A Day in the Life of a Lead Data Scientist

CQF alumnus, Victor Acevedo, is a Lead Data Scientist at Banco de Credito del Peru (BCP). He graduated with a degree in Economics and a master’s degree in Statistics. He now has 10 years of experience developing analytical solutions related to credit risk and leads a team of data scientists focused on this business segment.

We caught up with Victor to find out more about a typical working day in his current role.

I work at Banco de Credito del Peru (BCP), the largest commercial bank in Peru. As Lead Data Scientist, I am responsible for deploying and implementing analytical solutions for credit risk problems. Most of the time, the solution involves estimating inputs for expected loss, namely probability of default, loss given default, and exposure at default. We also build through the cycle version of these parameters as needed for economic capital requirements. Lately, I have been dedicating much of my time to designing a new workflow for building a loss given default model for my business segment. Below, is my typical working day.

8:30 AM - 9:00 AM

I start work with a quick review of my emails and meetings for the day. I also check the sticky note I have on my desktop with the messages I would like to give the team throughout the day.

9:00 AM - 9:30 AM

Time for a daily meeting to coordinate with the team. I usually ask for a quick summary of the previous day's progress and about any issues that may be holding up the team's progress to see how I can help.

9:30 AM - 12:30 PM

I look for an excuse to get up to stretch my legs and then go back to work. Depending on the daily meeting with the team, I have to decide if I will spend this time focusing on solving operative problems or working on one main task.

During this time, users will sometimes call me to ask about the models we develop. The analytical solutions that we provide to the business units are wide-ranging. They can go from mitigating the risk in specific sectors of the population to generating relevant offers for a target audience. However, the majority of our work is focused on developing models for the prediction of credit risk. We often discuss with our users the type of algorithm that should be used, main assumptions, how the solution can be deployed, and development times.

12:30 PM - 14:00 PM

If I am in the office, then I take advantage of having lunch with the team. Otherwise, I have lunch with my family for approximately 1 hour and use the rest of the time to read world news or rest and forget about my phone.

14:00 PM - 14:30 PM 

After lunch I prefer to spend my time on operational tasks instead of continuing some of the more mentally strenuous work I started in the morning. For example, I often use this time to start answering emails that have come in during the day.

14:30 PM - 17:30 PM

I continue working on the proposed solutions to business problems and then present them to the users. I usually meet with the team at some point in the afternoon in case they need my help.

17:30 PM - 18:00 PM

I review my emails one last time and write down list of things to start or continue the next day. Then it is time to go home. Sometimes, later in the evening, when everything is quiet, I spend some time reading articles related to my field or think about how what I learned on the CQF program can be applied to innovate what we do at work.

Find out more about Quant Finance Careers

If you are interested in becoming a lead software engineer, explore the CQF Careers Guide to Quantitative Finance. Learn more about the skills needed and average salary you can earn in North America, Asia, and Europe for key career paths in quantitative finance.