Associate Data Science Actuary

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 Associate Data Science Actuary, Biometric Assumptions is a qualified actuary with data science capabilities. The Biometric Assumptions Team 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 Associate Data Science Actuary 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: The successful candidate will ideally be located at RGA's HQs in Chesterfield, Missouri in a hybrid work arrangement; however, RGA may consider offering relocation assistance or possibly allow a fully-remote work arrangement for exceptionally qualified candidates.

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

  • 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

• FSA accreditation or equivalent

• Minimum of 6 or more years of actuarial experience in life insurance/reinsurance/pension risk transfer

• 2+ years statistical on-the-job modeling experience (not exam based) for insurance or related applications (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)

  • Proactive use of insurance expertise & actuarial concepts to feature engineering and model evaluation

• 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

Preferred

• Experience with longevity product design / pricing / experience studies / assumption development

• Reinsurance industry experience

• Master’s degree or PhD in Statistics, Actuarial Science, Economics, or related field

• 4+ 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

#LI-DL1 #LI-HYBRID

Compensation Range:

$114,750.00 - $175,450.00 Annual Base Salary

What you can expect from RGA:

• Gain valuable knowledge from and experience with diverse, caring colleagues around the world.

• Enjoy a respectful, welcoming environment that fosters individuality and encourages pioneering thought.

• Join the bright and creative minds of RGA, and experience vast, endless career potential.

Compensation Range:

Base pay varies depending on job-related knowledge, skills, experience and market location. In addition, RGA provides an annual bonus plan that includes all roles and some positions are eligible for participation in our long-term equity incentive plan. RGA also maintains a full range of health, retirement, and other employee benefits.

RGA is an equal opportunity employer. Qualified applicants will be considered without regard to race, color, age, gender identity or expression, sex, disability, veteran status, religion, national origin, or any other characteristic protected by applicable equal employment opportunity laws.