As a Data Scientist you will apply statistical techniques and machine learning to build solutions to core challenges in the life insurance industry.
You will be immersed in real-time business problems while engaged in a collaborative approach to delivering world-class, innovative solutions for our North American operations and clients.
We see the use of data as instrumental in making it easier for people to buy life insurance and to expand the number of people insured.
Apply advanced statistical and machine learning techniques to build models for underwriting, experience studies, assumption development, pricing, and claims management;
Help us to drive innovation, enabling new underwriting paradigms, distribution models, and data management;
Build and implement solutions that enable operational units to improve quality and speed of core processes in order to generate incremental revenue or reduce expense;
Proactively research new ways of modeling data to unlock actionable insights or improve processes;
Collaborate across Munich Re functions and with clients to use analytics to influence business decisions;
Work with existing data science groups at Munich Re and collaborate with internal partners to leverage capabilities in big data technology.
First and foremost, the successful candidate will demonstrate a natural desire to provide exceptional client service through his / her energy, enthusiasm and initiative.
In addition, we are looking for the following qualifications :
Undergraduate Degree in Computer Science, Engineering, Statistics, or Applied Mathematics, plus 3 years’ experience OR Graduate Degree in Computer Science, Engineering, Statistics, or Applied Mathematics, plus 1 years’ experience;
Insurance or financial services background is preferred but not required;
Actuarial examinations or designation is an asset but not required;
Expertise in advanced predictive analytic techniques;
Strong experience working with Python, or R; working knowledge of SQL (familiarity with multiple languages considered an asset);
Experience working with analytics through the modeling lifecycle including gathering data, design, recommendations, testing, implementation, communication, and retraining;
Familiarity with cloud computing platforms (ex. AWS, Microsoft Azure)
Familiarity with big data technologies (ex. Apache Spark, Hadoop, etc), natural language processing and deep learning frameworks (ex.
Tensorflow, Pytorch) is an asset but not required;
Excellent communication skills, effectively interpreting modeling results, distilling actionable insights and presenting them to partners;
The ability to learn quickly;
A drive to make a difference;
Thrive in a dynamic environment and successfully deliver on multiple assignments under deadlines.