The sexiest job in the 21st century vs the best job of 2015

Everywhere I go, I notice the term data scientist being mentioned.  Maybe it's because I am more aware of this or maybe because we have a data analytics team!  Recently, I attended a London Market Actuary Group where many actuaries turned up for the talk on machine learning, and having read that actuary is the sexiest job I thought I should do some more research.

I typed 'sexiest job' in Google and 5.9 million entries popped up telling me that 'data scientist' is actually the sexiest job in the 21st century, but according to, actuary has been voted the best job of 2015 in the US!  Why are data scientists so 'hot' right now? 

I will explore whether data scientists are friend or foe to the actuaries in this blog.

Origin of data and actuarial science

The term 'data science' was initially used as a substitute for computer science by Peter Naur in 1960.  In 1974, Naur published Concise Survey of Computer Methods, which freely used the term data science in its survey of the contemporary data processing methods that are used in a wide range of applications.

Equitable Life first used the term 'actuary' in 1762 to mean what it currently means but the important advancements that formed the basis of actuarial science were made in the 17th century. 

Based on Google Trends, the interest in 'data science' and 'actuarial science' tells an interesting story.  The data science term has gained popularity over time, especially in the last three years (i.e. the increasing blue line trend in the graph below) and the term actuarial science has a steady trend.

*example graph from Google Trends. Note - this graph does not convey absolute search volume.  It represents search interest relative to the highest point on the chart.

Actuarial science existed long before data science but data science has gained popularity in recent years!

What do data scientists do?

I found this on

“Data scientists use the ability to find and interpret rich data sources; manage large amounts of data despite hardware, software, and bandwidth constraints; merge data sources; ensure consistency of datasets; create visualizations to aid in understanding data; build mathematical models using the data; and present and communicate the data insights/findings.”

When I read this, I thought actuaries do all these too - so we are not that different!  I read on:

“Data science employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information theory, and computer science, including signal processing, probability models, machine learning, statistical learning, data mining, database, data engineering, pattern recognition and learning, visualization, predictive analytics, uncertainty modeling, data warehousing, data compression, computer programming, and high performance computing. Methods that scale to Big Data are of particular interest in data science… ”

Ok, so machine learning, database, data engineering, data warehousing, data compression may not be familiar terms, but mathematics, statistics, pattern recognitions etc are core to our training and our day-to-day work.

Surely in an insurance company, actuaries should be involved in the heart of data analytics.  

What can actuaries do in this area?

Actuaries have the ability to use data analytics to identify the vulnerable and use the data with positive intent to include high-risk groups rather than exclude them.  As pointed out in this blog, 'Taking responsibility for looking after the vulnerable', I believe actuaries could and should learn from data scientists.  The fact that actuaries operate under the Actuary's Code, means that others can rely on us to act with integrity, competence and care, impartiality and also to communicate effectively at all times.

Actuaries have a vast amount of insurance specific knowledge.  My opinion is that we do not need to compete with the data scientists. Instead, we should work with them as we have strengths that complement each other.

If actuaries do not embrace data science, the sexiest job in the 21st century and the best job of 2016 may both go to data scientists.  Actuaries may one day disappear!  Which is why we at Barnett Waddingham, are embracing data analytics techniques to solve traditional and new problems.

If you are keen to know more about machine learning or how actuaries can play a core part in the data analytics strategy for your company, please contact us for more information.

Key takeaways

  • data scientists skill sets are not that different to actuaries
  • actuaries have the Actuary's Code to provide a moral compass and ensure the communications are clear
  • actuaries have detailed knowledge of insurance companies and how data analytics techniques can help
  • actuaries should use data analytics to do more to identify and help the vulnerable