Mahesh Varavooru has over 20 years of experience in technology in Big Data and more than 8 years of experience in Data Science and Machine Learning. He worked for Citi, Bank of America and Morgan Stanley in various technology roles after doing his M.S in Computers from N.J.I.T. Mahesh is the founder and CTO of WildFire Force, an AI and Data Science consulting services company based in Basking Ridge, NJ. WildFire was started in 2013 and prior to that he has had multi-million dollar exits in two different startups.
How did you get into Data Analytics? What interested you in learning Data Analytics?
Mahesh Varavooru: When I was working for a large bank, I found that there is so much data which is being collected as part of the big data strategy but there was no proper use of that data. I learned that very few companies are actually getting value from this data. This sparked my interest in data analytics.
What was the first data set you remember working with? What did you do with it?
Mahesh Varavooru: It was customer accounts screening data. When a new client is on-boarded in the bank, it needs to be screened for any illegal activities. There is a large dataset which it gets screened against. Our goal was to screen the clients with less number of false alerts. It was an interesting project. I worked in python to write a machine learning algorithm to decrease the false alerts. The current system was based on rules.
Was there a specific “aha” moment when you realized the power of data?
Mahesh Varavooru: Everyone talks about the power of data. I would like to qualify that data needs to clean for it to make any sense. I realized as part of my first project that right data can empower organizations with great deal of competitive edge and also improve efficiencies in the processes.
What is your typical day-in-a-life in your current job? Where do you spend most of your time?
Mahesh Varavooru: As the founder and CTO of WildFire Force, I spend a lot of my time with our clients and new prospects explaining them how AI and machine learning can help them in their use cases. I help them analyse their data and find use cases fit for machine learning. A lot of my time is also spent on designing the architecture of our client solutions. I ensure that I still code at-least once a week developing different models.
How do you stay updated on the latest trends in Data Analytics? Which are the Data Analytics resources (i.e. blogs/websites/apps) you visit regularly?
Mahesh Varavooru: I am active on Linkedin and get regular updates on the latest in AI and machine learning from who I follow. I also visit the below sites regularly.
Share the names of 3 people that you follow in the field of Data Science.
- Yann LeCun
- Yoshua Bengio
- Andriy Burkov
Team, Skills and Tools
Which are your favourite Data Analytics Tools that you use to perform in your job, and what are the other tools used widely in your team?
What are the different roles and skills within your data team?
Mahesh Varavooru: Given we are a team of data scientists and ml engineers, we have a wide range of experience levels in this space.
Help describe some examples of the kind of problems your team is solving in this year?
Mahesh Varavooru: We are using NLP to solve many problems in Healthcare research, research in Legal, research in Finance and Banking.
We are also using machine learning and deep learning to solve pricing problems in Retail.
We are using deep learning for image analysis and solving many problems in Oil and Gas, Energy, Travel and Insurance.
How do you measure the performance of your team?
Mahesh Varavooru: You don’t need an A player all the time. All you need is the right team member. A good team has people with different experience and skills levels to complement each other.
Advice to Aspiring Data Scientists
According to you, what are the top skills, both technical and soft-skills that are needed for Data Analysts and Data Scientists?
- Be good in R or Python
- Learn the fundamentals on how different ml algorithms like SVM, decision trees and linear regression work
- Focus on understanding the customer problem. No one cares what technologies we use as long as we can effectively solve their problems.
- Spend time to understand the data
- Be honest and helpful to the team members
How much focus should aspiring data practitioners do in working with messy, noisy data? What are the other areas that they must build their expertise in?
- Data wrangling is an important skill. So learn to be good with this process
- Instead of focusing on how big the dataset is, focus on the customer problem
What is your advice for newbies, Data Science students or practitioners who are looking at building a career in Data Analytics industry?
- Programming and software skills – R, Python, SAS or Excel
- Visualization Tools
- Statistical foundation and applied knowledge
- Machine Learning
Learn Python. While R is good, it is not necessarily a good language for productionized systems. It is great for prototyping. Start with traditional machine learning and move on to deep learning. Note that whatever solution you build, it needs to explainable as most companies want to know how a certain value is predicted.
What are the changing trends that you foresee in the field of Data Science and what do you recommend the current crop of data analysts do to keep pace?
Mahesh Varavooru: I suggest build a strong expertise in one industry like Finance, Healthcare, Energy, Manufacturing, etc. Your data science skills will need to complemented with domain skills of a particular industry. Healthcare will be huge and entering this domain will definitely be useful on the long run.
Would you like to share few words about the work we are doing at Digital Vidya in developing Data Analytics Talent for the industry?
I find your blogs and articles very useful for a budding data scientist or ml engineer. Great work by your team overall and keep it up!
To know more about Mahesh Varavooru, you can check out his LinkedIn.
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