About the role

Senior Data Scientist contributing to analytical solutions for longevity product development. Working with RGA's multinational teams on data-driven decisions and statistical modeling.

Responsibilities

Lead, design, create, and interpret end-to-end models with a typical focus on mortality within longevity markets.
Support Pricing team with insights from large datasets and support efforts to adopt robust bespoke assumptions in quotes.
Evaluate new external data sources and explore new applications of non-traditional data sources for RGA in its various regions.
Participate in the development and enhancement of underlying processes and recommends improvements in data analysis/modeling best practice standards
Communicate with a variety of stakeholders at various levels of seniority
Offer risk management skills to any data processing or modeling exercise: Understand business context & where material scope for error lies
Adhere to professional standards, best practices, and ethical guidelines
Understand the strengths and limitations of a modeling approach
Have a strong understanding on tools / techniques their actuarial peers will not have had a formal education in such as: Understand applications, risks, transparency, quality assurance & peer review, and ethical guidelines
Stay abreast of new techniques, but focusing on practical applications
Liaise with RGA's data scientists across the globe about more sophisticated data science applications
Contribute to RGA's global analytics community, routinely sharing, maintaining consistency of approach

Bachelor's degree in Math, Finance, Economics, Statistics, Actuarial Science, Computer Science or related field
6+ years of experience developing statistical models (Regression, Decision Trees, Time Series, etc.)
Statistical programs/languages (R or Python)
Spreadsheet skills (Excel/VBA) and database applications (SQL, Snowflake, Oracle,...)
Advanced predictive modeling skills: Tree-based models, GLMs, GAMs, etc.; Cross-Validation, Residuals and model diagnostics; Basic Statistical concepts for feature engineering (e.g. percentiles, standardization, correlations, risk ratios / chi-square test, splines, and other non-linear transformations)
Advanced exploratory data analysis skills - Plots and graphics (BI/ggplot)
Ability to compile, analyze, refine, model and interpret very large data sets as well as the ability to incorporate expert judgment into statistical modeling techniques
Transform data to enhance its predictive value (feature engineering)
Advanced ability to translate business needs and problems into viable/accepted solutions
Advanced investigative, analytical, and problem-solving skills