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We Want To Make Hiring Cycle Shorter & More Efficient With Data Analytics: Shine.com’s Madhukar Kumar


We Want To Make Hiring Cycle Shorter & More Efficient With Data Analytics: Shine.com’s Madhukar Kumar


This week we talked to Madhukar Kumar, Chief Analytics Officer at Shine.com who has over 13 years of experience in the data science industry and he has been consulting across a multitude of both global clients and verticals.



Apart from his corporate life, Kumar is also interested in Academics and teach data science at various universities and spends a considerable amount of time nurturing future data scientists across the globe.

In this interaction, Kumar shares his take on how the analytics domain is playing a major role in Shine.com’s operations as well as in the Indian analytics ecosystem.

AIM: How will your appointment in the analytics division play a crucial role in re-imagining the analytics strategy for Shine?

Since my appointment, We have already gone through major organisational and structural changes to ensure that we are keeping Data science at the core of everything we do. We are bringing a data-driven culture in the organisation and the idea is to make Shine an AI company.

We are focused on leveraging technologies such as artificial intelligence, machine learning, and deep learning to catapult our product into the next phase of evolution. We are redefining our analytics strategy to streamline search results, making them more relevant and in-depth for jobseekers. We will also build on our existing products to better serve recruiters in their task to hire great talent.

I believe that we will be able to steer the analytics division towards greater success. Our overarching goal is to enhance user-experience which will only be possible with the optimum deployment of technology. Thus, the adoption and development of new-age technologies continue to be my major focus areas at Shine.com.

AIM: What is your vision for Shine.com? Do you plan to expand the product portfolio?

Customer centricity has always been at the heart of all our endeavours at Shine.com. Living up to this long-standing commitment towards enhancing user-experience, we plan to build on existing products, making them more seamless and easier to use for our customers.

Product expansion is also definitely on the cards for the brand. In fact, we recently introduced face recognition and touch ID capabilities on Shine.com’s mobile application to facilitate seamless login. We will continue to leverage forward-looking technologies to unlock new levels of growth for the brand.

The ultimate aim is to add immense value to the entire hiring process with our robust, tech-backed offerings. Through our product, we are working to shorten the overall hiring cycle which usually takes around 70 days. By reducing the time taken for each hire, we will be enhancing the experience of both recruiters and candidates in a sure-footed way.

AIM: What are AI and analytics expertise that you are going to bring from your previous roles?

I have been working in the analytics industry for the past 13 years and have built the data science team from scratch multiple times. All these years, I have used AI and data science to solve various complex problems that included structured as well as unstructured data. Also, I have taught 1,000+ professionals across the globe who were keen to make the transition to the data science field.

My experience of setting up the data science and solving complex problems using AI would again endeavour at Shine. My teaching experience will help the internal team of data scientists.

AIM: How are you planning to strengthen the data science team? What would be the biggest challenges in ensuring it?

Since Shine.com relies on the extensive recruiter and candidate data to facilitate efficient job-candidate matching, the data science team forms the backbone of our entire process. Therefore, we are focusing on growing our data science team.

While hiring the right talent for the team, we will be looking at technological-adeptness of candidates. The major challenge, therefore, will be to zero in on candidates with the relevant skill sets. Some of the major skills we are looking for include AI, ML and data engineering. 

AIM: How are technologies like deep learning and machine learning being used at Shine?

It’s important to provide an excellent experience to recruiters and candidates and we are working tirelessly for it. Deep learning enhances search result by doing a better matching of jobs and candidates. Matching jobs and candidates is a 4-way matching (job – job, job – candidate, candidate – candidate, candidate – job). We are currently using it and it’s a continuous improvement process.

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AIM: How has the role of analytics and AI evolved in the hiring and recruitment space? What are the various benefits that emerging tech has brought?

New-age technologies such as analytics and machine learning have become integral to the modern hiring and recruitment process. These tech-led tools deliver enhanced efficiency, accuracy, and seamlessness. This allows recruiters to save significant time by automating routine tasks, such as candidate outreach and shortlisting, and focus on more value-driven objectives. Tools such as analytics and AI can also enhance the user experience for candidates as they are shown more relevant jobs.

AIM: What are the ways that data analytics is being used at Shine? Would you like to explain with some use cases?

At Shine.com, we use AI-based algorithms to efficiently match candidates/skills with the right job profile. Through this, we make the process of job discovery and application extremely easy and convenient for both recruiters and jobseekers. This further enhances their experience on the Shine.com platform.

AIM: What are the overall challenges you foresee in Indian analytics industry?

The first challenge is too much fascination on buzzwords like AI, data science, deep learning, machine learning etc. Instead of focusing on problem-solving, we are focusing on means/methods of problem-solving.

AI and data science are just means to solving business problems. All these complex algorithms were invented decades ago, but it was not required earlier in the industry as problems were not that complex and data size was also pretty less as compared to what we have now.

As an industry, we have shifted towards using complex algorithms only because of the inability of standard algorithms to solve the problem. Nowadays, the first question is asked whether you have applied deep learning or machine learning to solve the problem or not and not how well the problem is solved. That’s the biggest irony.

The second challenge is the acute shortage of talented skill set in futuristic technologies like AI and ML. How to manage this gap is going to be the key to success for the overall Indian analytics industry.



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