BY Meghan MalasJune 14, 2022, 1:48 p.m.
The Mastercard logo was displayed in the Mastercard pavilion at the Mobile World Congress in February 2018 in Barcelona, Spain. (Photo by Joan Cros — NurPhoto / Getty Images)
Data science is a deep interdisciplinary field, which makes it difficult to define precisely. It’s a profession that involves more than just coding: the day-to-day tasks and responsibilities of a data scientist can change dramatically depending on the use of the data and, depending on the company.
By 2025, according to Raconteur, 463 exabytes of data will be generated every day worldwide. More data offers opportunities to improve your business strategy, but the more data available, the more staff you need to manage, analyze, and create solutions.
This widespread need for data scientists is something that Mohamed Abdelsad knows quite well. He oversees Mastercard’s data science efforts as executive vice president of analytics and analytics for the company’s data and services division, which, along with cyber and intelligence services, accounted for 35% of Mastercard’s net revenue in 2021.
Mastercard’s data and services unit uses data collected by credit card companies to provide customers with business tools and solutions. These services were once directly linked to the company’s core business: its card products. However, the company began to look for ways to help customers use all the information and technology that Mastercard could provide.
“Over time, we’ve invested heavily in ensuring that our data can be used and consumed so that we can add value,” says Abdelsad. “As we made these solutions more and more, we began to evolve beyond the core business to be able to offer broader solutions.”
Mastercard’s data and services team provides solutions not only to banks and merchants, but also to small businesses and governments. Today, a lot of data science is applied in the company, as data scientists work on product development, analysis, and customer support, ultimately turning data into action.
The team now supports more than just data scientists, engineers and consultants with degrees in data science, business analysis, information systems, math, statistics and engineering. Abdelsad holds a master’s degree in computer science from Columbia University and an MBA from Wharton.
To find out what a day in the life of a Mastercard data scientist looks like, Good luck Abdelsad and other company data scientists spoke.
Short answer: It’s different every day
It makes sense that a multi-professional job involves a variety of tasks on a daily basis, but some elements of the task and schedule are constant for Abdelsadek.
“If all days were the same, I would be pretty bored,” says Abdelsad. “My time is spent with customers, our partners and our team and staff.”
Aspects of his routine remain the same, and he finds it important to rely on some consistency to structure the various elements in his to-do list. Every day, Abdelsad wakes up from 5:00 to 5:30 in the morning and goes to the gym before work. Before turning on the computer, he writes everything he wants to do on the day on a blank piece of paper.
“If I don’t do that, I’m consumed with emails and everything that comes my way during the day and I don’t end up doing things that I think are really important,” says Abdelsad.
Here are some of the items written in this decision-making paper today:
1. Meeting with customers: Abdelsadek usually has a daily meeting with a customer to discuss orders and current product offerings.
2. Develop new product ideas: Abdelsad communicates with various Mastercard teams about new ways to use data and the development of products in the sky.
3. Tutoring others: Allow time to make a daily phone call with a small Mastercard employee to discuss ideas and career issues.
4. Meeting with industry experts: As an example, Abdelsad Bricklin Dwyer meets with Chief Economist and Mastercard Institute of Economics to discuss trends affecting partner companies and Mastercard’s business.
5. Identify key business priorities: Abdelsadek spends a lot of time making sure that the company continues to think about what third-party information — in addition to its information — can help to complement and make Mastercard services more powerful. One way to do this is to work with JoAnn Stonier, Director of Data at Mastercard, to work regularly to improve the company’s data infrastructure and analytics environment.
The day-to-day task contributes to the interdisciplinary nature of data science
In Mastercard, data scientists need to be balanced so far, that is, to ensure the effectiveness of current tools while preparing new tools for problems and issues that they have not yet addressed.
“We have more than 2,000 consultants, data engineers and data scientists who are deployed to help clients with a variety of questions,” says Abdelsad. “For some of these products, we’ve been working with customers for years on a variety of issues.”
As Mastercard continues to expand community-driven partnerships, the company is focused on growing its data science team to continue with additional projects. In February, the data and services team announced plans to add more than 500 young university graduates and professionals.
The day-to-day tasks reflect the interdisciplinary nature of data science, according to data scientist Fuyuan Xiao and director of product management at Mastercard. Although new solutions are always on the agenda, Mastercard data scientists often rely on a coherent system to work in new areas of development.
“The nature of data science is to use techniques that include statistics, math, computer science, and information science to develop algorithms that can extract perspectives from data,” says Xiao. “When we apply data science to solve industrial problems, domain knowledge is also necessary.”
Xiao’s daily workflow is a four-step cycle. First, it defines business objectives based on customer needs and expert advice. Second, a statistical model is designed to determine the best type of algorithm to deal with the business problem. Third, Xiao and his team train and validate the model with a large amount of data.
“Model training and validation will be recursive, so it’s important to use the right programming tools and systems,” says Xiao.
Finally, the team applies what they have learned to solve the specific business problem and help the customer meet their goals.
Communication and collaboration are an everyday part of being a data scientist
“Many data scientists in the field know that there is not enough coding, or interpreting data, or creating automated data paths, or machine learning, or that there is not enough talk,” says Joel Alcedo, vice president of data science and applied economics. Mastercard Institute of Economics. “The most successful data scientists I’ve seen are just as good, like a Swiss Army knife with a variety of tools that can be applied at any given time.”
Data scientists do not manage technical issues and provide product code; they often come into contact with internal and external actors.
Any solution related to data science involves cross-departmental collaboration, including customer service, product development, business development and account management, etc., says Vishal Arora, Mastercard Advisors’ senior data scientist and customer service management team.
So even though a data scientist’s daily routine changes dramatically, it basically involves a mix of technical tasks, analysis, and communication every day.
“Effective data scientists also know how and where there are gaps in their team’s workflow and work with the right stakeholders to improve team effectiveness,” says Alcedo. “They also know where the big gaps in their skills are, and what they can learn from others and ask for help.”