One day in the life of a leader in data science at Mastercard

BY Megan MalasJune 14, 2022, 1:48 p.m.

Mastercard logo, in the Mastercard pavilion, during the World Congress on Mobile, as seen in February 2018 in Barcelona, ​​Spain. (Photo by Joan Cross — NurPhoto / Getty Images)

Data science is a deeply interdisciplinary field, which makes it difficult to define precisely. This is a profession that involves more than just coding – the daily tasks and responsibilities of data scientists can vary dramatically, as can the use of data, depending on the company.

By 2025, it is estimated that 463 exabytes of data will be generated every day worldwide, according to Raconteur. More data provides opportunities to improve business strategy, but the more data available, the more staff is needed to manage, analyze and make decisions from them.

This growing need for scientists, according to data, is something that Mohamed Abdelsadek knows quite well. He leads Mastercard’s data science efforts as executive vice president of insights and analysis in the company’s data and services department – which, along with cyber and intelligence services, generates 35% of Mastercard’s net revenue in 2021.

Mastercard’s data and services department uses the data that the credit card company collects to provide business tools and solutions to customers. These services were once directly related to the company’s core business: its card products. However, the company has begun looking for ways to help customers effectively use all the information and technology that Mastercard can provide.

“Over time, we have invested heavily to ensure that our data can be used and consumed so that we can provide added value,” said Abdelsadek. “As we made more and more of these solutions, we began to grow beyond the core business so that we could offer solutions more widely.”

Mastercard’s data and services team now provides solutions not only to banks and retailers, but also to small businesses and governments. Today, data science is widely used in the company, as data scientists work on product development, analysis and customer support – ultimately turning data into action.

The team is already supported by more than 2,000 data scientists, engineers and consultants who come to the company with degrees in areas such as data science, business analysis, information systems, mathematics, statistics and engineering. Abdelsadek holds a master’s degree in computer science from Columbia University and an MBA from Wharton.

To find out more about what a day in the life of a scientist looks like according to Mastercard, Condition spoke with Abdelsadek and other scientists on data from the company.

The short answer: every day is different

It makes sense that the multifaceted profession involves different responsibilities every day, but some tasks and timetable elements are constant for Abdelsadek.

“If it was the same every day, I would be quite bored,” said Abdelzadek. “My time passes between clients, our partners and our teams and employees.”

However, aspects of his routine remain the same, and he believes it is important to rely on some consistency to provide structure for the various elements on his to-do list. Every day, Abdelzadek wakes up between 5:00 and 5:30 in the morning and heads to the gym before work. Before he even turns on his computer, he writes down everything he wants to accomplish for the day on a blank sheet of paper.

“I find that if I don’t, I’ll just be engrossed in emails and everything that comes my way during the day, and in the end I don’t do the things I think are really important,” Abdelsadek said.

Here are some of the elements that are written on this defining piece of paper for the day:

1 Meeting with clients: Abdelzadek usually has at least one meeting every day with a customer to discuss requests and current product offerings.

2. Developing ideas for new products: Abdelzadek communicates with several different Mastercard teams about new ways to use data and what product developments are on the horizon.

3. Mentoring others: There is time for a phone call every day with a junior Mastercard employee to discuss career ideas and topics.

4. Meeting with industry experts: As an example, Abdelsadek meets with Bricklin Dwyer, chief economist and head of the Mastercard Economics Institute, to discuss trends affecting partner companies as well as Mastercard’s own business.

5. Setting key business priorities: Much of Abdelzadek’s time is spent making sure the company continues to think about what third-party information – in addition to its own information – can help complement and improve Mastercard’s services. One of the ways he does this is by routinely working with Joan Stonier, Mastercard’s chief data officer, to improve the company’s data infrastructure and analytics environment.

Everyday tasks are conducive to the interdisciplinary nature of data science

At Mastercard, data scientists need to balance the current with the new – meaning they need to ensure the effectiveness of their current tools while preparing new tools for issues and topics they have not yet addressed.

“We have over 2,000 consultants, data engineers and data scientists who are available to help clients with a variety of issues,” said Abdelsadek. “For some of these products, we have been working with customers for years on a variety of issues.”

As Mastercard continues to expand community-driven partnerships, the company is focusing on growing its data science team to keep up with additional projects. In February, the data and services team announced plans to add more than 500 college graduates and young professionals.

Everyday tasks reflect the interdisciplinary nature of data science, according to Fuyuan Xiao, data scientist and director of product management at Mastercard. Although new solutions are always on the market, Mastercard data specialists tend to rely on a consistent system to work in new areas of development.

“The nature of data science is to use techniques derived from many fields, including statistics, mathematics, computer science, and information science, to develop algorithms that can extract insights from data,” Xiao said. “When we apply data science to solve industrial problems, knowledge in the field is also required.”

Xiao’s daily workflow is a four-step cycle. First, it sets business goals, taking into account customer needs and consulting experts. Second, a statistical model is developed that determines the best type of algorithm to deal with the business problem. Third, Xiao and her team train and validate the model with vast amounts of data.

“Model training and validation will happen recursively – so it’s important to use the right programming tools and systems,” says Xiao.

Finally, the team applies what they have learned to solve a specific business problem and help their client achieve their goals.

Communication and collaboration are a daily part of being a data specialist

“Many data scientists in this field know that it is not enough to be very good at coding, or interpreting data, or creating automated data pipelines, or machine learning, or speaking,” said Joel Alcedo, a data scientist. Vice President of Applied Economics at the Mastercard Institute of Economics. “The most successful scientists, according to data I’ve seen, are equally good at everything – something like a Swiss Army knife with a variety of tools that they can apply at any time.

Data scientists don’t just deal with technical issues and provide product code; they often interact with internal and external stakeholders.

Any data science solution involves collaboration between several departments – including customer service, product development, business development and account management, and more, said Vishal Arora, a data scientist and senior management consultant on the data service team. clients of Mastercard Advisors.

So while the daily routine of data scientists varies dramatically, essentially every day involves a combination of technical tasks, analysis and communication.

“Effective data scientists also know how and where there are gaps in their team’s work process and work with the right stakeholders to improve team performance,” says Alcedo. “They also know where there are major gaps in their own skills from which they can learn and rely on others for support.”

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