Interview written and conducted by Maria De La Pava, Former Program Analyst – Global Talent Marketing
Andrew Storey is a fantastic leader who uses statistics, machine learning, graph theory, and numerical optimization methods to implement automated decision processes at the Bank! Want to learn more about the Decision Sciences department at Scotiabank? Interested in taking a closer look at Andrew’s work-life? Keep reading!
Maria: So tell us a bit about yourself and your career path at Scotiabank!
Andrew: I have been with Scotiabank for 19 years now so I have been able to work across a bunch of areas at the Bank. Nineteen years ago I started in Canadian Banking, then I moved on to Risk Management in the Economic Capital portfolio, then into International Banking doing analytics. For the last four or five years, I have been in Decision Sciences. I have always done and supplied analytics in different spaces in various scenarios. That’s the general theme of my career path.
M: What does an “average” day look like for you?
A: My days in Decision Sciences are actually quite variable, which is one of the parts of my job that I love so much! It can range from partnering with executives for certain approaches to developing analytical strategies to drive their business objectives. Of course, I get to spend a lot of time with the teams looking at the work in detail; analyzing the approaches and methodologies that our team is using to support the business. I get to go and see both sides of that equation. I also advocate with our partners in terms of what it is that we can do and understanding what their problems are that we can solve. I spend quite a bit of time talking to various areas of the Bank regarding what analytics can do to help!
M: How would you describe Scotiabank’s company culture? How has the culture evolved since you began working at the Bank?
A: From an evolution perspective, the Bank was very traditional 19 years ago – we had cubicles, very rigid roles, and it was fairly hierarchical. However, it has definitely changed over the years, especially lately with the introduction of our new Ecosystems that we are moving into. They are fantastic and they have changed the way that people interact with each other and work, but more importantly, the way we think about people’s roles has become more dynamic and engaging. There is much better communication across all levels, which has softened the rigid hierarchies. Previously, people were more hierarchical; now, it’s much more fluid and open. I know that I appreciate that and I can only imagine other people do as well. It has become more outcome-oriented instead of process-oriented.
M: I agree completely! So tell us a little bit about your team – how big is it and what kinds of roles exist?
A: There are about 40 or so people on the team. The types of roles that we have include data scientists, data engineers, and we even have a few roles working with DevOps for analytics. The background of people also varies – from computer science to disciplines that highly leverage the STEM background.
M: That’s great! It sounds like you have a robust team enterprise-wide. How do you recruit and retain the best people—especially in markets where technology talent is in such high demand?
A: Well, we have quite an active intern and co-op program in the department so we always have four to five co-ops and two or three interns throughout the year. This creates a very good pipeline for new talent to come into the Bank, either directly from the university or indirectly through their conversations with friends about what it is that they were doing once they are back in school. We make sure that we treat the co-ops and interns exactly how we treat full-time employees here. Our interns and co-ops work on great projects and have a lot of deliverables, which gives them a lot of responsibility. The way I like to think of it is if a partner came into the office, they wouldn’t be ableto tell who’s a co-op, who’s an intern, and who’s a full-time employee. I find that this helps us a lot when recruiting and, more importantly, people like working this way.
In terms of recruiting in general, it’s about leveraging and focusing the skills candidates have to support the Bank’s objectives, as the ultimate goal is to drive value for the Bank. The way we can support the business lines and make the client experience better is to take the skill sets that we have and apply them. Therefore, we make sure to select projects where we add the best value, which gets the team engaged in the work and super excited to come in and develop their skillset.
M: So, how do you keep yourself and your team motivated and inspired to do great work?
A: Part of it is always trying to go beyond what’s at the forefront of the analytical capabilities that we can leverage to achieve the project objective. That really helps motivate people, as opposed to just always falling back on to something that people are comfortable with. We always try to push the envelope to get the best out of the project.
I also think getting executives to take a detailed interest in what we are working on helps gain exposure for the team on the great work they’re doing. I try to always push them forward as much as I can from that perspective and I know that it really inspires our team members to show the great work that is happening in analytics.
M: Of course! On the subject of the great projects your team is working on, how has your team applied artificial intelligence (AI) and machine learning (ML) at Scotiabank? Can you tell me more about what it was like to present at the Google Let’s Talk AI event?
A: We have donea number of projects using AI and ML across the Bank. One of them is the contract confirmation project – we call it “AIDox” – where we’re helping Global Wholesale Operations by programmatically reviewing contracts to make sure that they’re satisfying certain criteria. So now, they still have people reviewing them as well, but we have used ML and AI to pre-review them so that we can highlight where we think they should focus their attention. This has sped the process up a lot and has decreased the chance of errors.
Furthermore, we use these techniques when predicting the likelihood that an account has been taken over fraudulently or to identify customers that are the same entity across the different data sets, both internal and external. We also have an application that transcribes the calls in the Canadian Contact Centre and identifies significant topics – all done using advanced analytics techniques.
Moving on, the Google Let’s Talk AI event was an executive event for companies that are doing AI or ML techniques or wanting to do them. I believe there were 400-500 executives there and it was global. There were a lot of different industries: grocers, healthcare, insurance companies, banks, amongst others. We spoke about the applications that we have in the Bank and hear what other companies are doing. It’s nice for Scotiabank to be recognized as one of the leaders in AI in the financial services space, so I went and talked a little bit about what it is that we’re doing. It’s inspiring to also hear what other industries are doing with this technology.
M: As a leader of the Decision Sciences department, what is your mission and vision for the next fiscal year? What would you like to accomplish?
A: Well, there are a number of projects that we are working on, but fundamentally, we are working to achieve the analytical targets that Renato and Veeru have set for the entire analytics functions for the Bank. They’ve got very ambitious targets to achieve, so we focus our activities to make sure we’re generating the value to do that. We are really going to focus this year on driving the additional revenue and cost reduction through these projects and leveraging it broader across the organization.
M: And what are some of the highlights from 2018? Can you share anything about the exciting projects in store for 2019?
A: Some of the highlights from 2018 would be the work that we did jointly with the Risk Management team in terms of the enhancement of the credit management process for Canadian Retail by using the work that we’ve done from the financial networks space. The Risk Management teams are such great partners.
Another one would be our work with Google to get the availability of the analytic processes that we have and moving them up to the Google Cloud Platform in conjunction with the PLATO (Platform Organization) team to develop design and analytics. This will not only work for Decision Sciences but also for all of the analytics teams across the bank. We are really enabling a much better environment to be able to do analytics. We also worked with the Security and Privacy teams which have been fantastic partners in developing that approach.
Want to learn more about the available opportunities at Scotiabank? Visit scotiabank.com/careers
