Shaurya Uppal – Medium

Published in DataDrivenInvestor

·May 6

Beyond AB Testing | Experimentation without A/B Testing — Switchbacks and Synthetic Control Group

Prerequisite: Know all about A/B Testing What to do When you can’t AB Test? Abstract A/B Testing is one of the most important skills of a data professional. All major tech giants use this method for experimentation at scale. A/B testing has proven itself a lot of times (some popular case…

Experiment

6 min read

Published in DataDrivenInvestor

·Apr 26

A/B testing at Netflix, Uber, Pinterest, Google, LinkedIn, and Spotify- in easy words | Pitfalls of A/B Testing

What is A/B Testing? A/B testing is a data-driven approach for making business decisions instead of guesswork. It is also known as split testing, where users on the platform are split by a randomised experimentation process into two or more variants; the control group (original or older version) and the treatment group (new version). The crux of A/B Testing …

Data

7 min read

A/B testing at Netflix, Uber, Pinterest, Google, LinkedIn, and Spotify- in easy words | Pitfalls…

Published in DataDrivenInvestor

·Apr 12

Recommendation System with Graphs at eCommerce and Foodtech: Flipkart, UberEats, Swiggy, Instacart, Delivery Hero, Amazon, etc.

Disclaimer: I don’t endorse any brand. Flipkart, Walmart, Instacart, Delivery Hero, Amazon, etc. names are used to make the project more relatable to the audience and know in which business this approach can be used. This project can be applied at any eCommerce, Grocery, and Foodtech company. …

Graph

6 min read

Published in Nerd For Tech

·Apr 1

Uber Driver Ride Acceptance Probabilistic Model Building

We all have faced problems with taxi booking requests — I have faced issues like drivers not accepting my ride and I have to switch to some other app to get my request fulfilled. To find the best cab drivers for you — within a few seconds; Uber runs a…

Data

3 min read

Published in DataDrivenInvestor

·Mar 26

Using Data Science in Riding Hailing platforms like Uber, Lyft, Rapido, Ola, etc. to reinvent transportation

This blog focuses more on the Product side — where we can leverage data science better. I would be relating with Uber and different ride-hailing problems that can be solved with data science. I created a Demand Forecasting Project a few months back one can check out about it: HERE …

Data Science

3 min read

Published in MLearning.ai

·Mar 1

Mr. Wolf Fools the Data Science Team Again — Data Leakage Scam 🐺

Mr. Wolf earlier p-hacked an AB-Testing Experiment and now comes back with another mischief — Data Leakage. Over the years, I have seen and witnessed some evil practices from Mr. Wolf (expert data scientist — dummy character). Earlier, he was p-hacking and now he came up with another trick to…

Recommendations

4 min read

Mr. Wolf Fools the Data Science Team Again — Data Leakage Scam 🐺

Published in DataDrivenInvestor

·Feb 25

Strategizing a Recommendation System on a Jobs Platform | Personalising Jobs Feed for LinkedIn and XING— Part 2

I am back with Part Two of my previous blog, continuing on how to build a recommendation system for Jobs. Building a Job Recommendation Strategy for LinkedIn and XING | Job Feed Ranking — Part1 Personalisation at LinkedIn for Job Posting | Ranking and Relevance Data Science Problem Solvingmedium.datadriveninvestor.com Short Recap of Part One In my previous blog, I discussed the business objective of building a jobs recommendation system, metrics to measure a recommendation system, and further discussed two approaches to personalize job feeds…

Recommendations

4 min read

Published in DataDrivenInvestor

·Feb 16

Building a Job Recommendation Strategy for LinkedIn and XING | Job Feed Ranking — Part1

ABSTRACT LinkedIn is a Networking and Job Search Platform with a 700M+ user base and Millions of Jobs being posted daily, a need of Recommendation System is much needed so that users and companies find the best talent as fast as possible. …

Data

5 min read

Building a Job Recommendation Strategy for LinkedIn and XING | Job Feed Ranking — Part1

Jan 1

Thinking Data Strategies in Fintech Universe | Building Payments Recommendation System for Google Pay, RazorPay, NuBank, Gojek, Chime, Dave, Revolut and Tinkoff Bank — Part Two

We will open the book. Its pages are blank. We are going to put words on them ourselves. The book is called Opportunity and its first chapter is New Year’s Day. I am back with Part Two of my previous Blog, continuing on how to build a recommendation system for…

Fintech

5 min read

Dec 22, 2021

Thinking Data Strategies to Build Payee Recommendation for Fintech Universe — Gojek, Google Pay, Paytm, Cred, NuBank, N26, PhonePe, RazorpayX, Zeta, Revolut, and Monzo — Part 1

Disclaimer: I don’t endorse any brand Gojek, Gpay, Paytm, Cred, NuBank, N26, PhonePe, RazorpayX, Zeta, N26, Monzo, etc. names in the blog are used to make the study more relatable to the audience and know in which business this approach can be used. This approach can be used at any…

Data

7 min read