hybrid working here . What you’ll be doing
Helping to control fraud losses by developing rule sets to help identify fraud and mitigate significant exposure to financial loss. You’ll also use your own financial crime knowledge along with highly skilled data manipulation and analysis skills to monitor and analyse fraud detection performance, owning and delivering solutions for improving individual rules. About you
As a minimum requirement you’ll need: Experience in coding languages/programmes (for example either SQL/ SAS/Python etc) Strong knowledge of excel for analysis, including pivot tables and VLookups etc A logical approach to problem solving and the confidence to make recommendations for solutions Strong verbal and written communication skills with an ability to influence and challenge stakeholders at all levels Strong capability to interpret complex data, identifying trends and effectively communicating findings to stakeholders Excellent analytical skills Fluency in Microsoft Office 365 Suite (Word, Excel, Powerpoint, Outlook etc.) Experience of leading/supporting changes to process Excellent personal organisation with the ability to manage your own workload and prioritise effectively The extras you’ll get
There are all sorts of employee benefits available at Nationwide, including: A personal pension – if you put in 7% of your salary, we’ll top up by a further 16% Up to 2 days of paid volunteering a year Life assurance worth 8x your salary A great selection of additional benefits through our salary sacrifice scheme Wellhub – Access to a range of free and paid options for health and wellness. Access to an annual performance related bonus Access to training to help you develop and progress your career 25 days holiday, pro rata What to do next
If this role is for you, please click the ‘Apply Now’ button. You’ll need to attach your up-to-date CV and answer a few quick questions for us. We respond to everyone, so we will be in contact shortly after the closing date to let you know the outcome of your application.
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