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Key Responsibilities and Required Skills for Actuarial Analyst

💰 $65,000 - $110,000

ActuarialInsuranceFinanceAnalyticsRisk Management

🎯 Role Definition

We are seeking an experienced Actuarial Analyst to support pricing, reserving, capital and risk initiatives across insurance and reinsurance lines. The Actuarial Analyst will design and validate statistical and actuarial models, produce credible loss and premium forecasts, prepare regulatory and management reporting, and translate technical analysis into actionable business recommendations. This role requires strong quantitative modeling skills (GLMs, stochastic simulations), proficiency in data manipulation (SQL, Python, R, Excel/VBA), and demonstrated experience with reserving, pricing, triage of book profitability, and regulatory filings (IFRS 17, US GAAP, state rate filings). Ideal candidates will be exam-progressing with the SOA/CAS/IFoA or equivalent actuarial credentials and comfortable communicating findings to underwriting, finance, and leadership stakeholders.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Junior Actuarial Analyst / Intern with 0–2 years of actuarial exposure
  • Data Analyst or Statistical Analyst with hands-on insurance data experience
  • Recent graduate in Actuarial Science, Mathematics, Statistics, Economics, or Finance with actuarial exam credit

Advancement To:

  • Senior Actuarial Analyst / Pricing Actuary
  • Actuary (ACAS/ASA level) focusing on Pricing, Reserving or Capital Modeling
  • Manager, Actuarial Analytics or Product Pricing Lead

Lateral Moves:

  • Product Management (Insurance Product Manager)
  • Risk Management Analyst (Capital & ORSA)
  • Data Science / Predictive Analytics within the insurer or brokerage

Core Responsibilities

Primary Functions

  • Lead pricing analysis projects by building, testing, and implementing generalized linear models (GLMs) and advanced predictive models (GBMs, XGBoost) to produce rate indications, segmentation, and experience-rated premium recommendations for personal and commercial insurance lines.
  • Prepare and maintain loss reserving analyses including development of loss triangles, paid and incurred development methods, Bornhuetter-Ferguson techniques, stochastic reserving and capital margin calculations for quarterly and annual reporting.
  • Perform exposure and premium trend analysis, credibility weighting and experience studies to identify rating drivers and refine pricing factors for new business and renewal strategies.
  • Support statutory and GAAP/IFRS financial reporting by providing schedule inputs, reserve reconciliation, parameter documentation, and sensitivity analysis for reserve and premium recognition.
  • Design and run Monte Carlo simulations and scenario analyses to quantify reserve volatility, tail risk, and capital requirements under adverse scenarios for ORSA, enterprise risk management and solvency assessments.
  • Prepare rate filings, actuarial memoranda and regulatory support packages; coordinate with compliance and state filing teams to respond to inquiries and justify pricing changes to regulators.
  • Develop reinsurance analysis including structure optimization, cost/benefit of layers, stop-loss attachments, quota share simulations and the impact on net retained exposures and capital.
  • Conduct loss cost and loss ratio analyses, segmentation performance reviews and competitive benchmarking to support underwriting strategy and product profitability improvements.
  • Validate and back-test actuarial models, document model governance, and participate in periodic model validation and audit processes to ensure accuracy, transparency and reproducibility.
  • Extract, clean and transform large datasets from policy, claims, and billing systems using SQL, Python or R to create analysis-ready datasets and automated reporting pipelines.
  • Build, maintain and automate dashboards and executive reports (Tableau, Power BI, or internal BI tools) to visualize reserves, loss emergence, pricing metrics and portfolio trends for cross-functional stakeholders.
  • Collaborate with underwriters, claims, finance and IT to translate business requirements into model inputs, assumptions and KPIs; present findings and recommended actions to non-technical audiences.
  • Support capital modeling and pricing for new products, endorsements and territories including profitability projections, burn analyses and break-even pricing.
  • Produce periodic experience studies (morbidity/mortality, lapse, severity) and incorporate results into assumption setting and reserving methodology.
  • Implement and improve actuarial tools and workflows (Excel models, VBA automation, Prophet/AXIS/MG-ALFA where applicable) to increase reproducibility and reduce manual error.
  • Monitor emerging regulatory changes (IFRS 17, US GAAP updates, NAIC model changes) and incorporate impacts into actuarial models and financial reporting.
  • Participate in claim forecasting and settlement pattern analysis, including large loss identification and monitoring for potential reserve strengthening.
  • Estimate and model loss development and tail risk for long-tail lines (e.g., liability, workers’ compensation) using parametric and non-parametric techniques.
  • Conduct profitability and sensitivity analyses for account-level pricing decisions and retention strategy using policy-level predictive models and segmentation.
  • Support mergers, acquisitions and portfolio acquisitions/divestitures with actuarial due diligence, purchase accounting (PPA) inputs, and post-close integration of pricing and reserving assumptions.
  • Provide ad hoc analytical support for litigation, expert witness preparation, and actuarial input into contractual negotiations and policy wording reviews.
  • Mentor junior analysts through code and model reviews, exam guidance, and practical training on actuarial best practices and documentation standards.

Secondary Functions

  • Support development of enterprise-wide data dictionaries and actuarial data lineage documentation to improve auditability and model transparency.
  • Collaborate with data engineers and IT to prioritize data ingestion needs, define business rules for policy/claims reconciliation, and implement robust ETL processes.
  • Participate in continuous improvement initiatives: automate repetitive processes, optimize model runtime, and contribute to the actuarial automation roadmap.
  • Contribute to cross-functional projects (product launches, digital distribution initiatives, analytics center of excellence) to embed actuarial insight into broader business decision-making.
  • Work with external vendors and consultants on model implementation, third-party data integration, or reinsurance pricing tools; review vendor assumptions and outputs for reasonableness.
  • Support actuarial governance by documenting model assumptions, limitations, validation results and ensuring compliance with internal model risk policies and professional standards.
  • Assist in preparing materials and analyses for audit, actuarial peer review, and regulatory examinations; respond to data and methodology information requests in a timely manner.
  • Stay current with actuarial research, industry publications and best practices; propose and pilot new modeling techniques or data sources that add predictive value.
  • Contribute to ESG and climate-related impact modeling where relevant to claims or underwriting exposure for long-term risk assessment.
  • Provide subject-matter expertise for cross-selling, retention and marketing teams to help translate actuarial insights into customer-targeted strategies.

Required Skills & Competencies

Hard Skills (Technical)

  • Proficiency in actuarial modeling: generalized linear models (GLMs), credibility theory, loss development, Bornhuetter-Ferguson and stochastic reserving techniques.
  • Programming and data manipulation: Python (pandas, scikit-learn), R (tidyverse, mgcv), and strong SQL skills for querying relational databases.
  • Advanced Excel: pivot tables, array formulas, VBA/macros, and building robust model spreadsheets with version control and documentation.
  • Experience with actuarial and reserving systems such as Prophet, AXIS, Moses, MG-ALFA, or equivalent production tools.
  • Familiarity with IFRS 17, US GAAP reserving conventions, statutory accounting, and preparation of actuarial exhibits and regulatory filings.
  • Statistical and predictive analytics: model selection, cross-validation, feature engineering, and performance metrics (RMSE, AUC, Gini).
  • Monte Carlo simulation, scenario analysis and capital modeling (economic capital, stress testing, ORSA).
  • Data visualization and reporting tools: Tableau, Power BI or equivalent for dashboards and executive reporting.
  • Knowledge of reinsurance structures and analytics (proportional, excess of loss, finite reinsurance) and tendency to simulate net/net of reinsurance results.
  • Version control and reproducibility: Git, CI/CD awareness, and automated testing practices for analytics workflows.
  • Understanding of claims reserving concepts for long-tail lines (e.g., workers’ comp, liability) and short-tail lines (e.g., auto, property).
  • Familiarity with regulatory rate filing processes and actuarial memos supporting pricing changes.

Soft Skills

  • Clear, persuasive communication: translate technical analysis into business-focused recommendations for underwriting, finance and senior leadership.
  • Strong problem-solving and critical-thinking ability to identify drivers of loss and sources of model error.
  • Attention to detail and commitment to high-quality documentation and model governance.
  • Collaborative teamwork and stakeholder management skills; proven ability to work cross-functionally with underwriting, claims, IT and finance.
  • Time management and prioritization in a fast-paced environment with competing deadlines (quarterly reporting, rate filings).
  • Intellectual curiosity and continuous learning mindset—actively pursuing actuarial exams or professional development.
  • Ethical judgment and adherence to professional actuarial standards and codes of conduct.
  • Adaptability to changing data environments, regulatory expectations and business priorities.
  • Coaching and mentoring orientation to develop junior actuarial talent.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor’s degree in Actuarial Science, Mathematics, Statistics, Economics, Finance, Data Science or a closely related quantitative discipline.

Preferred Education:

  • Bachelor’s or Master's degree with strong coursework in probability, statistical modeling, linear algebra and econometrics; Master's degree or advanced quantitative certification preferred.
  • Progress toward actuarial credentials (SOA, CAS, IFoA) such as passing multiple preliminary actuarial exams; ACAS/ASA or equivalent designation preferred for mid-level roles.

Relevant Fields of Study:

  • Actuarial Science
  • Mathematics / Applied Mathematics
  • Statistics
  • Economics
  • Data Science / Computer Science
  • Finance

Experience Requirements

Typical Experience Range: 1–5 years of actuarial or quantitative experience, depending on seniority of role.

Preferred:

  • 2+ years experience in insurance pricing, reserving, capital modeling or predictive analytics within property & casualty, life, or health insurance lines.
  • Demonstrated experience producing rate filings, reserve analyses, or financial reporting inputs (IFRS 17/US GAAP).
  • Practical exposure to production actuarial systems (e.g., Prophet, AXIS), and programming in Python, R and SQL.
  • Evidence of progressive responsibility: leading small analytics projects, mentoring junior staff, and presenting to business stakeholders.
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