June 01, 2021

Discovering Data Science Career Paths with ATB Financial and AltaML

Discovering Data Science Career Paths

At ATB, AI is Everywhere 

Over the past five years, ATB has undergone a substantial digital advancement and we are now helping to lead Alberta’s tech revolution. And not to brag or anything, but we were also named the #3 Best Place to Work in Canada by Great Place to Work® this year and made the Forbes list of the World's Best Banks in 2019 and 2020—just sayin’.

At ATB, we’re taking a different approach to provide scalable financial advice to recommend the right mix of products and services, no matter what stage of life our clients are in. And you might think that AI-enhanced advice is the only opportunity for AI in Financial Services, but did you know that we’re also using AI to help reimagine how we can solve for well… everything? 

Let’s start with an everyday use case. How do we deliver on an essential service like a loan, and make that funding available to our clients as soon as possible? When applying for a business loan, we might need to deal with scans of financial statements, lawyer letters, documents from insurance companies—you name it. All of these documents have a role to play in processing that loan, but they also all need to be processed by hand, and that takes time and slows us down. 

To rise to the challenge of providing better, faster service—ATB is blending the power of Robotic Process Automation with the intelligence enabled by Machine Learning (ML). Over the last two years, we have built a unique partnership where ATB Data Scientists team up with ATB Automation Analysts to build bots that can read, understand and even take action. 

The end result is a dramatic boost in our processing times, allowing us to quickly connect Albertans with the financial services they need when they need them the most. And this is just one example where careers in different technologies: data science, automation, database and software development—all combine to help us enhance the client experience and make it possible for Albertans and Albertan businesses, whatever their “it” may be (insert fireworks emojis here).

Enter the AltaML Applied AI Lab

Part of ATB's vision is growing Alberta’s technology skills and future-ready workforce. That requires focus and commitment to the talent pipeline. ATB is one of four sponsors of AltaML’s applied data science internship program.

Interns, also known as Associates, benefit from working with experts in machine learning, artificial intelligence and data science on case studies and real-world applications, while Alberta gains the immeasurable benefit of creating a deeper pool of skilled data scientists.

To gauge perceptions of applied data science career paths in the banking sector, AltaML connected with Associates partnered with ATB for their impressions. While the Associates knew banks are data-rich and that data science practices are being adopted, they were still surprised by the scale they encountered at ATB. “Working with tabular datasets that are hundreds of GBs in size adds a unique challenge to data science that is not present in most Kaggle competitions or academic settings,” said Mike Lasby, AltaML Associate ML Developer. “Banking generates a lot of high quality data, so it is a great industry for data scientists to thrive in.” Similarly, Devin Norris, AltaML Associate ML Developer, commented, “ATB Financial has an entire innovation lab dedicated to research and development for AI and machine learning. Pretty cool for a financial institution that's been around since the 1930s to be leading the way in fostering innovation for our province!”

Thus, the opportunity in the banking sector is larger than they realized. Given this growing opportunity, how important is domain expertise, i.e. a background in finance, in addition to data science expertise? Drishti Mannan, AltaML Associate ML Developer, answered unequivocally that on a team basis, “all data science problems require domain expertise - it is impossible to propose valuable solutions without having an understanding of the business environment.” Drishti also noted that “most data science problems in industrial settings are less about models and more about making better operational and product development decisions. Hence, it is a collaborative problem and requires diverse skill sets.” 

Devin commented that the team “quickly learned that domain knowledge is extremely important when working with machine learning in industry.” By way of contrast, data science skills dominated his academic data work. Devin concluded that “applying AI to generate tangible business value usually requires a breadth of domain knowledge not necessary in academia.” 

The absence of a finance background, given data science skills, was not a constraint to the Associates, given the collaborative environment. Rachel Shen, AltaML Associate ML Developer, commented that she is “comfortable working with ATB because they equip us with the necessary context and knowledge, and we are working together to transfer business logic to technical logic.” Mike echoed the sentiment, saying “my lack of formal training in banking has not prevented me from finding valuable insights in the data. That’s what makes practicing data science so fun; you can find fundamental truths about a topic even if you lack subject matter expertise. The language of math and statistics is the same, no matter the domain.”

The opportunity to work with ATB teams has both expanded our Associates’ horizons and helped them find their specific path. Drishti remarked: “this experience has exposed me to an even broader range of opportunities where data science may apply. I no longer think that data science is limited to models and performance metrics.” Devin noted “I've learned how to distinguish between the many different job titles in the data world, each requiring an analytical mind and a wide skill set, but all having differing expertise, duties and responsibilities. For example, certain positions may be more consultative and business- or client-facing, and thus require a skill-set focused on soft skills like communication and presentation. Other positions could be purely back-end model development and research, which would require a deeper theoretical and practical understanding of machine learning and AI. The wide range of data science positions means that there's probably a fit somewhere for anyone interested in data.  It all comes down to personal preference and areas of proficiency.” Mike has gained confidence that his decision to pursue a data science career was a sound one, noting, “ML today is analogous to the personal computer in the early 1980s. Within the coming decades, data science and data-driven decision making will transform most industries and ML practitioners will be in high demand as a result.”

To those looking for a career in data science, our Associates recommend getting started and practicing. Devin notes “whether it be through the many emerging data science programs at universities across the country, or less traditional methods like YouTube tutorials or Udemy courses, there's plenty of opportunity out there for anyone who's interested in learning more about the field.” Mike suggests “Look for opportunities to practice data science wherever you can. Side projects will foster your talent and help you stand out amongst other candidates.”

In terms of fit, our Associates also emphasized the importance of problem solving in an applied context. Rachel stated “This job is about looking for problems and solving problems, so it will be very interesting if you are into exploring the data world. SQL, Python and ML knowledge are main skills, and AltaML is really a good platform.”  Drishti said “if you can think through problems, especially in a holistic manner, you are golden. Modelling and technical aspects of data science come much later - you need to understand what the problem is before you start modelling. If this sounds exciting to you, data science is the field for you!”

To explore a career with ATB, check out  

About the Applied AI Lab

The Applied AI Lab was created to accelerate applied AI development through a machine learning and data science internship. The AI Lab is a collaborative industry-led initiative, partnering with ATB, Suncor Energy, Spartan Control, and TransAlta. It helps bridge the talent gap between academic and applied skills by providing interns with guidance and access to real business problems, data, and industry partners. Over the 3-year program term approximately 240 individuals will have the opportunity to gain hands-on experience in applying AI.

For more information contact:

Danielle Gifford, Senior Manager of the Applied AI Lab

[email protected]