Forensics - Investigative Analytics Analyst
About Investigative Data Analytics
In an increasingly digitised world, instances of fraud and electronic crime are becoming more and
more common. Investigative Analytics involves the examination of data in order to discover
meaningful patterns, measure historic events or predict the likelihood of future ones. Much of our work
requires us to analyse vast quantities of electronic data, using cutting edge technology and advanced
statistical techniques to extract meaningful insights for our worldwide clients. We operate in an
environment where speed of response is often critical which drives us to continually innovate and
draw on the very latest technologies.
Join us and you will get to work alongside leading professionals in our Belfast Forensic Laboratory in
fields such as Big Data, Predictive Analytics, Machine Learning and Data Visualisation.
You could be searching for evidence of fraud and abuse within a company’s finance system or
helping to identify illegal activity within millions of regular transactions. You may also be researching
new tools and technologies and developing new propositions to take to our clients.
As a valued member of the team you will gain exposure to a wide variety of analytical techniques,
providing you with a solid foundation for your career in data analytics. The very nature of what we do
means that many of our clients have an international reach, and no two jobs, or indeed days, are the
same. So, if you are naturally inquisitive, have an analytical mind and enjoy solving problems in a
rigorous and methodological manner, we’d be interested in talking to you.
This position is based in Belfast, however candidates should also be prepared to travel as some of
our projects may require team members to work at other PwC offices and/or client sites across the
UK or overseas.
You will need to have a 2.1 degree or above in a technology or analytical related discipline e.g.Computer Science, Software Engineering, Maths or Physics.
Possess an aptitude for technical and analytical work with Data Analytic tools and technologies.
Data programming languages (e.g. SQL, Python, R, SAS, Java).
An understanding of common data quality problems.
Data cleansing and manipulation.
A general understanding of statistics.
Clustering and/or text mining concepts and techniques.
Software development lifecycle and methodologies.
An understanding of basic accounting principles.