A fantastic opportunity has become available for a Senior Data Scientist in our thriving Retail Financial Risk Modelling team.
As a Senior Data Scientist within Retail Financial Risk Modelling, you will be responsible for delivery of cutting-edge tools that support the business to deliver our Mutual Good Commitment and support Financial Crime. You will provide strong technical leadership and support, to lead, motivate and enable your direct reports and the wider team. You will be engaging senior stakeholders, planning and leading multiple projects relating to the ongoing development, maintenance and monitoring of machine learning models for Climate Change and Financial Crime.
The role will require deep understanding of different machine learning techniques, and capability to optimise the use of modelling tools. Proven experience in stakeholder management and communication will be integral to the role. Your role will also be to support other colleagues to develop their careers, and that Retail Financial Risk Modelling is a great place to work.
At Nationwide we offer hybrid working wherever possible. More rewarding relationships are supported through our hybrid approach, bringing colleagues together across our UK wide estate, whilst also supporting generous access to home working. We value our time in the office to solve problems, to learn, and to feel connected.
For this job you'll spend at least two days per week, or if part time you'll spend 40% of your working time, at Swindon or Cardiff office. If your application is successful, your hiring manager will provide further details on how this works. You can also find out more about our approach to hybrid working here.
If we receive a high volume of relevant applications, we may close the advert earlier than the advertised date, so please apply as soon as you can.
Leading your team to develop, maintain and deliver advanced analytical models, to support Climate Change and Financial Crime, employing relevant statistical techniques and adhering to current modelling standards and validation regulation.
You will also support the wider Retail Financial Risk Modelling to embed machine learning into models.
You’ll need to plan effectively and prioritise activities, to ensure your team are working on several business-critical activities at one time.
You’ll also need to pro-actively engage and communicate with the wider business on developments/enhancements and how they will impact business decisions.
Coach and develop your team in machine learning modelling.
To be successful in this role you will:
Our Customer First behaviours are all about putting customers and members at the heart of how we work together. You can strengthen your application by showing the behaviours that resonate with you, and how you might have already demonstrated these.
We know applying for jobs can sometimes feel like you’re sending an application into a black hole. We review each application individually. So, it’s a good idea to call out your most relevant experience on your application to give yourself the best chance.
There are all sorts of employee benefits available at Nationwide, including:
Nationwide is the world’s largest building society. With over 15 million customers, we have a relationship with almost a quarter of the UK’s population. We’ve got the scale to compete with the big banks, but we’re not a bank.
As a building society, we’re owned by our members – that’s our customers who have their current account, mortgage or savings with us. It means we can do things differently to deliver our Purpose – Banking – but fairer, more rewarding, and for the good of society.
When you work at Nationwide, you can experience that difference for yourself. You’ll be part of a high-performing, purpose-driven organisation that offers rewarding career experiences and a highly competitive range of benefits to match. You’ll also be joining us at an important time as we seek to reach more and more people in the UK. We want everyone in the UK to know that they don’t have to bank with a bank. They can choose a modern mutual instead.