About Wildfire Defense Systems, Inc.
Wildfire Defense Systems (WDS) is the North American leader in insurer wildfire services, combining professional wildfire consulting and loss intervention services with Qualified Insurance Resources (QIR). Founded in 2001 to fulfill initial-attack assignments for the US Forest Service, the company added professional services support in 2005 and launched wildfire loss-intervention programs for insurers in 2008.
WDS Wildfire Loss Intervention Service (WDS Service) applies a comprehensive approach to reducing risk in catastrophic exposure on an industry scale, decreasing the average annual loss (AAL) for our insurer clients, and providing peace of mind to their policy holders during wildfire events. WDS provides these industry-leading services to insurer clients and their policy holders across a 22-state service area.
The Associate Actuary is a pivotal technical and collaborative role within WD InsurTech's Actuarial Pricing & Analytics function. This position sits at the intersection of traditional actuarial science and modern data science, responsible for designing, building, and maintaining advanced pricing frameworks across WD InsurTech's property and casualty insurance and reinsurance portfolios.
The incumbent will leverage statistical modeling, machine learning, and predictive analytics to develop rate indications, evaluate price adequacy, and support underwriting strategy across commercial, specialty, and reinsurance lines. Working in close partnership with Underwriting, Claims, Reserving, Catastrophe Modeling, and senior leadership, this role drives data-informed decision-making that directly influences portfolio profitability, growth strategy, and risk selection.
This is a high-impact individual contributor role with mentorship responsibilities, offering the opportunity to shape the evolution of pricing capabilities at WD InsurTech through the adoption of modern tooling, advanced analytics, and scalable model infrastructure.
Your duties may include, but are not limited to:
Pricing Model Development & Data Science
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Job RequirementsDesign, build, and validate predictive pricing models using statistical and machine learning methods, tailored to P&C and reinsurance lines.
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Develop and maintain end-to-end pricing pipelines, from raw data ingestion and feature engineering through model training, validation, deployment, and monitoring.
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Apply credibility theory, loss development, trend analysis, and exposure rating in combination with data science techniques to produce robust rate indications.
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Develop pricing tools and actuarial models that support estimation of price adequacy, rate change measurement, and portfolio segmentation across all assigned lines of business.
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Automate recurring pricing workflows and reporting using Python, R, or SQL to improve efficiency, consistency, and auditability.
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Maintain version control and documentation of models, code, and assumptions in alignment with company standards and actuarial professionalism guidelines.
Rate Indications & Regulatory Filings
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Perform regular rate indications for all assigned states and programs using standard actuarial methods, including loss ratio, pure premium, and experience rating approaches.
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Recommend rate actions with a focus on long-term profitability, market competitiveness, and regulatory feasibility.
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Research peer company filings and develop actuarially supportable rates in emerging or data-sparse segments.
Portfolio & Transactional Pricing
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Perform portfolio-level and individual account/treaty pricing analyses across property, casualty, specialty, and reinsurance lines.
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Assist senior staff in review and pricing of new program opportunities, facultative submissions, and treaty structures.
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Identify areas of strong profitability for growth and underperforming segments for re-underwriting, contraction, or exit.
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Develop segmental and trend analyses to support underwriting decisions and strategic planning.
Performance Monitoring & Analytics
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Monitor pricing performance metrics including rate change, price adequacy, loss ratio emergence, and plan-vs-actual comparisons.
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Design and maintain dashboards and reporting tools that deliver actionable pricing intelligence to underwriting teams and senior leadership.
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Conduct experience studies, parameter reviews, and assumption updates on a defined schedule; present findings and recommendations to key committees.
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Support reserve reviews for assigned segments and respond to ad hoc data calls from internal and external stakeholders.
Cross-Functional Collaboration & Stakeholder Engagement
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Liaise with Underwriting, Claims, Reserving, Exposure Management, and the Managing Actuary to ensure pricing reflects all relevant loss, exposure, and operational information.
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Provide training and technical guidance to underwriters on pricing methodology, rate change requirements, and model outputs.
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Clearly and concisely present findings, model results, and recommendations to diverse audiences including senior actuarial management, underwriting teams, compliance officers, MGU/MGA partners, and regulators.
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Ensure data quality, integrity, and completeness in pricing tools; identify data gaps and champion improvements.
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Build and maintain constructive working relationships with internal and external partners identified as key to the role.
Process Improvement & Governance
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Identify opportunities to improve pricing processes, methodologies, and data infrastructure; contribute to and lead delivery of such improvements.
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Maintain pricing model documentation at required frequency, obtaining appropriate managerial sign-offs and adhering to model governance standards.
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Understand and apply the company's pricing quality assurance process; support underwriting controls related to pricing and relevant regulatory principles.
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Keep abreast of industry best practices, emerging data science techniques, new actuarial initiatives, and changes in regulatory requirements.
Talent Development & Team Contribution
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Operate as an effective team member by supporting colleagues and contributing to overall team and business objectives.
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Pursue actuarial examination progress toward FCAS designation and engage in ongoing professional development.
Basic Qualifications (i.e. education, certifications, etc…)
Educational Qualifications
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Bachelor's degree in Actuarial Science, Mathematics, Statistics, Data Science, Computer Science, Economics, or a closely related quantitative field required.
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Master's degree (M.S. or M.A.) in Actuarial Science, Statistics, Data Science, Applied Mathematics, or a related discipline strongly preferred.
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Active pursuit of the Casualty Actuarial Society (CAS) Fellowship (FCAS) designation required; ACAS or near-ACAS standing preferred.
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Supplemental credentials such as CPCU, ARe, CERA, or formal data science certifications (e.g., AWS ML Specialty, Google Professional Data Engineer) are a plus.
Experience
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3 or more years of progressive actuarial experience in property and casualty insurance and/or reinsurance pricing required.
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Demonstrated experience building and deploying predictive models and pricing tools in a commercial or specialty insurance environment.
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Experience with rate filing preparation, DOI interactions, and regulatory compliance preferred.
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Familiarity with MGU/MGA, program, treaty, or facultative reinsurance business structures is a plus.
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Prior experience working in or alongside data engineering or data science teams preferred.
Key Certifications (if applicable)
Actuarial & Technical Knowledge
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Strong command of P&C actuarial pricing fundamentals: loss development, trend analysis, credibility theory, exposure rating, retrospective rating, and experience modification.
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Solid understanding of reinsurance pricing concepts including burning cost, experience rating, exposure rating, and swing plans.
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Knowledge of catastrophe modeling concepts and their interaction with property pricing and portfolio management.
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Understanding of insurance accounting, loss reserving concepts, and their relationship to pricing adequacy.
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Familiarity with actuarial standards of practice (ASOPs) and professional guidelines relevant to pricing.
Data Science & Programming
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Advanced proficiency in Python required.
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Proficiency in R strongly preferred.
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Strong SQL skills for data extraction, transformation, and analysis from relational databases.
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Experience with machine learning frameworks and techniques: GLMs, GBMs, gradient boosting, random forests, clustering, neural networks, and model ensembling.
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Familiarity with MLOps concepts including model versioning, monitoring, feature stores, and deployment pipelines.
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Experience with cloud platforms (AWS, Azure, or GCP) and big data tools (Spark, Databricks, Snowflake) preferred.
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Advanced proficiency in Microsoft Excel, including Power Query, pivot tables, and financial modeling; VBA or Excel automation experience a plus.
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Experience with version control systems (Git/GitHub/GitLab) and collaborative development workflows.
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Familiarity with data visualization and BI tools such as Tableau, Power BI, or Looker preferred.
Analytical & Problem-Solving
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Exceptional analytical and quantitative skills with strong attention to detail, accuracy, and numerical reasoning.
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Ability to identify, frame, and solve complex, ambiguous problems using structured thinking and creative approaches.
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Demonstrates intellectual curiosity and a drive to expand technical knowledge independently.
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Capable of synthesizing macro and micro-level perspectives to draw actionable insights from complex data.
Communication & Interpersonal
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Exceptional written and verbal communication skills, with the proven ability to translate complex technical results into clear, actionable insights for non-technical audiences.
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Comfortable presenting findings and recommendations to senior leadership, underwriting management, regulatory bodies, and external partners.
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Strong collaboration and relationship-building skills; able to operate effectively in cross-functional, matrix environments.
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Ability to provide constructive mentorship and training to junior team members.
Organizational & Professional
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Strong organizational, prioritization, and time management skills; able to manage multiple concurrent deliverables and meet deadlines in a dynamic environment.
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Proactive, self-motivated, and highly driven with a continuous improvement mindset.
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Committed to maintaining the highest standards of actuarial professionalism and ethical conduct.
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Demonstrated ability to navigate existing systems and processes to accomplish business objectives efficiently.
This position is eligible for a hybrid or remote work arrangement, subject to WD InsurTech's flexible work policy and business needs. Occasional travel may be required for team meetings, industry conferences, regulatory hearings, or client engagements. The role operates in a standard office or remote office environment with extended periods of computer use.
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Prolonged periods sitting at a desk and working on a computer
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Must be able to lift 15-lbs.
WD InsurTech is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other protected classification under applicable law.
This job description is intended to convey information essential to understanding the scope of the position and is not an exhaustive list of skills, efforts, duties, responsibilities, or working conditions associated with the role. WD InsurTech reserves the right to modify duties and responsibilities as business needs evolve
The pay range for this role is:
74,000 - 90,000 USD per year(Remote (United States))
