My data science journey


I was both nervous and excited when I received the email that I was selected for an internship at McKinsey. I still remember crying and smiling at the same time in my school’s library when I received the email. I had been working on my master’s in Texas after a few years of working in India and now I had gotten the chance to continue studying remotely while working for McKinsey in New York.

I was going to join a data analytics team and work with cutting edge tools and technologies, some of which were familiar to me, but others that were totally new. I was excited to learn so many new things and work with a data squad in an agile environment with analytics tools like PowerBI, Tableau, and Alteryx.

I was so excited to work cross-functionally with various squads on a complete agile journey. I learned how sprints work and how the work packets are tracked through Jira, which is needed in today’s fast-moving world.

End-to-end data projects

A major part of my internship was spent working on a complete RStudio Connect project. This included identifying useful data, building R Scripts in order to fetch that data through APIs, scheduling those scripts to run every five minutes in Linux to get the refreshed data on our VM, and further ingesting this data into Splunk so that we could have all the data under one umbrella. After that, we had critical data metrics readily available in Splunk through the NMON App, which helped us make our dashboard very insightful. On top of this, we built reports in Splunk to make our visuals and showcase them in dashboard form.

The other project I worked on was supporting McKinsey’s adoption of Microsoft’s newly launched analytics product PowerBI. Our first goal was to learn how users at McKinsey were adopting this new product, and how many of them were content creators and content viewers.

From there, we built a dashboard in Splunk which gave us insights on product adoption, total number of active users, and other features insights like top workspaces, datasets and shared reports.

I had the chance to work on a project in Snowflake PoC, which not only helped me to learn Snowflake, but other cloud databases like PostgreSQL and MongoDB. I also learned Snap Logic, which is a favorite ETL tool.


Mentorship and support

During my internship, I jumped into data science, agility and visualization with the support of my mentors, who made sure I was staffed on projects I was truly interested in. Initially, I was overwhelmed with the project options, but my manager, senior software architect Jaspreet Singh, assured me it’s okay to be confused sometimes. He advised me to follow my instincts and made me feel understood.

My team included me in meetings so I could understand the context of my work and made sure I had access to training courses for the tools I used. For example, I learned Tableau through my master’s program, but it was helpful to dive even deeper through McKinsey’s learnings.

This journey has been amazing, with extraordinary moments I will definitely cherish, from the learning opportunities to the incredible colleagues I work with on daily basis.

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About Pooja

Pooja is a data science intern based in New York. She has a bachelor’s in engineering from the DY Patil College of Engineering in Pune, India and is currently pursuing her master’s in information technology management at the University of Texas, Dallas. Prior to joining McKinsey, Pooja was a software engineer with HSBC bank and a data warehouse analyst with Infosys. In her free time, Pooja enjoys painting and gardening.

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