Applied Scientist, Coalition

• 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)

Desirable

• We are hiring an Applied Scientist II to build and improve the machine learning and GenAI models that power our underwriting decisions
• You will take ownership of high-impact modeling problems end-to-end
• This includes framing and data exploration through model design, evaluation, deployment, and monitoring, directly influencing how we assess and price cyber risk
• You’ll work closely with underwriters, product managers, and engineers to design robust pipelines, experiment with state-of-the-art ML/GenAI techniques, and ship models that meaningfully move business metrics
• Your work will turn complex insurance and security signals into reliable decisioning systems, helping Coalition write better business at scale while pushing the frontier of AI in underwriting
• 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

• Application Updates: Consistent with our commitment to transparency, if you are interviewed for this role, we will provide a status update on your application within 45 days of your final interview