Senior Data Scientist - (Open to Remote)

Reinsurance Group of America, Incorporated•Chesterfield, MO

17d•Hybrid

About The Position

You desire impactful work. You’re RGA ready RGA is a purpose-driven organization working to solve today’s challenges through innovation and collaboration. A Fortune 200 Company and listed among its World’s Most Admired Companies, we’re the only global reinsurance company to focus primarily on life- and health-related solutions. Join our multinational team of intelligent, motivated, and collaborative people, and help us make financial protection accessible to all. Overview The Senior Data Scientist is a strong contributor to the Biometric Assumptions Team, which provides analytical expertise for the global development of data-driven solutions in longevity product development and pricing. This role involves combining technical skills and predictive modelling to contribute to innovative model solutions and collaborates on project-teams consisting of data scientists, actuaries, IT, and business developers. The Senior Data Scientist will focus on internal mortality assumption development for longevity markets by leveraging new and existing data sources through exploratory analysis, insights, model maintenance, and model R&D. Location: Ideally located in a hybrid work-arrangement in one of RGA's offices - St. Louis, USA or Toronto/Montreal offices, Canada or London, UK or in a fully remote work-arrangement.

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

Nice To Haves

Experience with longevity product design / pricing / experience studies / assumption development
Re/insurance industry experience
Master’s degree or PhD in Statistics, Actuarial Science, Economics, or related field
Actuarial designation (ASA/FSA; AIA/FIA)
4 or more years of experience with statistical modeling for insurance
Familiar with actuarial modeling platforms (AXIS, Prophet, Exp Studies etc.)
Basic data engineering capabilities (Python, Scala)
Basic machine learning models/concepts (SVM’s, GAN’s, Neural Networks/Deep Learning, Naive Bayes, NLP) and/or basic statistical concepts for feature engineering for dimensionality reduction such as PCA’s, SVD’s, and clustering
Proactive use of insurance expertise & actuarial concepts to feature engineering and model evaluation

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