We are seeking a hands-on Actuarial Data Analyst to develop, maintain, and optimize actuarial models, processes, and analytical tools. This role will work closely with actuaries and other stakeholders to support month-end processes, rate change calculations, and ad-hoc analyses, serving as a liaison between the Information technology team and the actuarial team. The ideal candidate has a strong technical background in Python, R, and SQL, with experience in NoSQL databases, Power BI, and Excel. This role requires a mix of actuarial analysis and data modeling skills, ensuring accuracy and efficiency in delivering data-driven insights.
Key Responsibilities & Maintenance:
– Develop and maintain actuarial models and data-driven processes using Python, R, and SQL to support insurance pricing, reserving, and risk management.
– Implement and enhance month-end processes, rate change calculations, and ad-hoc analyses with a focus on completeness, accuracy, and consistency to ensure data is of the highest quality.
– Work with the Actuarial and Financial Planning and Analysis (FP&A) teams to automate and improve model performance using Python-based scripting and automation. – Ensure accuracy, consistency, and efficiency of actuarial models and methodologies.
– Support reserving analysis to estimate unpaid claim liabilities primarily in partnership with internal and external actuaries.
– Develop and maintain loss development triangles and incurred but not reported (IBNR) calculations both based on financial and operational data (e.g., claims closing ratios).
– Support the development and validation of actuarial assumptions for pricing, reserving, and forecasting.
– Develop and regularly report on rate change calculations including bifurcation of exposure changes from pure rate by line of business.
– Conduct stress testing and scenario analysis to assess financial impacts.
– Develop, update, and maintain models for predictive analytics, profitability analysis, and business planning.
– Assist in forecasting financial performance and evaluating risk exposure. Data Management & Analysis
– Write complex queries in SQL to extract and transform data, leveraging Python for advanced data processing and automation.
– Build queries with a controls-oriented focus to ensure accuracy, completeness, Analyze large datasets using Python libraries such as Pandas and NumPy to identify trends, patterns, and opportunities for optimization.
– Develop and implement Python scripts to automate data processing, actuarial calculations, and reporting workflows.
– Work closely with the Actuarial and FP&A teams to support financial planning, forecasting, and profitability analysis by providing robust data models and insights. – Partner with Data Engineers to optimize data workflows and ensure seamless data ingestion.
– Communicate technical results and methodologies to non-technical stakeholders in a clear and concise manner.
