In continuation to our initiative of interviewing domain experts to help job aspirants carve the right career path for themselves, we had a chance to connect with Mr. Anand Das, Head – Data Science & Engineering, TVS Motors.
Mr. Das has 19+ years of valuable experience working with leading names of the industry, including Genpact, GE, Target Corporation, and WNS Global Services.
Let’s hear from the expert himself!
Interview Log – Mr. Anand Das
Naukri Learning – Please share a brief introduction about your role as a data scientist.
Mr. Das –
I lead Data science, data engineering, and Business intelligence teams for customer-facing functions of TVS motors. My teams build data intelligent products to help our customer-facing teams provide a best-in-class experience to our customers in their pre-purchase and post-purchase life cycle.
I joined TVS motors in 2019. Before TVS motors, I worked with a US-based retail and e-commerce company and with analytics consulting companies.
Data scientists in my team spend a majority of their time understanding end-customer needs and pain points through direct interactions with domain experts and by analyzing large volumes of data generated throughout the value chain – Transactions with suppliers, manufacturing, vehicle telematics, customer engagement, customer services, sales, digital marketing, mobile apps, website, etc.
Naukri Learning – What type of career opportunities are available for a data scientist? Could you please share some insights?
Mr. Das –
All entities that are generating or collecting large volumes of data are great places to work for a data scientist. While Global Digital native companies like Google, Flipkart, Amazon hire a lot of data scientists in their India offices, Indian companies like Big Bazaar, Reliance Jio, Tata Digital, TVS motors, also investing heavily in data analytics capabilities. Also, Tech startups are a great place to start your career as well.
Some major industries that invest heavily in data scientists’ roles are banking, insurance, media, healthcare, retail, education, manufacturing, and energy utilities.
In terms of career path, data scientists typically end up specializing in 3-4 years with experience – data analyst with domain specialization, data engineers, BI developers, and machine learning scientists. With the advent of applications of deep learning in the last 2-3 years, many data scientists end up specializing in deep learning for solving problems in areas of computer vision, Natural language processing, reinforcement learning, recommender systems, etc.
There is an excellent article in Analytics India mag that presents comprehensive views on this topic.
Naukri Learning – Which are the top companies in this field?
Mr. Das –
Globally, the top companies are Amazon, Facebook, NASA, Google, Netflix, Tesla, Microsoft, IBM, General Electric. At the same time, a lot of cutting edge exciting work is happening in academic institutes and tech startups in the USA, Israel, Germany, India, China, UK, etc.
Naukri Learning – What would you say is your motivation behind pursuing what was back then a fair niche field?
Actually, my interest in data-based decision making started with my first company Tata Steel, fresh out of college. As a fresh engineering graduate, I was exposed to quality improvement philosophies like Six Sigma, Lead, TPM, etc. All these philosophies emphasized on understanding and solving problems by analyzing data. Back then, my tools for data analysis used to Minitab and SPSS. But all these tools had limitations on the volume and variety of data they could handle.
Over time, as I started working at GE and later on at Target, I was exposed to big data and open-source Machine learning tech that allowed us to handle large volumes of data to make automated algorithmic decisions.
Naukri Learning –Tell us about some lessons that you have learned to excel in this field.
Mr. Das –
- Be very curious and speak directly to end-users. Spend as much time needed to understand the pain point or the problem statement. Unless you know the problem, you won’t be able to formulate an excellent technical solution.
- Build a hypothesis and test them. Invest in tech that allows you to test your hypothesis quickly.
- Keep a tab on the latest tech available to solve the problems. Continue to invest in learning a new topic every six months.
- Leverage ML/AI codes and frameworks that are already available in the open-source communities. Don’t reinvent the wheel unless needed.
- Analytics is as much about solving the problem as it is about communicating how you solved it. Your stakeholders won’t embrace your solutions unless they understand how it was done. Explain the solution as you would do it to a 6-year-old.
Naukri Learning – What does a day in the life of a data scientist look like?
Mr. Das –
A data scientist –
- Spends time to understand the problem statement by talking to users by analyzing the data available to him
- Reads a lot of technical or functional documents to formulate how to solve the problem
- Builds hypothesis and tests them, typically uses SQL, Pandas, Numpy in Python
- Writes codes on Python, R to study data, clean up data, transforms the data to improve ML model performance
- Submits his codes to a code version control platform like GitHub for peer reviews
- Works with ML engineers to deploy his models in production
- monitors performance of previously deployed ML models
Naukri Learning – What skills or characteristics make someone an effective remote worker?
Mr. Das –
- Knows how to communicate clearly while working in a virtual or remote set-up
- Reaches out to his online communities when they need help
- Comfortable working on online collaboration tools like Slack, MS teams, Atlassian, etc.
Naukri Learning – Any courses/paths that you’ll recommend to aspiring data scientists?
Mr. Das –
I wrote an article on – My 2019 reading list for data science aspirants
Naukri Learning – Parting words – Do you have any expert advice for our readers?
Mr. Das –
- Read a lot. There is excellent learning material available on the internet
- Code, do it yourself
- Speak to end customers to hear the problem statement first hand
- Build a hypothesis and test them
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