Risk Associates

This two-year programme will have you explore different roles, teams and techniques – from the Risk Office, departmental risk teams, Supplier Management roles, Compliance or Audit – all while earning a post-graduate, professional qualification with the Institute of Risk Management (IRM).

Applications are closed for 2017/18 intake and will reopen in September 2018

Whether it’s working on management reporting, focusing on our cost base, preparing our statutory accounts or drafting a new contract with a supplier, you’ll be given full support. We’ll also ensure you receive extensive support in completing a professional accountancy qualification.

Applications are closed for 2017/18 intake and will reopen in September 2018

Software Academy

Our Software Academy is a two-year programme. In your first year you’ll be learning the basics and getting a great grounding in software engineering. In your second year, you’ll have the option to rotate and try different teams, as well as explore different technologies – from big data to cloud to cyber security – all while developing world-class technical skills on cutting-edge projects. 

Applications are closed for 2017/18 intake and will reopen in September 2018

 

 

 

Business Analytics and Consulting

Simply put, you’re here to change the way we do business. You love to solve problems – and you get a real kick out of creating ground-breaking strategies based on a wide range of data sources. You’re not satisfied until your thinking has created real-world impact.

Brand Marketing

You’re a passionate story teller, fuelled by fresh ideas and ready for a career defining challenge. Our team is on a mission to share Capital One’s exceptional story. From national advertising campaigns, to social media.

Data Analysts

There is a data revolution happening right now and we’re leading the way. This is a critical role at the intersection of business strategy and technology. Our Data Analysts play a fundamental role in our business. Their work impacts everything from marketing to operations and fraud.

Data Scientists

If knowledge is power then our Data Scientists could compete with the national grid. Data Scientists at Capital One are continually increasing our understanding of our customers and of the markets in which we operate. 

Questions to a Data Scientist

Written By:

Q&A with Dan Kellet, Director of Data Science @CapitalOneUK

First up, could you explain the difference between structured and unstructured data?

A lot of the data we’re used to is structured. If you’ve ever used a spreadsheet with rows and columns, then you’ve seen structured data. Structured data is generally where you’re working with information that’s regular, predictable and in a standard format.

Unstructured data is everything else.

A simple example is free text. Our call centre agents make notes when they’re helping customers over the phone. We analyze these notes and use the insights to spot trends that help us to better support customers.

As you can see, the complexity comes in when you consider that each agent has their own style of writing and uses different words or phrases to describe the same thing. The unpredictable nature of this makes it much trickier to process.

Rich data like images and video take it to another level. Most of us would rather watch a video than read a pages of text, right? But from an analyst’s point of view, extracting useful data from video can be a real challenge.

 

There’s a lot of analytic software in the market right now, each with its own pros and cons. Do you think there’ll ever be one super programme that does everything?

Ha! In short, no. 

I don’t think so. At least not in the near future. There’s just too much complexity, and the needs of different businesses are too diverse for a single bit of software to be able to do it all. 

We’re still debating whether open source or proprietary software is best. At Capital One we’ve decided to go down the Open Source road. But I do think there is a strong case for both. And as technology changes, so will we.

Ultimately, it all serves the same purpose – turning data into useful insight. Any analysis, however it’s done, should always stem from wanting solve a problem, or to improve a product or service. You’ve got to understand the problem you want to solve in the first place.

 

Projects like Capital One’s Growth Labs are a great way to bring big businesses together with startups who are considered to be shaping the future of tech. If your team was a startup, what problem would you try to tackle?

One of the biggest challenges right now is efficiency. So if we were a start-up, I’d be really interested to explore new ways for businesses to understand and translate data in real time.

Up until recently it was OK to work in batches. You know, you might complete a batch of work in a month, or a week – depending on the size of the project.

Now we want answers in a day. An hour. Or in real-time. And that’s where the future is. It’s a big challenge for data scientists. That kind of work takes serious tech, computing power and resource.

Just look at the growth of the Internet of Things. It’s estimated there’ll be around 26 billion devices operational by 2020. Each collecting huge amounts of real time data, some of which will never have been observed before. It’s going to change the way digital businesses operate in big ways.

At Capital One we’re super excited about the potential of new technologies. It’s something we’re heavily invested in. Both because it’s exciting, and because we want our customers to be the first to benefit.

 

There’s a lot of competition right now for tech talent. Do you think there’s enough talent to go around?

Totally. I think the data science world is flourishing. Schools and universities have recognised the important role of STEM subjects, and we’re starting to see the benefits.

There’s no doubt there’s a lot of competition out there. We’ve done really well to attract some incredibly skilled people, and will continue to do so. A big part of this is the opportunities people get here. We offer people the chance to work on big scale projects, where their work could make real differences to the lives of millions of people.

 

Looking back on your 15-year career in data science, what’s the one thing you know now that you wish you knew sooner.

That’s a good question.

If I had to pick one thing, I’d say: collaboration. It’s a big part of our success at Capital One. We don’t have any one man research projects. Sure, we have talented people who specialise in particular fields, but we’re not about silo working.

For the magic to happen – you need to bring people together. 

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Customer Satisfaction 2.0

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Three steps to achieving customer delight with automated predictive analytics.

As President of Mercedes-Benz, Steve Cannon, famously said, “Customer Experience is the new marketing”. Many would not disagree.

Whether you are using Net Promoter Score (NPS) or Customer Effort Score (CES), almost all businesses have customer satisfaction KPIs in place. But how do you know that these measures are useful predictors of behaviour, and more importantly, that they support sales and positive advocacy?

Welcome to CSat 2.0.

By introducing predictive analytics, and in particular automated predictive analytics, you can turn this concept on its head. You can now ask ‘What are the outcomes I am actually looking for?’, and then mine the data to generate the factors which drive those outcomes.

This means that a dynamic propensity model can be achieved for ‘nature’ (the attributes of the customers which predict greater sales and advocacy) and ‘nurture’ (how their customer experience affects these propensities).

In this way, practical indicators can be provided to sales and marketing teams to target the appropriate customers, and prioritise corrective or preventative action. This also has the secondary benefit of optimizing the cost of customer services, as a threshold of spend can be decided based on the desired service levels.

The traditional way of setting all this up would be to bring together predictive analytics software, a bunch of smart data scientists with time on their hands, and of course a hefty budget.

But as this isn’t possible for most businesses, this is where automated predictive analytics comes in.  This is a new breed of predictive analytics which does not require pre-defining terms or feature extraction (i.e. the deciding and defining of the variables to use in the analysis). It can be of great assistance in this step, as well as automating the subsequent steps to get to a usable answer quickly, in a matter of minutes rather than weeks or even months (data scientists spend around 80% of their time manually transforming, cleansing and preparing data).

Three steps to success with automated predictive analytics

Step 1: Simplify

The first step is to cluster the Voice of Customer (“VoC”) data from social media, reviews, weblogs, loyalty data, CRM, POS and surveys. Similarly, customers can be clustered by attributes and behaviours. This provides value in itself in terms of insight, and will really help to provide the foundations for further analysis.

Step 2: Predict

Once the data is simplified and aggregated, there are a variety of statistical and machine learning techniques that can take the results and predict what will happen given the mix of events and segments.

Step 3: Recommend

The final step is to recommend prescriptive analytics which present insights which are actionable. This can take various forms such as a traffic light indicator. For example, if the analysis shows a customer who has complained more than twice is 50% more likely to leave, a set of business recommendations can be created to help avoid this, like a pre-emptive outbound call. 

Understanding the interdependencies of customer services and sales can bring huge benefit. Predictive analytics, and in particular automated predictive analytics can assist marketers and customer services managers to do the right things at the right time for the right reasons. Welcome to CSat 2.0.

About Warwick Analytics

Warwick Analytics is a spin-out from The University of Warwick with proprietary algorithms for automating predictive analytics, even with heterogeneous data. It will be working with Capital One’s Growth Labs on the Capital One accelerator to refine its technology towards this sector and use cases and the teams are excited to develop cutting-edge applications.

The opinions expressed in this article are the author's own and do not reflect the view of Capital One.

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Simple, fair and accessible credit

Written By:

recent study by the FCA has revealed that millions of Brits will still be paying off their current credit card balances in 2026. That’s over ten years away.

A major cause of this is minimum payments. Credit card holders can choose how much they want to pay back each month, as long as they pay a minimum amount to avoid a fee. That minimum can be as low as 1% of the total balance. To put this in context, it would take someone with a credit card balance of £2,000 up to 24 years to pay it off.

Persistent credit card debt is a problem for a growing number of people and it’s clear that credit card companies can be part of the solution.

Capital One are already paving the way in this area. They recently made headlines with a campaign to encourage their customers paying the minimum to increase their monthly repayments. Customers who responded to the campaign upped their monthly repayments by 73% on average, saving themselves hundreds of pounds in the process.

In 2014 I founded Credit Kudos, a technology startup that uses consumer transaction data to build highly accurate and transparent credit score-cards and affordability metrics. This live data can be used to help lenders to better understand their customer’s financial situations. 

Credit Kudos founder Freddy Kelly

Over the next ten weeks, I’ll be working with Capital One’s Growth Labs to take the great strides they’re making even further.

The FCA’s guidance endorses behavioural ‘nudges’, but we want to actually predict customer’s ability to repay, enabling fairer credit decisions and maintaining healthy behaviour throughout the credit lifecycle.

Credit scores today are like spot-checks that provide little to no visibility for the consumer. Everyone’s financial situation changes continually, so why shouldn’t credit scoring and evaluation adapt as we do?

Credit Kudos enables a direct link between financial behaviour and lending outcomes. Our process automatically connects to a user’s current account to build a financial health score. This can be used to inform lending decisions, and help give a clearer picture of a customer’s financial health. So rather than missing a payment, we could detect income shocks and allow credit providers to make appropriate allowances on the fly.

How great would it be if your credit card provider could one day offer you a better rate, because it automatically knows you got a promotion?

The opinions expressed in this article are the author's own and do not reflect the view of Capital One.

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The build

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The central intent of the project was that the app was a useful tool for customers. It was intended as a way to help them succeed with credit. After all, this is core purpose of the business. The most important thing we needed to deliver was the payment facility. However, other functionality was added incrementally after the initial release; things like payment alerts so customers didn’t fall behind with their payments.

There was also a complete UX redesign. This made it easier for us to add things and extend the app without having to resort to a complete redesign - which obviously helped us a lot. It also needed to be brought in line with the UX and UI guidelines that came from the US.

Ongoing development and maintenance of the app is another key focus for us. We have iOS and Android versions and we made a decision that these should be developed in tandem so there was a unity of experience across the two. We wanted the look, feel and function to be as similar as the different systems allowed.

Initially, we didn’t have the in-house expertise to be honest. We had people who were familiar with apps but no-one who had built one from the ground up. So it’s been a big learning experience for all of us – but that’s what attracted me in the first place. It’s about the culture of collaboration and shared experience as much as anything else. There is a very real freedom to explore over and above the day to day role. We’re not siloed. We work as one team towards one cohesive goal. It also helps that we can volunteer at the intent stage to be a feature champion. You can choose – or at least ask – to work on something that is of specific interest to you.

Essentially, our job as testers focuses on making sure the app behaves and functions as it should do. We anticipate problems at the design stage so these don’t become a bigger issue further down the line when they’ve become incorporated into the build. That being said, there was a lot involved in ensuring this was the case. That’s one of the best things about working here; you’re not limited by your job title; you get to do other stuff too. You get to look beyond your own immediate remit. We’re always developing and learning new stuff.  This project exemplifies this. As testers we got to help with development and write the tests required. For my own part, I worked beyond automation testing and took on a certain amount of development on the app, as well as getting involved in APIs and analysing business systems. Roles are becoming blurred. If you have the initiative to take things on, you’ll be given the freedom to just run with it.

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Defining the business case

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Managing the business case for the release of our new mobile app was down to me. As you can imagine for a project like this, our objectives were pretty wide-ranging. We needed to ensure a smooth transition to a stable app so our customers could carry on managing their accounts. We also wanted to improve the user experience and functionality. From our point of view, it was imperative that introducing the app didn’t create additional risks and compromise profitability.

One of the key challenges was balancing the expectations of senior stakeholders in the marketing team and the people in our tech teams. It was my job to keep us on point and focused on the intent. On the one hand we had to assess and manage credit risks while, on the other, our software developers were keen to get a product out into the wild and add new features. We wanted to achieve as much as possible but there were commercial realities to consider; time constraints etc. I had to be realistic about what could be achieved.

For the project to be successful, a massive amount of collaboration was needed. There were people involved in the project at all levels and from all areas, and none of them could deliver what they needed to without me and my team assembling the business case.

I’m really proud of what we achieved. Firstly, we replaced an old app with one that looks and feels brand new. It also adds new functionality for customers to make payments. We don’t stand to make much (if any) money from doing that, but we did it because we knew people wanted it and because it’s the right thing to do.

The other advantage of this update was to bring the app in-house. We built it on new code written by our own software engineers. This means we can update the app much more often (every couple of weeks if we need to), adding features that customers suggest in App Store reviews etc. This helps us build trust with our customers, and people get genuinely excited when their suggestion appears as a new feature. That’s not bad going.

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The design process

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For me, this project exemplifies one of the main reasons I joined Capital One in the first place: collaboration. I love the way that everyone here pulls together to create the best possible outcome and best possible product for our customers. It’s a steep learning curve at times. But that’s how you develop.

We’re also currently working on bringing our Online Account Servicing platform in-house. The control is beneficial for a number of delivery reasons, the main one being we can work fast and push forward in this space. Our Agile way of working enables this but, also, not being tied to any third parties when we need to make changes gives us much more freedom.

We work in a genuinely collaborative environment and this, along with our flat structure, makes this a fab place to learn and develop. This is a business where an idea can be born and developed in a matter of a few months – which for a highly regulated financial services company is virtually unheard of.

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