More than anything, RGA employees love their jobs because it’s challenging work. We believe smart people work smarter when they’re empowered.
When they use logic, balanced with creativity and curiosity. At the forefront of RGA’s culture is collaboration a must to be successful within our organization.
These are just a few of the attributes we are looking for in a Lead Data Scientist to join our Data Science team here in London.
The successful candidate will be expected to do the following. Manage a team of data scientists to deliver quality modeling results to business units and ensure the performance of team.
Lead projects to design, develop and review predictive modeling solutions and commercial applications for both RGA internal and external clients across the globe.
Routinely educate, update and communicate with senior management teams, other key stakeholders and at client & industry seminars.
Management : leads and is accountable for a team of data scientists. Reviews and adapts plans and priorities to meet service and / or operational challenges.
recruit new talent with potential to enhance the team. Modeling : Lead projects to design, develop, interpret and implement end-
to-end statistical models for the applications in insurance industry, including mortality, morbidity, persistency, fraud, consumer response, etc.
for use internally or externally.
Commercial Applications : Identifies and resolves technical, operational and organizational problems to generate solutions for clients and oversee the development of commercial applications of models for use in underwriting, claims, risk management and / or projections.
Subject Matter Expertise / Client Contact : Serve as an expert in statistical modeling, insurance applications, implementation, and solution design predictive modeling and advanced data analytics to both internal and external clients.
Collaboration / Communication : Lead collaboration efforts and routinely serve in a client-facing role as a project expert.
Routinely present information to clients and at various industry & RGA hosted events. Actively participate in other research-
related initiatives Maintain regular and predictable attendance. Perform other duties as required.
Required : 10+ years of actuarial experience or in developing statistical models for insurance or related applications Bachelor’s degree in Statistics, Actuarial Science, Business, Finance, Economics, or related field Preferred : 12+ years of experience with statistical modeling for insurance (GLM, Decision Trees, Time Series, Regression, etc.
Master’s degree or PhD in Statistics, Actuarial Science, Business, Finance, Economics, or related field Required : Advanced PC and technical skills, including statistical programs (ex.
R, SAS, MATLAB, or Python), spreadsheets (VBA) and database applications (Access, Oracle, SQL or equivalent technology).
Advanced knowledge in econometrics, statistics, math and / or computational finance. Ability to quickly adapt to new methods, work under tight deadlines and stressful conditions Ability to work well within a team environment, participate in department / team projects and balance detail with departmental objectives Advanced oral and written communication skills, demonstrating the ability to convey business terminology that is meaningful and well received Strong ability to manage multiple projects and / or teams simultaneously Advanced persuasion and negotiating skills when working with internal / external customers Skills in customer relationship management and change management Ability to resolve conflict and foster teamwork Investigative, analytical and problem solving skills Advanced ability to translate business needs and problems into viable / accepted solutions Advanced ability to liaise with individuals across a wide variety of operational, functional and technical disciplines Preferred : Knowledge of actuarial concepts including mortality, morbidity, and persistency studies.
Knowledge of life, health, and / or annuity products. Knowledge of life insurance underwriting and biometric risk analysis Experience with R, SAS, MATLAB or Python.
Knowledge of SQL and VBA. Knowledge of insurance risk analysis. Experience in computational finance, econometrics, statistics and math.