Drawing a wide range of professionals from engineers to analysts, data science has become one of the fastest-growing occupations in the United States.
According to the U.S. Bureau of Labor Statistics, a data scientist is someone who uses analytical tools and techniques to extract meaningful insights from data. However, that umbrella encompasses multiple tracks and specialities — including data engineer, data analyst and machine learning engineer — and the field is quickly growing. The Bureau expects employment of data scientists to grow 36 percent between 2021 and 2031, with 13,500 job openings projected each year.
Boasting diverse opportunities for growth and learning, data science is undeniably an exciting, albeit intimidating, field for early-career professionals hoping to make their mark in the tech world. To better support these ambitious data novices, Funding Circle’s Senior Analyst of Customer Analytics Kaitlin Crenshaw sat down with Built In Colorado to reflect on her career and share advice for the next generation.
Funding Circle connects small businesses with fast, affordable loan programs.
Describe your data specialty and current role at Funding Circle.
I’m currently a customer-centric data professional. My role revolves around the customer experience, from the first piece of marketing all the way through the final sale. Every day, I’m assessing and obsessing over how the customer perceives us, how they interact with us and how to improve their every encounter with us.
What this means from a practical perspective is that my typical day is split into a few main jobs: one, developing a deep understanding of the teams that interact with our customers and how they operate; two, streamlining and optimizing the data sets and reporting that everyone uses in making business decisions; three, leveraging modeling to identify pain points and optimize the way in which we work with people; and four, identifying opportunities to improve the way we work.
In reality, this is oversimplifying the job. Every day is different, and I’m expected to know what all of our teams do and be ready to apply and advise on a vast range of analytical techniques. This could mean that, one day, I’m designing a testing strategy to understand how customers respond to our marketing, and the next day, I’m optimizing our pricing strategy or running customer segmentation models
What influenced you to choose a career track in data?
My professional career started in strategy and technology consulting. I experienced firsthand the shortcomings of companies that made decisions without leveraging operational data and the successes of companies that did take advantage of their data. I found myself building out data solutions for leadership when none existed, either out of sheer frustration in the lack of progress or in a personal effort to better understand what was happening around me.
From these experiences, I knew that I wanted to have a seat at the table in helping make successful business decisions and building a business strategy that sets up a company to win.
I knew that I wanted to have a seat at the table in helping make successful business decisions.”
One other perk of more strategic roles is that there is an opportunity to apply a diverse range of technical skills. This means that I can keep my tried-and-true techniques sharp while integrating new skills that I’ve been wanting to try out.
What advice do you have for someone considering different career tracks in data science?
There are a few things to consider when picking a career track in data science, but the most important is to understand what drives you at work; what excites you? In data science, you can impact larger business strategies, be heavily specialized in machine learning and coding, or be somewhere in between.
You should also think about who you want to spend your days working with. Do you enjoy working with other business teams within an organization, working directly with customers or doing your own thing? Finally, do you want to be the “data person,” or do you want to have other data professionals around you?
You may find that your answers to these questions change over time, but the best part about working in data science is that you have highly transferable skills, so you can explore new career tracks if your answers ever change.
The final piece of advice I will give is to not obsess over the job title; companies use titles interchangeably. The best way to understand what the role will be like is to ask questions during interviews, get to know what types of projects are being worked on and determine what the tech stack includes.
