Shivam Srivastava is currently working as associate director of the AIA (Artificial Intelligence & Analytics) practice of Cognizant technology solutions.

His area of expertise is life science commercial analytics, including sales force effectiveness, alignment, segmentation, targeting, incentive compensation, sales analytics, data science, machine learning, NLP, etc.

INDIAai interviewed Shivam Srivastava to get his perspective on AI.

What motivated a physics and mathematics graduate to pursue a job in artificial intelligence?

Coming from a small village in the Sitapur District of Uttar Pradesh, I came to Delhi with a dream of making a career and pursuing the Post Graduate Diploma in International Business. I had a deep affection for Physics and Mathematics and entered the field of primary research in marketRx Inc, a niche analytics firm focusing on Life Science Industry Analytics. During the initial years of my career (2006-2009), I championed the art of data processing and statistical analysis. By 2010 Data Science and Artificial Intelligence were becoming buzzwords in the Analytics industry. I also started learning the concepts of Machine Learning and Artificial Intelligence. Now I successfully placed myself as a strong leader of AIML in Cognizant with a deeper understanding of Life Science Industry data and Commercial Analytics. 

What initial difficulties did you have during the transition? How did you triumph over them?

Transitioning from Research Analyst to Associate Director of AIML was challenging from 2006 to 2022. The shift involved moving from small to large and unstructured datasets and learning new skills such as R, Python, Azure, AWS etc. Moreover, looking at problems from a broader perspective rather than following a structured analysis plan (which is typically observed in primary research). In the same phase, I was also moving from a data analyst to a manager with strong relationships with his team members and excellent people management skills. This period also witnessed a tough time (2008-2009) in the IT industry, giving us much learning. 

Tell us about your research in PhD science. What were your research contributions?

My research topic was "Green Management Practices & Organizational Performance- A study on Indian Pharmaceutical Companies". In this research, I explored the various green management practices that We can follow to enhance the overall performance of a Pharma company on two critical parameters: operational and financial performance. The research attempted to explain the concepts of green management, its necessity and the benefits of adopting such practices. The study outcome suggests that adopting green management practices significantly impacts the pharma company's operational/financial performance. 

Which position was the most challenging and engaging, from research analyst to associate director?

The position of manager analytics (Incentive Compensation) was more challenging for me. In 2013 I was tasked to manage a large team of 22 analytics professionals with a good mix of diversity. This role was very different from my previous roles as I did not have expertise in Incentive Compensation. To have good command over the team, I needed to learn the nuances of incentive compensation. On the people management front, there were a lot of challenges to keep the team united as there were many factions within the group. It took me six months of tremendous effort to bring the project under control and keep the team motivated. 

Describe an intriguing research obstacle you encountered during your work and how you overcame it.

During my PhD research, I came across multiple challenges. The top of that was data collection. Since my research audience is employees of pharmaceutical companies, it took a lot of work to get the survey filled out from them. I had reached out to more than 1200 people who worked in Pharma companies via multiple channels such as social networking sites, Linkedin and other means, but the response rate could have been better. I spent close to 6 months doing various follow-ups, but the result could have been more appealing. In the end, I started reaching out to professional research agencies who helped collect data. 

What advice do you offer students and professionals interested in artificial intelligence careers?

My simple and essential advice to students will be to strengthen their statistical and mathematical concepts. Also, get a good hold on technical skills such as R, Python, SQL, AWS, Azure etc. The market demands ready AI professionals who can start delivering the 1st day onwards, so please share your capabilities through personal projects in AI and assignments on Github. Share your experience on Linkedin or other social sites leveraged by the analytics industry.  

Can you recommend AI-related books or research papers to individuals entering the field?

I would suggest the following books for beginners: 

1. Artificial Intelligence For Dummies (John Paul Mueller)
2. Machine Learning For Absolute Beginners (O Theobald)