Juhi Mittal is a former Applied Scientist at Amazon and currently a Quantitive Researcher at JP Morgan. She has an amazing experience with big companies such as Google and has knowledge in topics such as Deep Learning and Convolutional Neural Network.
- How she got into the Data Science field
- The skills she believes is vital in the technology field
- Challenges she faced with amazing advice.
Nisha: How did you first get into data science?
Juhi: I always enjoyed my probability and statistics classes at college. I took courses on Artificial Intelligence and Machine Learning, which introduced me to ML algorithms’ maths, like what happens behind the hood in backpropagation. Thereby I did various online courses too to get some exposure. To get hands-on experience of how my learnings translate to the industry, I recently did my internship with Amazon solving transit time problems and currently working with JP Morgan building Machine Learning signals for E-Trading.
“I feel the key to becoming a data scientist is patience.”
Nisha: What are the key skills that you use every day as a data scientist, and how did you develop them?
Check out what books helped 20+ successful data scientists grow in their career.
Juhi: I feel the key to becoming a data scientist is patience. Modeling is just a tiny part of a data scientist’s job. It is crucial to building scalable ML models that can be used in the industry, which demands a good grasp of software engineering concepts. It is essential to find the sweet spot between your model performance and latency, requiring a thorough understanding of ML models. I’d say few of these come with experience, but one can always read articles, books and take up challenging projects to get started.
“Data is notorious; if not labeled or cleaned correctly, the results you may get may be significantly different than what you’d expect. “
Nisha: What are the top challenges you currently face as a professional data scientist, and how do you go about tackling them?
Juhi: A challenge I currently face in the data scientist field is often around getting the correct data. Data is notorious; if not labeled or cleaned correctly, the results you may get may be significantly different than what you’d expect. Also, fetching data in real-time is always a challenge; processing the inputs is time-consuming, shooting up the latency. Good knowledge of data structures is essential to handle the data efficiently. I always make it a point to understand how the data would be available in real-time and how to compute features efficiently. So it is essential to have a vision of the complete ML pipeline and not just the modeling part.
Nisha: How important is the domain knowledge of the business/industry you’re in as a data scientist, and how did you acquire it?
Juhi: While domain knowledge can be acquired once you enter into a particular industry, I feel it is essential to understand the domain and visualize the problem you are trying to solve after entering the field. Data science, in my opinion, is not just about technology; it’s an art where you have to convince various stakeholders. A compelling story adds credibility and trust to your model, which otherwise is a black box to many.
Nisha: 3 words that best summarize how you learned ML and data science:
- Tons of hard work
Nisha: Courses: what courses/programs have you taken that have significantly contributed to advancing your career in data science?
Juhi: I took courses on Machine Learning, Artificial intelligence, Information Retrieval in college that introduced me to the field and did the deep learning specialization certification to broaden my understanding. I particularly enjoyed various lecture series on youtube, namely- CORNELL CS4780 “Machine Learning for Intelligent Systems” by Kilian Weinberger, Machine Learning Course MIT OpenCourseWare. These courses and reading articles on Medium provided me with deep insight into the Machine learning field in general.
Nisha: What is the biggest improvement that you introduced in the last 12 months that has considerably improved your workflow?
Juhi: Recent corporate experiences have helped me transition from a theoretical person to a more practical person. The changes I’ve introduced are on two fronts- personal and professional. On the professional side, I’ve realized that my present code will be somebody else’s tomorrow and that I need to write it in a way that people can understand in my absence. I’ve started studying in detail large-scale machine learning systems. Knowing Machine Learning-based modeling is just the beginning; to become a leader in this field, one must know how to design the whole infrastructure so that the ML model can be part of the system. Also, the pandemic has made me realize how unorganized and inefficient I had been in managing my time. Delaying the work thinking I’ve got the whole day till night and later realizing there’s so much to do not only makes me exhausted but leaves no time for myself and my family. I’ve learned to wear different hats and not think about anything else while I’m working, and this has helped me get some time for myself towards the end of the day, which leads to the changes I’ve introduced in my personal life. I’ve begun to give my body long-due attention and have started including meditation and physical exercises, which surprisingly leaves me more energized than I’d expect.
“The data science ocean is vast and deep.”
Nisha: What advice would you give to someone who wants to get into data science today?
Juhi: The data science ocean is vast and deep. There’s no denying it can get overwhelming at times. As you learn to swim, don’t worry about cutting-edge styles. Focus on the basics, and you’ll build a lasting foundation that will allow you to reach the cutting edge of many different fields. Once you’ve learned the primary art, explore various areas in it, and try to dive deep into what excites you the most. And lastly, try to get as much hands-on as possible because you always get to learn something you previously didn’t know.
Nisha: What inspires you about working in Data Science?
Juhi: Data science feeds my curiosity for the why of things perfectly. From being able to draw insights from data to being fascinated by the innovations around you, there seems no end to this. Being a part of this revolution affecting millions of lives pushes me to give my best each day.
Nisha: Tag one or two people in your industry who you would like to see answer these questions.
A young data scientist, who wishes to explore the different ways that Artificial Intelligence can help benefit the longevity of human life and conquer terminal illnesses. I would also like to explore peoples opinions on Artificial Intelligence and what they believe it brings or does not bring to the table. There are so many unanswered questions, which I would like to get more insight on.
Are you interested in sharing information and knowledge about AI and connecting with some of the brightest minds in the field?
Learn about joining as an Associate Editor.