I am a seasoned Data Scientist/Analyst with a rich experience spanning two years in the field. Over the past year, I have proudly contributed my skills and expertise to the dynamic teams of both Data Science (Personalisation - ML) and Data Engineering at McAfee. My journey has been marked by a relentless pursuit of excellence and a passion for transforming data into meaningful, concrete insights.

My proficiency in SQL, Python, Spark, my ability to figure out a way around blockers on the go, and my decisiveness with sound reasoning has been the cornerstone of my success, enabling me to extract, analyze, and interpret data with precision. My adeptness in these tools has empowered me to spearhead feature engineering projects, playing a pivotal role in enhancing the value of data and optimizing its use for strategic decision-making.

One of my significant accomplishments includes being a key contributor to the establishment of a feature store at McAfee. 
As we all know, from start to end, 70% of time in most Machine Learning projects is spent on finding out correct POCs within the company, communicating with them to acquiring the correct data and also understanding it, predicting probable metrics to be obtained from it, and then performing the pre-processing. 
Developing a feature store involved a lot of Feature Engineering, gaining close familiarity with the raw data, understanding the business and performing a lot of SQL intricacies, not to forget a lot of internal communication. The objective was to cut down on the preprocessing time and provide Data Scientists 
with data they could rapidly build prototypes upon without having to worry about the entire logistics of how it came to be. The nature of this 'pre-processed' data was that it could support a lot of Data Science efforts, since this data captured the essential behavior of our customers. 

This innovative solution has not only streamlined workflows but has also facilitated the acceleration of data-driven innovation within the organization.

Motivated by my unwavering commitment to continuous growth and a thirst for knowledge, I am eager to expand my horizons into the realms of Data Engineering and Data Science. Drawing upon my foundation in analytics and my proficiency in key technologies, I am excited to take on new challenges and leverage my skill set to drive transformative insights and solutions.

In essence, my journey reflects a fusion of experience, technical prowess, and an unrelenting desire to create value from data.

Sidharth Kriplani

Hey there!
I'm Samarth Singhal, and I turn messy data into clear decisions at CARS24.
Over the past three years, I've transitioned from automating week-long processes into hours at Google to uncovering major revenue leaks and building dashboards that leadership actually uses. 
My sweet spot? Identifying expensive problems that are often hidden in plain sight and addressing them before they escalate.
What I do:

Automate the boring stuff (60-90% time savings, consistently)
Hunt for insights that drive real business impact
Build reports people want to open, not avoid
Share everything I learn with 28K+ data enthusiasts on LinkedIn

My toolkit: SQL, Power BI, Python, Advanced DAX, and an unhealthy obsession with data accuracy.

I've worked across Google Cloud's startup ecosystem, PhysicsWallah's ed-tech scale, and now CARS24's automotive marketplace. Each taught me the same lesson: every company has data, but few know how to use it effectively.
If you're passionate about analytics, stuck on a tricky problem, or just want to talk data—let's connect. I believe the best way to grow is to learn out loud and help others along the way.
Currently solving: Performance puzzles at CARS24
Always looking for: The next "why didn't we catch this sooner?" moment

taplio

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