SPOTLIGHT ON SILVIA SECELEANU, T'13

“Be careful to manage burnout—I learned early on that it will happen, it has nothing to do with your abilities or your strength, and it will come if you don’t take breaks or take care of your body and mind.”


DECEMBER 1, 2020 | BY LILY ZHU


Silvia is a Senior Data Scientist at Udemy, an educational technology company in San Francisco, where she is responsible for solving business and marketing problems using machine learning. Previously, she worked at Square as a Technical Business Analyst and Data Scientist and explored smaller startups. She started her career working for JPMorgan in Equity and Fixed Income Research in New York.

During her time at Duke, Silvia majored in Economics with a Finance concentration, minored in French, and likes to joke that she studied a bit of everything. She took courses at Duke Law School and Fuqua, did independent research on BRIC countries, studied abroad in Paris, participated in Duke Engage in Guatemala, as well as co-founded a startup and Duke’s entrepreneurial selective living group and incubator, the Cube.


Looking back at your time at Duke, what are you most grateful for?

The abundant resources and opportunities that were made available. Is there really anything you can’t do at Duke?!

Also, the relationships I developed! The Duke connection is strong enough that even after graduation it’s pretty easy to network and meet new Dukies. I also want to mention the amazing professors I invested in that also invested in me too. I never expected the amount of dedication they would have toward my growth and wellbeing.


Why did you decide to transition from equity research to data science? What are the biggest similarities and differences between the two fields in terms of skills, day-to-day work, work-life balance, etc.?

Equity and fixed income research was the perfect place to learn a lot about the world economies, financial markets and their complex instruments, as well as businesses. Ultimately, my heart was always set on startups and creating products that touch the lives of millions in a way that was more tangible to me.

The similarities: both fields are quantitative, and you need to commit to delivering polished work and convincing stakeholders/clients.

The differences: Finance at times felt easier because when you are client-facing, it’s clear what impact you have on the bottom line. The industry is also more established, so there are clear processes that help guide individuals through various challenges. Doing data science means you’re really involved in longer horizon projects, so the nature of the work is quite different. Tech data can be very messy, so the running joke is that 80% of our time tends to be spent on data cleaning and processing, and 20% on modeling and the rest. Having said that, if you enjoy building a company, you can truly shape the direction of its growth with good data stories.

Skills wise, the jobs are very different. For finance, you need some spreadsheet modeling skills, maybe Visual Basic for Applications, and a solid understanding of the financial domain you are covering. For data science in tech, you need to be a good computer scientist, a decent statistician, and a solid machine learning engineer (depending on the role—what a data scientist does can be vastly different even between different teams within the same company).

Work life balance is what you make of it—you can work days and nights in both domains! But I hope you do it sparingly.


What advice do you have for people who are looking to make a career switch? How do you make time for a job search when you're working full time?

Make sure you learn coding/stats/machine learning somehow—pick up books, do online courses, attend a bootcamp, and practice. For a few years, I pretty much worked 7 a.m. to 11 p.m., but I was passionate about it. Be careful to manage burnout—I learned early on that it will happen, it has nothing to do with your abilities or your strength, and it will come if you don’t take breaks or take care of your body and mind (I’ll add soul too).

Look for companies that are larger so they can afford to train you and that have entry-level data science positions.


While navigating your career path, how have you served as both a mentee and mentor to others? I teach data science and mentor as part of a bootcamp. I want to help people because I myself felt so lost when I was trying to transition to data science and felt that the amount of knowledge I needed in order to make the leap was insurmountable. That’s not the case…

I have always found mentors to help me on my path—you should not do it alone, even if you can. Learn from others, take up mentors from all sorts of backgrounds, and always remember that mentors are great but they will—with the best intentions—guide you toward the universe they know. Have a good head on your shoulders and trust that you will have your own path and that sometimes it means you will need to go against some recommendations.


What sorts of problems are you solving through data science and machine learning at Udemy?

I work on the algorithms team, figuring out how to spend our marketing money, how to identify high-value opportunities and users, and how to measure whether we’re successful in what we’re doing. Soon, I’m going to be working more on our Structured Data projects that involve tagging with the use of natural language processing. This will eventually feed the personalization and recommendation systems at Udemy, which are some of the highest impact areas as far as a marketplace is concerned.


How has COVID-19 impacted the educational technology industry?

It’s been a boon—Edtech was well-positioned for this sort of a situation. I’m curious what will happen with all the successful fundraising that we’ve seen across the industry—will that lead to companies not just expanding but investing more in research and product? And will these trends last? I’m answering your question with a question I ask myself a lot these days.


What do you enjoy doing in your free time nowadays? How have your interests or hobbies changed due to stay-at-home orders?

I’ve been updating my own skills in machine learning and statistics these days. I’m very lucky to have very supportive leaders who have a passion for teaching.

I also make sure to work out, and have enjoyed reading more—check out Homegoing by Yaa Gyasi. It’s a poetic book and incredibly timely.

I make sure to call friends and family often—we all need each other. It was much easier at the beginning of COVID, but I’ve noticed just how important that daily connection is for my mental health now.

I’ve also been building an app with my brother and doing art projects. Creating and contributing rather than consuming has been incredibly refreshing and energizing.


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