Inspirational Conversation with Leaders

Data Science Lead - LinkedIn emphasizes the Skills of Analytics

Data Science Lead - LinkedIn emphasizes the Skills of Analytics

LinkedIn is a business and employment-oriented service mainly used for professional networking, including employers posting jobs and, job seekers posting their CVs. LinkedIn was founded in the year 2002 and, it is headquartered in Sunnyvale, California. In India, it is headquartered in Bengaluru. As of March 2019, LinkedIn had 610 million registered members in 200 countries!

Pawan Kumar is experienced analytics professional with a demonstrated history of working in the internet industry. He has more than 7 yrs of experience in the field of analytics. He started his career as Business Analyst in Mu Sigma and, currently, he works as a Data Science Lead in LinkedIn where he leads the Analytics for all pillars of India Consumer/Product Analytics.

1. How did you decide to be in the Field of Analytics?

I have always been curious about solving problems. No matter how complicated the problem is I have always enjoyed solving them. This sparked a desire in me to find the desired job function where I can make my work a play. That’s how I landed up in the world of analytics and, I have seen myself excel in this field.

2. What are the major challenges faced while hiring an Entry-Level Talent in Analytics?

The major challenges are in terms of Problem-solving skills. Most of the business problems are ambiguous in nature. The ability to identify the right issue, seeking clarity and breaking down the problems into smaller chunks are some of the key aspects the candidates lack. I have mostly found freshers directly jumping into designing solutions even before understanding the problem at hand. And in most cases, the candidate ends up solving the wrong problem.

3. How do you think these Challenges can be overcome?

Freshers should be more curious and not shy away from asking lots of questions to clarify the problem. Einstein said, ‘if I had an hour to solve a problem, I’d spend 55 minutes thinking about the problem and 5 minutes thinking about the solution.’ This should be the key mantra for a lot of candidates. Also, below is a framework that helped me a lot in my Journey as a Data Scientist, see if the candidates can leverage some of these:

  • Identify the issue:
    - Be clear about what the problem is
    - Seek clarity on what triggered this problem? Why is it important to solve the problem now? What all do we already know about the problem? (remember that different people might have different views of what the issues are)
  • Understand everyone's interests and establish the success criteria:
    - This is a critical step that is usually missing. Interests are the needs that you want to be satisfied with any given solution. We often ignore our true interests as we become attached to one particular solution. The best solution is the one that satisfies everyone's interests (This is the time for active listening. Put down your differences for a while and listen to each other with the intention to understand)
    - Document the success criteria and gets alignment
  • Hypotheses Building:
    - This is the time to do some brainstorming. There may be lots of room for creativity. List down all the factors and hypotheses pertaining to the problem/issue.
  • Evaluate the Hypotheses (options):
    - Data Collection/ Cleaning/ Analysis to validate or invalidate the hypotheses (use exploratory data analysis to statistical techniques to resolve the problem)
  • Interpret the results:
    - Derive findings, insights and recommendations from the analysis
    - Align the results to create a narrative that meets the success criteria defined above

4. What are the Skills that are necessary for the role of Data Analyst/Data Scientist?  

A Data Scientist/Analyst, in my opinion, is nothing but a problem solver who leverages data to help make informed decisions. He/She needs to have a blend of business, mathematics and technology. Technologies/programming languages: R, SQL, Python, SAS, Big Data PL. Statistical techniques and algorithms: Regressions, Timeseries, Clustering, etc.

5. Do you offer Internships and live Project Opportunities?

Yes, we do for 3-6 months. Based on the business requirement and candidate performance a PPO may be offered. We hire mostly 3rd and 4th year.

6. Do you think certifications help someone stand out from the crowd?

I think certifications are a good value add. I believe if an individual takes the certification courses seriously and, not just for the sake of getting a certificate that will have some value.

7. Any interesting projects happening on LinkedIn?

At LinkedIn, data is always at the center of decision making which naturally paves the way for a lot of interesting projects.