Who are we
EY is a global player active in the field of (Tech) Consultancy, Assurance, Tax and Transactions. With our expertise, systems and financial services, we contribute to a better working environment. That starts with a culture in which you receive training, opportunities and creative freedom to continuously improve yourself and EY.

Within EY Actuarissen
the largest actuarial advisory group at a multidisciplinary service provider – we deal with current topics in the pension and insurance field. The consequences of the new pension agreement, the quantification of insurance risks, a higher life expectancy, digitization and innovation within the insurance sector, modeling of financial risks via data analytics and the introduction of new legislation and regulations (including IFRS17) are just a few topics that EY Actuaries actively provides services in.

Working for us
Within EY Actuaries our specialist work in the following 6 fields:
– Asset Risk Management
– Non-life insurance- Life insurance
– Pensions
– Data Analytics
– Health Insurance

Due to the diversity of the work that the Actuarial Services team does, it is possible as a consultant to perform financial, insurance and pension-related activities, which give you insight into how the theory as learned in the study is applied in practice. The background of our consultants generally consists of a mix of econometrics, actuarial science, business analytics and mathematics.


Sectors: Data Science, Consulting & Actuarial Science
Location: 14
Male-Female ratio: 60-40%
Number of employees (in Holland): 5000
Number of starting positions per year: 10
Number of internship positions per year: 4-6
Number of working students per year: 6-10
Average age: 32


The company case will consist of an outlier detection challenge. Within an enormous dataset, it is up to the participants to identify outliers and/or erroneous lines within the data, with the support of relevant software. This challenge requires the use of data visualization, data manipulation and outlier detection, such that a cleansed dataset can be used as a basis for future model development. As a clean dataset is the basis for a well estimated model, this challenge is very relevant in the current econometrical field.