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hackajob is partnering with Verisk Analytics to fill this position. Create a profile to be automatically considered for this role—and others that match your experience.
Verisk is a leading analytics and data solutions provider that helps organizations better understand and manage risk. Drawing on deep domain expertise, proprietary data, and advanced analytics, Verisk supports decision‑making across insurance and related markets by translating complex information into actionable insights.
Verisk’s Catastrophe & Risk Solutions business unit develops analytical tools and models that help clients assess extreme and systemic risks. The group supports underwriting, portfolio management, pricing, and capital decisions by combining scientific research, data analytics, and domain expertise across natural and man‑made perils.
The Verisk Casualty Catastrophe Model addresses large‑scale casualty risks that can generate correlated losses across multiple insureds and industries. The model applies a catastrophe‑style framework to litigation‑driven and liability‑based risks, incorporating legal, regulatory, scientific, and behavioral dynamics to help clients understand accumulation, severity, and tail risk.
About the Role
We are hiring a Scientist I to join a growing research team focused on understanding casualty litigation as a systemic risk. This role is ideal for a MSc- or PhD trained researcher who enjoys deep investigation, mixed methods research, and applied analysis, and who wants their work to directly shape models, products, and thought leadership.
You will study casualty litigation risks across domains (emerging and legacy), translating complex legal, scientific, and regulatory information into structured, model ready insights. The role combines qualitative research (reading cases, synthesizing narratives) with quantitative analysis (building datasets, trends, and indicators).
This is a hands-on individual contributor role with high visibility and real-world impact.
• In this role, you will research casualty litigation risks across industries, perils, and time horizons, covering both emerging and historical risks. You will define litigation-driven risks as systemic liability events, framing them from a casualty catastrophe perspective that accounts for correlated claims, multi‑defendant exposure, and aggregation and accumulation dynamics. As part of this work, you will identify and document key event triggers, such as scientific or medical developments, regulatory or enforcement actions, manufacturing and design allegations, failure‑to‑warn claims, and whistleblower reports or investigations, and assess how these triggers influence litigation behavior and risk severity.
• You will develop and maintain a structured understanding of the litigation landscape by analyzing defendants and exposure channels, including manufacturers, operators, suppliers, vendors, service providers, and affiliates, as well as plaintiffs and claim structures such as individual claims, mass actions, class actions, municipalities, and governmental entities. You will create and refine high‑level taxonomies of liability theories, including product liability and design defect claims, negligence and failure‑to‑warn allegations, consumer protection theories, public nuisance arguments, and claims related to misrepresentation or fraud. In parallel, you will track procedural posture and venue patterns, identifying jurisdictions that disproportionately influence severity or settlement leverage, and monitoring filing trends, coordination mechanisms, and case progression stages.
• From a quantitative perspective, you will design, maintain, and analyze structured datasets capturing litigation cases, parties, venues, theories, and outcomes. You will use Python, R, and SQL to study litigation trends, detect clustering and emergence patterns, build indicators related to frequency and severity, and support model assumptions and scenario design. You will also apply AI‑based research tools to assist with classification, summarization, retrieval, and synthesis of large volumes of unstructured information, while ensuring outputs are validated and sources are traceable.
• You will translate research findings into model‑ready inputs, including event definitions, exposure pathways, key assumptions, and uncertainty ranges. You will collaborate closely with modelers, data scientists, actuaries, and product teams to support model development and enhancement, contribute to product content and documentation, and inform internal and external research publications. Your work will result in clear, high‑quality deliverables such as litigation landscape reports, emerging risk briefs, executive summaries, and presentations that communicate complex risk insights in a structured and actionable way.
Required
• MSc (PhD is preferred) in a STEM field, actuarial sciences, economics, computer sciences, or other relevant disciplines.
• Strong qualitative and quantitative research skills, including:
o synthesizing complex source material into structured insights
o analyzing data and trends using statistical or analytical tools
• Proficiency in Python and/or R, plus SQL.
• Experience using AI-based research tools (e.g., for text analysis, classification, retrieval).
• Interest in and aptitude for the casualty risk and litigation domain, including long-tail and systemic liability risks.
• Excellent written and verbal communication skills.
• Ability to work independently as an individual contributor in an ambiguous research environment.
Nice to Have / Preferred
• Background in litigation analytics, mass tort research, nuclear verdicts, regulatory risk, or insurance-related research.
• Familiarity with casualty insurance concepts:
o aggregation, clash, severity, long-tail exposure
• Experience building research taxonomies, trackers, or knowledge repositories.
• Prior collaboration with modeling, actuarial, legal, or product teams.
Why This Role
• High-impact research that directly informs models and products
• Opportunity to publish and contribute to thought leadership
• Broad exposure across casualty domains, not limited to a single Named Peril Group
• Designed for researchers who enjoy both deep thinking and applied impact
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hackajob is partnering with Verisk Analytics to fill this position. Create a profile to be automatically considered for this role—and others that match your experience.