Mugdha Bhusari, Data Science Intern, Schlumberger Technology and Innovation Center
Mugdha Bhusari (MIMS ’20) spent the summer of 2019 as a data science intern at Schlumberger Technology and Innovation Center, the research center for Schlumberger, a company that provides technology for oilfield services.
Describe a typical day at your internship.
The research center (STIC), located in Menlo Park, has the expected Bay Area vibe with casual dressing, bean bags, and game stations. The center has tech experts and field experts. The tech experts work on oil and gas data in consultation with the field experts who guide them through the data.
My typical day starts at 8:30 am. After a quick look at my emails, I prep for a meeting with either my mentor, the field expert, or my N+1 manager. On Tuesdays and Thursdays, I attend Vinyasa yoga before lunch. After lunch, there is a ritual of everyone playing “running ping-pong,” which is 10 to 15 people taking turns playing a shot until there is only one winner left, or playing Mario Kart or FIFA video games. Every Thursday, there is a weekly machine learning team meeting where I and other interns present our progress over the week and get input from other interns and mentors. Every Friday, there is a research paper reading where one team member reads and explains a paper to the entire team. The latter part of the day is quiet to focus on work and code in peace.
What did you like best about your internship?
There are many things unique about STIC. It is a small center with only 30 full-time employees. So you often talk and network with everyone from UX, cloud computing, and ML teams, to field engineers. You get to hear about different projects and problems and have access to work on newer things.
The center is very diverse with people from all over the world. It is an innovation center and most projects are about testing what can and can’t be done with certain data. There is constant experimentation, so it’s an avenue to keep learning new and cutting-edge technology with the freedom to take the project in multiple directions.
How did your work at the I School prepare you for this role?
The courses I studied at the I School such as quantitative research methods, and natural language processing provided me the capacity to build the models.
I bring back the knowledge of how models are built in the real world and which are used by 1000’s of people.
Any advice for next year’s MIMS students as they prepare for their internships?
Something I’d like to emphasize is the realization that, unlike the data you deal with in school, data in the field is noisy and you should learn how to deal with such data. As we often hear: “In reality, data cleaning is what takes the most time.”
To all the incoming MIMSies — don’t get worked up. Things work themselves out and everyone gets an internship they are happy about.
Did your internship influence your career plans after graduation?
The internship gave me the reassurance that the choices I had made over the last few years are what I love working on. It also provided me clarity about which topics I’d like to get more of a grasp on during my remaining time at the I School.
