Q: Can you tell us about your work at Data Plus Math, a Liveramp Company?
A: Certainly. At Data Plus Math, my primary focus is on big data exploration and ensuring data quality using tools like Spark, Scala, and Python on EMR. I also prototype and design Lift and Attribution Models on Amazon Redshift using SQL. Essentially, my work revolves around creating custom datasets for TV viewing populations to measure lift and attribution, which helps in optimizing TV ad campaigns.
Q: How did your previous experience at TiVo prepare you for your current role?
A: My time at TiVo was incredibly formative. I worked on predicting user-level probabilities for TV viewership using machine learning models like Random Forests and Linear Regression. I also developed methods for clustering TV viewers into heavy and light categories using the K-Means Algorithm. These experiences taught me a lot about handling large datasets and extracting actionable insights, which are crucial skills in my current role.
Q: Your educational background is impressive. How did your studies at Northeastern University shape your career?
A: My Master's Degree in Computer Science from Northeastern University provided a solid foundation in areas like parallel data processing, machine learning, and algorithms. Projects such as predicting crime locations using big data and performing principal component analysis on large datasets were particularly impactful. They equipped me with the technical skills and problem-solving mindset needed for my professional journey.
Q: You also had internships at Novartis Pharmaceuticals. How did those experiences influence your career path?
A: At Novartis, I worked on developing distributed tools for data analysis and performing graph database analysis. These projects involved handling complex datasets and developing tools to derive meaningful insights, which deepened my understanding of data science applications in the pharmaceutical industry. The skills I acquired there have been invaluable throughout my career.
Q: Before diving into data science, you started as a Senior Software Developer at Infosys. What were some key learnings from that period?
A: At Infosys, I worked on optimizing SQL queries and resolving production defects, which gave me a strong grounding in database management and agile methodologies. Developing new system functionalities and working with technologies like Java and PL/SQL were crucial in building my technical expertise.
Q: Let’s talk about your technical skills. You have a wide range of expertise. Can you elaborate on that?
A: Sure. I’m proficient in Python, PySpark, Scala Spark, and various SQL and NoSQL databases like Presto, MySQL, and Amazon Redshift. I also use tools like Jupyter Notebook, Zeppelin, and IntelliJ IDEA for data analysis and development. These skills enable me to handle and analyze large datasets effectively, which is critical in my role as a data scientist.
Q: Outside of work, you have some interesting hobbies. Can you share more about them?
A: Yes, I’m an avid table tennis player and even won a bronze medal in the 2019 US Nationals in Las Vegas. I also enjoy playing cricket and regularly practice table tennis at the Boston Table Tennis Center. These activities help me stay active and balanced.
Q: What advice would you give to aspiring data scientists?
A: I would say focus on building a strong foundation in statistics and machine learning. Practical experience is crucial, so work on real-world projects and internships to apply what you’ve learned. Also, stay curious and continuously update your skills as the field is constantly evolving.
