I consider myself an accidental data scientist: Viacom18 Media’s Anubhav Srivastava
Anubhav Srivastava is the head of data science at Viacom18 Media Pvt. Ltd. The BTech graduate from the Indian Institute of Technology, Kanpur, has started his data science journey with Evaluserve Consulting. He has also been recognised as a top 40 under 40 Data scientists by National Machine Learning Developers Summit 2020. In an exclusive interaction with Analytics India Magazine, he spoke about his tryst with data science. AIM: What drew you to data science? Anubhav:  My undergraduate thesis was about building a predictive model for preventing accidents in spaceships. Around the same time, we lost Kalpana Chawla when Columbia broke down upon re-entry. It was an eye-opener for me and made me realise data science can solve many real-life problems. I started my career with equity research, where I worked on building predictive models for identifying a company’s future prospects. It helped me understand how KPIs and parameters influence a business; why is data so integral to its survival etc. That’s what formed the basis of my interest in this field. AIM: How important is it for data science aspirants to start early? Anubhav: I consider myself an accidental data scientist. I worked across different domains before I came into this field. Today, you find a lot of people transitioning to data science mid-career. People migrating from other industries come with a strong advantage of having already been exposed to other facets of the business. Also, how data science would impact the business outcome is ingrained in such data scientists. Getting the fundamentals right is very important. The advantage that newcomers have is that there are a lot of resources to help them improve their skills and motivate them to build models, make predictions, etc. However, a longer learning curve is better in the long run. The only suggestion I would give to aspirants is not to rush into this domain. Give yourself time, and build strong fundamentals. AIM: Is there a project that stands out in your career? Anubhav: It’d be tough to pick a project when your career spans across so many industries. When you work in new industry, you have to deal with two things. One is domain knowledge; the other is building a solution for problem-solving. Having said that, there are two aspects of a project that give me an adrenaline rush. One is, projects where there are a lot of possibilities for human input. Trying to predict a model where there’s a lot of human input is very challenging. The other is when there’s a shortage of adequate training data. I once worked on a project for a private equity client, and we built a model that predicted which global SME would be the next target for acquisition. The challenges included: too many industries and hence too many players and limited data accumulated in the past 20 years. Solving that kind of problem gave me a lot of motivation because we had only fewer data to work with and a lot of human input. AIM: How does Viacom18 leverage data science? Anubhav: We were a traditional broadcast media company; now, we have a digital arm. It’s a digital content business. Essentially, all data is stored digitally, and all user interaction for anything built into the product happens online. So in that sense, the business design is data-driven. Our business model caters to various aspects of day to day performance and the analysis of the data that we derive. When it comes to data science and AI, we focus on two major aspects. The users and our content. We have a two-pronged strategy. One is monetisation. It’s a cut-throat world, and we try to find ways to monetise our content while considering the user demographic’s needs. We constantly strive to improve the user experience to avoid making it obtrusive. That’s where all AI/ML solutions come in. The primary focus is recommendations. We leverage AI/ML to either monetise content that was not monetised before or increase the probability of monetisation: questions like how can you motivate the users into buying subscriptions, how can we make people watch more content, how can we make our users watch more ads or click on said ads etc are what we primarily work on. AIM: What are the key trends you see in data science? Anubhav: First and foremost is that data is now becoming an integral component of a business model. 5-10 years ago, a company’s business model would be based on the consumer, the buyer and the intermediaries. Nobody would talk about data. Today, when presenting a business model to investors, stakeholders, etc., data and its monetisation are discussed prominently. I think that’s a big change. When I started my career, I had to convince people that data is important. Now everybody understands that. The rise in data privacy is another trend.. To my knowledge, only banking institutions focused on data privacy and cybersecurity. Now, they are gaining importance in the organisational scheme of things. Another trend I see is no-code data science, which is essentially a cloud-native business that allows one to plug and play models into an ecosystem. Such businesses are fast emerging in the country and worldwide. I think that will gain tremendous traction in the future. AIM: What’s your long term vision? Anubhav: I have always had a keen interest in video. Be it Photogrammetry, adversarial networks, or synthetic media. I think those are the domains that genuinely pique my interest. I want India to be the hotbed of innovations in video streaming and anything related to AI/ML in the augmented or virtual reality domain, and I want to play an integral part in it. AIM: What’s your advice for data science enthusiasts? Anubhav: Aspirants should not give up on problems. Keep chasing a problem until you build something marketable or noteworthy. This approach will help aspirants build the mindset to succeed as data scientists. Experimentation is very important. Not thinking just like a data scientist but as a problem solver is very important. And being persistent is very, very important.
My undergraduate thesis was about building a predictive model for preventing accidents in spaceships.
My undergraduate thesis was about building a predictive model for preventing accidents in spaceships.
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["AI", "machine learning", "ML", "data science", "analytics", "Aim", "RAG", "R", "Go", "Git"]
A writer by passion, Kartik strives to get a deep understanding of AI, Data analytics and its implementation on all walks of life. As a Senior Technology Journalist, Kartik looks forward to writing about the latest technological trends that transform the way of life!