I sat with Megan Robertson, Data Scientist at Nike to speak about her journey. Megan is an incredibly dedicated and smart young woman. With eloquence, she can alternate between speaking about history and current affairs, to discussing theoretical mathematics and statistics, to being a leader on the basketball court. Read more about Megan and hear her tips on how to land a job as a Data Scientist.
Did I mention that she is an amazing photographer too! Checkout her data science work, her published papers and her photography on her website here.
Tell us a little about yourself and how you became a data scientist?
My name is Megan Robertson, I work as a data scientist at Nike. My team and I do a lot of work predicting the lifetime value of our customers based on what they have purchased in the past, and what they might purchase in the future.
My story about becoming a data scientist is frankly more straightforward than most people who enter this field. During my undergraduate studies at Amherst College, I studied History, Math and Statistics. It was during the spring of my junior year that I made a decision to go to grad school. After having been to many conferences, and having had countless conversations with my professors, I was very encouraged to go to graduate school to bolster my computer science and theoretical math skills.
Therefore, I chose to attend Duke, which is one of the top schools in the country for an advanced degree in statistics. I really liked their project-based approach, which motivated us to learn the math, apply it to a data set, and ultimately draft a report and present the findings. This really strengthened my presentation skills and taught me how to communicate with a less technical audience.
We actually had an entire course dedicated to doing ‘statistics consulting’, where PhD students and community members would come to us with various problems, and we were tasked with exploring and communicating a course of action on how to go about solving a particular problem. Having gone to Amherst, where there was a lot of emphasis on writing, so I was more comfortable making super complicated ideas sound simple.This is why I strongly believe that statisticians and data scientists who posses such great knowledge of different area of statistics must also have a solid base in communication.
I had quite a bit of fun at school, I enjoyed being handed a messy data set, finding the story within that data and then talking about it with different people. I thought that’s what a data scientist is and decided that’s what I wanted to do.
I strongly believe that statisticians and data scientists who posses such great knowledge of different area of statistics must also have a solid base in communication.
What was you most fulfilling or fun project? What did you learn about yourself?
My favorite project was my master’s thesis, where I had an internship with the Charlotte Hornets, the professional basketball team in North Carolina. I used the data collected from their tracking system, which records information every 1/8th of a second,to better predict the upcoming shots and the overall game.
I really enjoyed being in professional sports analytics. Given my background as a varsity basketball player, it was a great opportunity to combine my love for the game, and my love for data science. The project turned out to be harder than I thought, like most things, but it was great to try many approaches and explore potential solutions to provide the Hornets with better predictions to help them with scouting prospects.
What professional advice do you often share with others?
Given that I was looking for opportunities and interviewing quite frequently in recent memory, I would suggest to applicants and anyone networking to ask the following question: If you could go back and change something, what would you change?
Personally, If I could go back, I would take more computer science courses. Although coding and programming languages vary widely by company, here we mostly use Python, R, SQL and google cloud, I think that it is very important to have at least a knowledge of the fundamentals of computer science, this will make you a better data scientist.
Most often, when talking about Data Science, most only mention the cool projects. You only rarely hear anything about all the wrangling work, all the data cleanup, and all the resulting trials and errors. This is mainly due to the fact that talking about all of that does not really bring much value to the business. Nevertheless, it is during these trying times that a data scientist learns the most, and it is important for new people attempting to enter the field to understand that wrangling and cleanup often constitute the majority of the work in data science.
Show that you’re always learning and tailor your projects to topics that interest you.
What skills do you consider crucial to learn?
When It comes to data science, I encourage people who do not have a lot of background in it to spend some time understanding the concepts and tools behind big data. On the job, I am still learning about what is possible given our millions of transactions per day, and working to understand what can be reasonably accomplished given the computational power that we have.
I also think that another crucial skill for data science is the production of a given project and learning how to speak about it with the right people. While some companies may have silos, at Nike, I am always communicating with the engineering team, and that has been a very important aspect of our team having a successful and efficient work culture.
Do you find that women are often a minority in the Industry?
Yes, women in tech fields, in my opinion, are a minority. However, many things have changed in the past 2 years, and more women are showing up in big data and machine learning meetups, more women are being represented in schools (Duke had a 50/50 split), and conferences specifically for women in computer and data science are popping up everywhere.
Additionally, it is encouraging and inspiring to see more and more women in senior positions who have had to push the boundaries to make that possible. It is important to recognize that this is going to be a difficult journey for anyone attempting to break into an industry where people like them are not represented, nevertheless, it is equally as important to recognize this is not an insurmountable challenge.
it is encouraging and inspiring to see more and more women in senior positions who have had to push the boundaries to make that possible.
Do you have advice for young women excited to enter the field?
My motto is ‘Always Be Networking’, because you never know when that connection will lead to another connection or even a potential collaboration. Attend conferences, make a genuine effort to send personalized messages, build a meaningful connection.
My approach to connecting to people is similar to preparing for an interview. If I am preparing for an interview, I do my research, I try to learn everything there is about the company and I prepare my own questions. I try to make the same effort whenever I connect with people. Once you have connections, and have built a network, applying to jobs will be much easier.
Most of my advice comes from my recent job-hunting experiences. If you’re just coming out of school, it’s always a good idea to show your work, and have presentation ready examples that include your code. Make sure that your work in polished, take the time to cleanup your code, fix typos, edit your writings and see if you can get access to your classroom presentation recordings.
Obviously, learn everything on your resume and be prepared to discuss it in depth. I have been in interviews where they’ll pick a model or a statistical concept from the ones I mention and ask me to explain it to a technical and to a nontechnical person. Having great communication skills will carry you a long way.
And lastly, show that you’re always learning. Although you can do blog posts about one particular coding library or one statistical or machine learning concept, try to take on a whole project that incorporates many of those concepts. Try to tailor your projects to topics that interest you, for example, if you want to work in biotech, use plant data or analyze disease data, this will prove more beneficial to you in the long term.
My motto is ‘Always Be Networking’, because you never know when that connection will lead to another connection or even a potential collaboration.
What are you excited about in your professional future?
Right now, I am really excited to take ownership over my own projects and provide clear direction on how they will be used and how it will impact the company, I hope that this will result in greater visibility within the company. In the future, I’d like to grow into a team leader where I can interact with very smart people and continuously learn from them.