Credit risk modelling data scientist developing and maintaining models for Desjardins. Guiding risk quantification, credit decisions, compliance, and practitioner - led modelling projects.
RI
Senior Data Scientist – Longevity, Biometric Assumptions
Posted 8 months ago
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
Requirements
- 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
Benefits
- Health insurance
- Retirement plans
- Annual bonus plan
- Long-term equity incentive plan
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