Applied Scientist II

<div class="content-intro"><h4><span style="color: rgb(0, 0, 0);">About us</span></h4> <p>Coalition is the world's first Active Insurance provider designed to help prevent digital risk before it strikes. Founded in 2017, Coalition combines comprehensive insurance coverage and innovative cybersecurity tools to help businesses manage and mitigate potential cyberattacks. </p> <p>Opportunities to make an impact with bold thinking are real—and happening daily at Coalition.</p></div><h4><span style="color: rgb(0, 0, 0);">About the role</span></h4> <div> <p><span style="color: rgb(0, 0, 0);">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.</span></p> <p><span style="color: rgb(0, 0, 0);">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.</span></p> </div> <h4><span style="color: rgb(0, 0, 0);">Responsibilities</span></h4> <ul> <li style="color: rgb(0, 0, 0);"><span style="color: rgb(0, 0, 0);">Build and advance our most sensitive and business-critical ML and GenAI models that power underwriting decisions and risk selection.</span></li> <li style="color: rgb(0, 0, 0);"><span style="color: rgb(0, 0, 0);">Drive and execute ML projects end-to-end: problem framing, data exploration, feature engineering, model design, prototyping, offline/online evaluation, deployment, and monitoring.</span></li> <li style="color: rgb(0, 0, 0);"><span style="color: rgb(0, 0, 0);">Design and implement ML pipelines for data preprocessing, feature engineering, model training, hyperparameter tuning, and model evaluation, enabling rapid and reproducible experimentation.</span></li> <li style="color: rgb(0, 0, 0);"><span style="color: rgb(0, 0, 0);">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.</span></li> <li style="color: rgb(0, 0, 0);"><span style="color: rgb(0, 0, 0);">Own model quality and robustness by defining success metrics, running ablations and diagnostics, and iterating to outperform prior baselines.</span></li> <li style="color: rgb(0, 0, 0);"><span style="color: rgb(0, 0, 0);">Survey and incorporate recent advances in ML/GenAI research into our core underwriting capabilities, balancing scientific rigor with practical constraints.</span></li> <li style="color: rgb(0, 0, 0);"><span style="color: rgb(0, 0, 0);">Collaborate closely with underwriters, product, data, and engineering partners to clarify requirements, align on tradeoffs, and ensure models integrate cleanly into production workflows.</span></li> <li style="color: rgb(0, 0, 0);"><span style="color: rgb(0, 0, 0);">Communicate methods and results clearly through documentation, presentations, and design reviews; share learnings and patterns that level up the broader team.</span></li> <li style="color: rgb(0, 0, 0);"><span style="color: rgb(0, 0, 0);">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.</span></li> </ul> <h4><span style="color: rgb(0, 0, 0);">Skills and Qualifications</span></h4> <ul> <li style="color: rgb(0, 0, 0);"><span style="color: rgb(0, 0, 0);">Ph.D. or MS in a quantitative or computational field (e.g., Computer Science, Statistics ... (truncated, view full listing at source)