By Orson Clay on March 1, 2021
- Find things to be excited about during job interviews – enthusiasm can set you apart from other equally qualified candidates.
- As you interview for jobs and after you get offers, also consider how well the company and team fit your personality and goals.
- Graduate research is difficult and has sparse rewards – practice now and aim for consistent progress once you’re in a program.
Orson: What got you interested in math in the first place and how did that lead you to ACME?
Sydney: In high school, I got really excited about airplane route optimization and flight itinerary suggestions. Through this, I got my first taste of what data analysis and optimization look like to consumers. When I got to BYU, I wasn’t sure exactly what I wanted to do, but I started as a math major intending to explore math and statistics in search of a path that would let me do the same sort of applied analysis that I had found before. Three days after I moved to Provo, Dr. Evans pitched ACME at the Math Department freshman orientation, and I was completely sold. I added ACME the next week.
Orson: During the ACME program how did you use textbooks, students, professors, and other resources to learn the material?
Sydney: Textbooks and lectures were my primary resources for getting an initial taste for the material, but most of my deep understanding came from collaborating with other students on the homework. My cohort was full of incredible people and I relied on them constantly. Not only was I able to ask people questions about the material, but I was also able to help others, which enabled me to better understand what we were learning and think about concepts from a new perspective.
Orson: With the rigorous demands of the program, how did you balance time between coursework and finding your job?
Sydney: It definitely took a while for me to find any sort of balance between coursework and other tasks. My first semester in ACME felt a lot like just barely surviving with enough understanding of the material to keep moving forward. In my second semester, I was doing a data science internship in Draper at Proofpoint 15 hours a week, which forced me to define a more structured schedule for myself. This ultimately led to starting homework assignments earlier, so it was easier not to feel panicked and rushed with my work. I didn’t need to spend much time actively searching for jobs, mostly because I didn’t need to apply to many positions before I got one that I wanted. This was because I started looking very early and (according to my boss at Proofpoint) because I was really excited about the opportunity and curious about what the team was working on throughout the interview process. It was also easier to get a second internship because I had done a data analysis internship after my sophomore year.
Orson: Could we take a quick detour to talk about your internship at Proofpoint? How did you land that in your second semester? Could you talk about the value that experience had in your career development?
Sydney: I found Proofpoint at the fall STEM fair, kind of by accident. I think many people overlooked it because Proofpoint isn’t a very sexy company – one of their main products is for corporate email security and they don’t advertise free lunch. I also didn’t think much of it when I found it, but after several interviews I realized that it was a much better fit than I thought. The data science team was doing an interesting application of NLP to Twitter data, which made it a great place to learn about machine learning applied to real, messy data. My prior experience working with real data from my sophomore internship, my background in statistics, and a knowledge of some basic data science best practices were extremely helpful in doing well during my interviews to get the position. That experience got me job interviews in my senior year because I knew about models and tools used in real settings. I also think it was helpful in getting into Yale, since it gave me 16 months of hands-on experience reading and implementing research papers, which helped shape my interests and provide talking points in my personal statements and interviews.
Orson: What initially lead you to Zenabi?
Sydney: Initially, I was excited about Zenabi because they seemed like a fast-paced company with a wide range of interesting problems and experienced employees. I was also working with a two-body problem because my husband (who graduated in the third ACME cohort) was also looking into industry data science jobs and graduate school at the same time. Many of the programs we liked were in the New York metro area, the location was also convenient for moving forward with our careers.
However, I will say that one of the important lessons I learned from my time at Zenabi is to vet the company you’re thinking about joining as seriously as they are evaluating you. I think at the time I was so excited that people were interested in me and my work that I overlooked some big warning signs that I now know to look for.
Orson: How did ACME prepare you for the job? What did you wish you learned before starting?
Sydney: I felt prepared for the job in large part because of my comfort with learning independently and dissecting equations. ACME taught me how to read about a concept on my own, think about it deeply, try it out, and then ask questions. I’ve used this process over and over both in my job and in graduate school. On a more functional level, my internship was critical in teaching me about tools that I used at my job, such as Tensorflow and AWS. The biggest thing I wish I’d learned before I started my job was to start simple and establish baseline results before moving on to a more complex model.
Orson: How did you decide to pursue a graduate degree? Was this the plan all along?
Sydney: I had not originally planned to get a graduate degree, but I started thinking about applying as I learned more about my Proofpoint coworkers’ experiences with graduate school. From them, I learned that Ph.D. can indicate to employers that you know how to think independently and test new ideas. It also enables the pursuit of jobs in academia, which is not possible without a Ph.D. I also realized that the parts of my job that I like the most were learning about current methods of deep learning, and then implementing them and expanding their application. The next semester, I started doing research with two other members of my cohort in Dr. Wingate’s lab in the CS department, which I really enjoyed. Another big factor in my decision was that I did not like the day-to-day work I was doing at Zenabi. I was more interested in learning new techniques and exploring how they could be used than in optimizing model performance and doing data analysis.
Orson: What is the subject matter like at Yale? How does it differ or complement ACME and your job experience?
Sydney: At Yale, coursework is not the emphasis for Ph.D. students because our primary goal is to learn how to do research. But we do have course requirements for our first two years, and I primarily chose courses that expand on the topics I learned in ACME. My favorite class that I’ve taken so far focused on using tools from linear algebra, mostly eigenvalue decomposition, to model data transparently. Without ACME, I doubt that I would’ve gotten much out of the course, but since taking the class, I’ve used techniques that I learned there in my research.
Concerning my research, the most important difference between it and ACME or my industry job is the structure. Compared to coursework or a job, where there are clear deliverables and expectations, research is about discovering something new. As a new researcher, it’s often unclear precisely where that end result is going to be or exactly how to get there from the start. Also, I am the one defining my own route forward, so there is no ‘next chapter’ to read – there is a ever-growing body of literature to sort through. Learning how to find recent, relevant papers, reading them quickly, and drawing my own connections between them is a critical feature of my current job that I haven’t learned anywhere else.
To students who are seriously considering a Ph.D. program, the best advice I have gotten has been from this article, which articulates well the paradigm shift needed to be a successful Ph.D. student.
Orson: Where do you see your experience taking you after completing your degree?
Sydney: Right now, I plan to work as a researcher in the tech industry after completing my degree. I am currently researching social robotics, and I have found that I love working in a space that is so applied. I like that my work can be relevant outside of my own niche, so I hope to continue working in a similarly applied field after I graduate. This summer I will be a research intern at Argo AI, which makes autonomous vehicles. After graduation, there are a couple of teams at Microsoft and Disney that work with social robotics that I’m really excited about right now, and the autonomous vehicle space is full of opportunities in human-computer interaction.
Orson: How were you prepared for the application process and what do you wish you had known before starting?
Sydney: The best thing I did to prepare was to spend a lot of time searching for programs and faculty members whose work I enjoyed reading. This helped me to narrow my applications to include only schools where I was interested in the research and helped me to tailor my personal statements for each school. Once I was admitted, I also spent a lot of time asking questions and getting to know the advisors and other graduate students, which was really beneficial for actually making the decision to accept my offer at Yale.
Before I started applying, I wish I’d known that admission into a Ph.D. program is not a science – it can be dependent on many factors outside your control, like funding, space in labs, or the applicant group that year. The first time I applied I was not admitted anywhere, which made it difficult to apply a second time without this perspective. I also wish that I would have spent more time before I applied talking to current graduate students and faculty about their experiences and advice. That could have helped me to both develop my research interests and know what I was looking for when evaluating programs.