Meet the Team: Ryan Jones, Codecademy Senior Data Scientist

Everybody’s career journey looks different — even the journeys of our own team members! That’s why we’re so excited to share stories and advice from Codecademy’s Software Engineers, Data Scientists, Product Managers, and more to show people what it’s like to work in tech.

Today we’re spotlighting Ryan Jones, a Senior Data Scientist on Codecademy’s Engineer team who’s been with us for two years.

Tell us about yourself! How’d you end up working in tech?

My career journey has been somewhat unconventional. I was slow to earn a college degree and spent time working in various roles across industries.

Once I finished my degree in mathematics, I secured a job teaching high school math and stats. This was by far the most rewarding role I’d ever had, and as a result I grew personally and professionally very much during this time.?

While teaching, I became aware of an exciting career that combined statistics, math coding, and scientific rigor – data science. Feeling confident with my math/stats background, I focused on learning to code, specifically SQL and Python. I spent many hours over the course of one and a half years becoming proficient in these tools and using various online resources, including Codecademy!

My hard work paid off when I secured an entry-level job as a Business Analyst and was accepted into a graduate program for Data Analytics. From that point forward, I focused on completely immersing myself in data science. I learned topics in school and immediately applied them to my work. Later, I moved to a consulting company and worked as a Data Scientist. The work wasn’t always compelling, but I learned to manage my time and projects more efficiently.

In July of 2022, I joined Codecademy as a Data Scientist. It was, and still is, a dream job for me. I truly love the work that I do and the people that I work with.

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Tell us about your role. How would you explain your job to a stranger?

I consider myself lucky because I get to work in many different spaces under the broad umbrella of data science. The best part though is that I get to collaborate with smart, talented, respectful, and supportive people from many different teams.?

We provide high-visibility analyses for stakeholders that affect product and strategy decisions. We create data models that power dashboards and API endpoints; construct models to serve predictions and recommendations; and support project managers performing experimentation of new features in our product. We also build exciting proof-of-concepts to explore the feasibility of potential solutions to known problems.

One example of an impactful project I’ve taken part in is that we’re now serving recommendations to learners in multiple locations within our product. These recommendations are based on the similarity of content across our many different content types, which allows us to serve them to all visitors, both enrolled and anonymous. The desire for additional recommendations is growing and it will be a space that we continue to progress, which I’m excited about it.

This “full-stack” data science work isn’t necessarily for everyone though. It can be difficult to context switch throughout the day, especially when the work is highly technical and requires intense focus. Other professionals may prefer to hone their craft within a single subspace, but I prefer the wide breadth of work that I get to take part in on a day-to-day basis.

* Learn more about the different careers you can have within data science here.

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What advice do you have for people interested in a role like yours?

If you’re early in your journey to become a Data Scientist, I would recommend focusing on two primary areas: learning to code and gaining a fundamental understanding of statistics. These two technical areas are paramount in the field.

If you are in a related role and you are having trouble advancing or shifting to a Data Scientist position, try not to get discouraged. Keep a healthy perspective while continuing to grow. Doing projects, either collaboratively or solo, is one of the best ways to learn, practice, and showcase skills.

Finding others with similar interests and ambitions is also a fantastic way to stay focused. I married a fellow Data Scientist, but you certainly don’t have to go to such an extreme — we have a Community at Codecademy just for this!

And where possible, adopt habits that will help you with sustained commitment, as opposed to quick bursts of effort that burn out quickly. This can be very hard; it certainly is for me. James Clear’s Atomic Habits helped me to better understand how habits are formed and how we can consciously design and adopt the habits we want.

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Ask questions, and then ask more questions, and then a couple more for good measure. From my personal experience, and my experience as a teacher, I’ve learned that if there’s one person that doesn’t understand something there’s likely others who don’t understand either.

What do you wish you knew before you began your career?

Be confident and continue learning. And I don’t mean “fake it ‘till you make it” confidence. Quite the opposite actually.

First, I would tell myself that everyone experiences some form of impostor syndrome. Take a breath, you’re okay. So, you don’t know something. No big deal, it's just another opportunity to learn something.

Secondly, ask questions, and then ask more questions, and then a couple more for good measure. From my personal experience, and my experience as a teacher, I’ve learned that if there’s one person that doesn’t understand something there’s likely others who don’t understand either. Asking clarifying questions will align everyone involved in the conversation and set the stage for collaboration.

Thirdly, the field advances rapidly, so you must devote time to continuous learning. This isn’t inherently a bad thing, but the job requires persistent effort, sometimes outside of work, even just to maintain relevant skills.

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What’s something you want to learn next?

I regularly use Codecademy to re-learn technology topics and to upskill. One awesome perk of my job is complete access to the suite of content and features that Codecademy offers. Of course I’m going to take advantage of that!

Recently, I’ve adopted the frequent practice of reading about advances with large language models. My Google news feed has grown accustomed to this habit and serves up curated content for me daily. These reads can be light or in-depth depending on the topic and my mental capacity at the time. Sometimes I dig into research papers and check out related GitHub repositories to better understand how the logic works.

I’m also working on improving my software engineering design principles and spend time on the weekends and evenings coding small programs using different design patterns. I spend little time writing production code for work, but I still find it to be a valuable experience. It has helped me to better understand some of the production code I come across in my work and personal endeavors.

Unrelated to my career, I recently learned to knit, and I want to learn and practice needlepoint next! I’m not good, yet, but it’s very relaxing and I love having an end product I can wear or share.

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What’s your favorite Codecademy course?

Because I typically use Codecademy for very specific topics to upskill or re-learn, I focus on some small module or group of content items to gain the understanding I’m in need of. This means that I have 25+ courses or paths started, and very few completed!

I recently started the Software Engineering for Data Scientists career path to help upskill my software engineering skillset in conjunction with the work I’m doing externally to better understand software design principles.

Our courses in Python are fantastic. They cover topics ranging from beginner through advanced use cases. I’ve found myself in these courses many times throughout my career, before and during my tenure as a Codecademy employee.

But my favorite course calls out to the stats and math nerd inside of me. I thoroughly enjoy Learn the Basics of Causal Inference with R. This isn’t material that’s taught as part of normal curriculum in high schools — I would know! I’m sure some people have heard the adage “correlation doesn’t imply causation” but haven’t gone beyond that to understand how and when one can infer causation. This course is great whether it's new material for the learner, or just a refresher. Five stars!

*Interview edited for clarity and length