Pushing Forward Progress: A Day in the Life of a Data Scientist

A typical day for a data scientist might vary, but the goal remains the same: to efficiently address real world challenges.

Written by Mia Goulart
Published on Jun. 25, 2024
Pushing Forward Progress: A Day in the Life of a Data Scientist
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8:30 – 9:00 a.m. — Start the day.

9:00 – 10:00 a.m. — Pair programming.

10:00 – 10:30 a.m. — Scrum.

10:30–11:00 a.m. — Prep for presentation.

11:00 a.m. -12:00 p.m. — Lunch. 

12:00 – 4:30 p.m. — Code.

No matter which tasks fill a data scientist’s daily itinerary, the goal remains the same: to efficiently address real-world challenges. 

Be it optimizing shipping routes, assessing athlete performance, combating tax evasion or automating digital ad placements, data science is revolutionizing nearly every industry. BARC research reported an increase in profits for surveyed companies by 8 percent. 

Regardless of what time they start or end their day, innovators like Adswerve’s Jason Qin are pushing forward progress. 

Recently, Built In Colorado spoke with Qin for a glimpse of his day-to-day, and how his current projects will contribute to Adswerve’s success. 

 

Jason Qin
Data Scientist 2 • Adswerve, Inc.

Adswerve is a leading data, media and tech consultancy trusted by hundreds of brands and agencies.

 

Describe a typical day for you. What work do you tackle and what tech do you use?

My day-to-day tasks range from building pipelines to analyzing datasets to developing machine learning models. We often collaborate with cross-functional teams, which makes us fluent in many verticals of jargon. Whether we are discussing solutions with CXOs, marketing, analytics or technical-savvy folks like ourselves, our ideas are translated seamlessly to provide the best understanding for our clients. Our tech stack includes SQL, Python, C#, Javascript, Terraform and everything GCP-related. 

We pride ourselves on being a highly agile team that is able to handle anything thrown our way, quickly adapting to new challenges and finding innovative solutions.

 

Describe a project you’re working on right now. What’s the impact of this project, and what do you find rewarding or challenging about it?

The latest model I am currently working on is a propensity-to-churn model. This model aims to predict which customers are likely to stop using a service within a specific timeframe. The impact of this project is significant — its aim is to allow the company to proactively address customer retention, ultimately improving customer satisfaction and increasing revenue by reducing churn rates. 

However, the project presents several challenges. One major issue is managing the imbalance of labels in the data, which must be consistently accounted for. Developing a robust automated pipeline is essential for scaling and monitoring the model. Ensuring model explainability is also crucial —it allows us to convey our insights to stakeholders in an understandable way. 

Data science is key to driving critical business decisions; it empowers the company to be at the top of its game and pioneers new growth for the company.

 

“Data science is key to driving critical business decisions; it empowers the company to be at the top of its game and pioneers new growth for the company.”

 

What’s the culture like on your team? Are there any rituals or practices that enable team members to grow their knowledge and connect with each other?

Our team is incredibly diverse culturally and cross-continental, which stimulates open-mindedness and brings forth a wide array of perspectives. This diversity helps us approach problems with a multifaceted lens, leading to more robust, creative solutions. We have regular knowledge-sharing sessions where team members present their recent work or new techniques they’ve learned, fostering a culture of continuous learning. Our team also organizes an annual boot camp to bring our fully remote team together where we can connect and strengthen our relationships both professionally and personally.

 

 

Responses have been edited for length and clarity. Images provided by Shutterstock and listed companies.

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