Data Scientist in Financial/Insurance Fraud Detection and Prevention (KTP)

KTP

Knowledge Transfer Partnerships (KTPs) are a unique UK-wide activity that help businesses to improve their competitiveness and productivity by making better use of the knowledge, technology and skills within universities, colleges and research organisations. Further information is available at: www.ktponline.org.uk

THE PROJECT 

The University of Essex in partnership with Ticker Ltd offers an exciting opportunity to a graduate with the relevant skills and knowledge to apply leading-edge approaches from mathematical data science to challenges including fraudulent behaviour in a high-growth insurtech start-up. This post is fixed term for 24 months and is a remote first role.

Please note that as part of the recruitment process, candidates will need to submit a satisfactory report from completing the Ticker Data Science Technical Test.

Previous applicants need not apply.

DUTIES OF THE POST

The duties of the post will include: 

• Identifying the areas of insurance fraud that are most maleficial to Ticker Ltd and the car insurance industry.
• Identifying external datasets to complement the data from Ticker Ltd. Performing data fusion on these disparate datasets.
• Applying cutting-edge statistical analyses (including classical, big data and machine learning approaches), on datasets to address and classify fraudulent insurance behaviour.
• Using telemetry data analyses to identify measures and metrics to be calculated from Ticker Ltd data, for use in identifying and classifying fraudulent behaviour in driver profiles.
• Aiding in embedding these identification approaches into the systems of Ticker Ltd and help upskill company staff.
• Preparing academic papers for publication of research findings in peer-reviewed high impact journals, in collaboration with Ticker Ltd and academics at the University of Essex.
• Participating in academic and/or industrial conferences and other events, to disseminate and present research outcomes to the wider community.

These duties are a guide to the work that the post holder will initially be required to undertake. They may be changed from time to time to meet changing circumstances.

KEY REQUIREMENTS

• BSc in Mathematics, Statistics, Computer Science, Data Science, Actuarial Science or equivalent.
• PhD in Computer Science, Data Science, Economics, Mathematics, or a related discipline is preferred. Candidates currently pursuing a PhD or those with an MSc (accompanied by significant experience in Data Science/Analytics, data wrangling/interrogation) will also be considered.
• Knowledge of Python and related libraries for working with data and training models.
• Experience working as a data scientist, or on academic related projects, solving problems related to data fusion, big data, neural networks and deep learning.
• Knowledge of modern pattern detection tools and techniques, such as time-series and geo spatial analysis.
• Understanding/experience of modern software development practices.
• Understanding/experience of the fundamentals of machine learning and data fusion.
• Experience of handling large and complex datasets and fusing disparate data streams using, for example, APIs.
• Practical and theoretical knowledge of computational intelligence / machine learning algorithms for predictive modelling and forecasting.
• Problem solving and organisational skills.

Please use the 'Apply' button to read further information about this role including the full job description and person specification which outlines the full duties, skills, qualifications and experience needed for this role. You will also find details of how to make your application here.

Our website http://www.essex.ac.uk contains more information about the University of Essex. If you have a disability and would like information in a different format, please email resourcing@essex.ac.uk.

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