state-of-the-art ML and GenAI workflows (e.g., gradient-boosted trees, deep learning, LLMs, prompt engineering, transfer learning) to improve underwriting accuracy, automation, and decision support.
• Own model quality and robustness by defining success metrics, running ablations and diagnostics, and iterating to outperform prior baselines.
• Survey and incorporate recent advances in ML/GenAI research into our core underwriting capabilities, balancing scientific rigor with practical constraints.
• Collaborate closely with underwriters, product, data, and engineering partners to clarify requirements, align on tradeoffs, and ensure models integrate cleanly into production workflows.
• Communicate methods and results clearly through documentation, presentations, and design reviews; share learnings and patterns that level up the broader team.
• Contribute to a culture of scientific and data excellence by bringing mature empathy, best practices, and lightweight processes to experimentation, code review, and model governance.

Skills and Qualifications

• Ph.D. or MS in a quantitative or computational field (e.g., Computer Science, Statistics, Applied Math, Electrical Engineering) or equivalent practical experience.
• 5+ years of full-time experience developing and deploying ML- and data-based solutions in production.
• Practical, hands-on experience with supervised and unsupervised learning methods, including model selection, regularization, and calibration.
• Expertise in statistical analysis methods, especially regression analysis, statistical inference, and forecasting / time-series methods.
• Strong proficiency in Python and core ML libraries (e.g., scikit-learn, XGBoost/LightGBM, PyTorch/TensorFlow) and SQL for working with large, messy datasets.
• Experience with experiment design and evaluation (e.g., A/B tests, offline metrics vs. online KPIs, guardrails) and with ensuring reproducible results.
• Comfortable and effective in ambiguous problem spaces; demonstrated ability to own and drive projects with minimal oversight and process.
• Exceptional written and oral communication skills, with the ability to explain complex modeling choices and tradeoffs to technical and non-technical stakeholders.
• Minimum 1+ year of experience in insurance underwriting modeling (pricing, risk scoring, eligibility, or related applications).

Bonus Points

• Experience with modern GenAI techniques relevant to underwriting (e.g., using LLMs for document understanding, unstructured-to-structured extraction, or underwriter copilot workflows).
• Familiarity with model governance in regulated environments, including documentation, validation, monitoring, and change management.
• Experience with ML orchestration and MLOps tools (e.g., Airflow, Prefect, MLflow, SageMaker) for managing training, deployment, and monitoring at scale.
• Exposure to causal inference or uplift modeling for understanding the impact of underwriting or guideline changes.
• Experience working with cyber or P&C insurance data (exposures, limits, deductibles, claims, external risk signals).

Compensation

US base salary ranges from $115,900/year to $155,250/year depending on geographic market.