It’s often said that variety is the spice of life — and this is especially true in the life of Evan Pease, a senior data scientist at Cisco Meraki.
Data science is a versatile — and in-demand — profession, with practical applications to essentially all forms of business. For the team at Cisco Meriki, data science helps track the customer journey into cloud-based network management to improve the overall experience. By tracking customer behavior, Pease’s team is able to accurately address customer pain points and obstacles in their transition before they arise.
And for the team at Chime, data science has played a direct role in new product features that customers love — like MyPay, which allows users to access payday funds ahead of time. Like the team at Cisco Meraki, a variety of idea sharing at Chime is what keeps Data Scientist Li Wang inspired and energized to create new, data-powered solutions for her team and Chime customers.
With a mix of storytelling, decision making and building tangible solutions for customers, a day in the life of a data engineer is anything but bland. Built In San Fransisco sat down with Wang and Pease to hear more about the exciting impact data science brings to their daily work lives.
Cisco Meraki simplifies the digital workplace by providing cloud-controlled Wi-Fi, routing and security solutions.
What does a typical day for you at Cisco Meraki look like?
Our team sits within the Cisco Meraki engineering organization, but we collaborate across nearly all Meraki teams. We have a mix of long-term strategic projects like adding predictive machine learning or descriptive analytics into customer-facing features, short-term analyses for things like helping executives make data-driven decisions quickly and dashboarding work in Tableau.
As a senior data scientist, I often have a few short meetings each day for project updates and syncing with stakeholders, and I will also try to pick up a code review to keep my teammates unblocked. That allows me about two-thirds of each day to work on assigned tasks for my current projects. Depending on the stage of the projects, this could include data discovery with subject matter experts within product or engineering, or constructing a dataset with SQL. Our data is warehoused in Snowflake, so SQL proficiency is a must-have. I regularly work in Python using Visual Studio Code, which integrates nicely with Snowflake and Gitlab. Depending on the requirements, I will use some AWS, Kubernetes or other tools for analysis or modeling projects.
Tell us about a project you’re working on right now.
As the networking industry shifts toward cloud management, more and more Cisco Catalyst devices are migrating to the Meraki dashboard for management. I’m working with others from the Meraki data science team and counterparts on other Cisco networking teams to link these migrated Catalyst devices to their most recent Cisco order. The analysis of this data will tell us how Cisco customers are progressing in the transition from on-premises to cloud management. Understanding this change in customer behavior is critical to the future of our business. I am also getting ramped up on a generative AI project to make customers’ network management experience simpler.
These two workstreams highlight one of the most appealing aspects of data science at Meraki: variety. We’re applying new data science solutions to simplify network management and performing exploratory analyses that can help us understand the future of our business. Regardless of the technologies used, I find it most rewarding when the results of my work change the opinion of a stakeholder. It’s a valuable learning experience for all involved and can lead to better business decisions.
“Regardless of the technologies used, I find it most rewarding when the results of my work change the opinion of a stakeholder.”
What’s the culture like on your team? How do Cisco Meraki team members grow their knowledge and connect?
Our team has changed a lot in the last five years. When I started in 2019, I was on a team of two data scientists and three data engineers daily in the beautiful San Francisco office. Now, there are close to 30 of us, and we work primarily remotely — though it is nice to have the flexibility to coordinate in-office days with other local colleagues. We were small pre-pandemic, but we established a strong and caring team culture, which persisted after we shifted to remote and expanded.
Today, we attend regularly scheduled standups with our immediate team and engineering-wide meetups for technical updates from other teams. We’ve also attended conferences with other machine learning and software engineering colleagues on various Meraki teams, which helps to foster strong cross-functional working relationships. I think that has helped us all to keep learning. My team also has monthly social lunches to catch up with each other and to celebrate milestones: a paper accepted to data science-related conferences or journals, a feature launched, a new teammate joined or a new family member added. It’s nice to remain connected in these different ways.
Chime is a mobile banking app with features like no hidden fees, early direct deposit and automatic savings.
What does a typical day for you at Chime look like?
Data Science is crucial to a company’s success. Data Scientists manage relationships with stakeholders and translate business problems into analytical insights, process automation, experimentation and machine learning solutions.
Since I joined Chime over four months ago, I have been on a financial security cross-functional team to provide data science support to MyPayTM, one of our biggest new products. I’m currently working with another Data Scientist on developing a new ML model for MyPay.
ML models shine the most when they provide business value to the stakeholders. To launch a product like MyPay, many teams have to chime in, so I spend a lot of time understanding the updates on the legal, compliance and design sides — and especially on the product, risk and engineering fronts.
In addition, I spend most days conducting exploratory data analysis, engineering features, creating machine learning models in the model development phase, evaluating the model performance, updating the stakeholders in the later phase of the model development and, more often than not, troubleshooting different issues and solving problems.
Tell us about a project you’re working on right now.
When SpotMe* a fee-free overdraft product, was launched in 2019, most banks still charged overdraft fees. But over the years, many banks have dropped them. Chime’s member-centric trailblazer has made a positive impact on consumers.
MyPay is also a game changer. It empowers members by providing them with funds ahead of payday. It is my great privilege to contribute to a product that has such tremendous potential to help members with my data science expertise.
Different data scientists may have different answers to this, but I love framing ML problems and designing end-to-end ML solutions. Thinking about how MyPay relates to other products at Chime and how MyPay brings value to Chime is very interesting to me. I also really enjoy brainstorming different model approaches with other Data Scientists and cross-functional partners.
“Different data scientists may have different answers to this, but I love framing ML problems and designing end-to-end ML solutions.”
One potential challenge of working on a super high-impact product is staying informed when there are many moving pieces. Thanks to Chime’s culture of encouraging documentation and teams sharing updates in public, the challenge becomes a learning opportunity.
What’s the culture like on your team? How do Chime team members grow their knowledge and connect?
I am a part of the data science and machine learning team and based in San Francisco. In addition to the financial security data scientists I work with day-to-day, I also work with data scientists from other business functions and ML platform engineers. Those of us based locally also enjoy having lunch together in the SF office.
Our team is very supportive of individual growth and learning from each other. We have brown bag Thursdays and other technical learning sessions to stay updated with what other data scientists and engineers are up to.
Chime also supports the culture of learning and sharing. We have biweekly experimentation hours, demo hours, insight hours, research hours, etc., so all Chimers can keep up to date with other departments’ work.
My favorite learning experience so far is Hack Week. We had three days to turn an idea into a product, and it was a thrilling experience. That Friday, we had five hours of demos. It was so inspiring to see all the wonderful ideas and realize that I get to work with so many brilliant and humble colleagues. Fun fact: MyPay was once a Hack Week idea!
