Capital One reviews

1. Work pressure. No work life balance. Managers tell you that you should not work after 5 PM. But on the other hand, you have unrealistic deadlines. Agile is out of picture. Work is taken in chunks and divided into sprints. PO says “we have to deliver this in this many sprints”. In office hours, we have tons of meetings and no room for actual work. This leaves no choice and you end up working from home after office hours. 2. Office politics - corruption, bias, favoritism at its peak. 3. Incompetent managers and peers who are waiting for you to finish your work and then steal it and showcase as their own piece of art. After work is done, manager will say you didn’t do anything great. You are hired to do this. Everyone else is doing the same. What are you doing extra?? 4. No freedom to be yourself - just pretend you like Capital one in front of your manager. 5. Team work vs individual performance management - all year, mobbing is enforced and people who want to take up something on their own are blamed in the retrospectives. At the end of year, no one knows who did the actual work. Only manager’s favorite guy gets the promotion. The least favorite might even end up losing their job. 6. Idiotic meetings - two hours of useless meetings to come up with some points on blah blah which is not helpful in any actual work.

3 out of the 60 DS actually do work that resembles industry standard DS work. These are the folks that are sent to public and recruiting events to make it sound like we all do real DS work. Don't be fooled! Most of the business leaders do not know what DS resources are or how to use them. They think Data Scientists' core job is to write SQL scripts for business analysts and process managers for "controls". Instead of investing in proper tech resources and fixing processes, leaders choose to use Data Science resources to write SQL script based "controls" designed by business stake holders who know nothing about data or SQL. You don't think you will be writing SQL scripts for data pulling when you sign up for a job description that throws out AI Machine Learning and PhD requirements. It's outright outrageous false advertisement. There are frequent breakdowns in processes that impact our customers. The in-house euphemism for this is a "risk event". Data Science resources are used regularly to fix these risk events by adhoc query writing to pull data for process manager who were not able run their processes properly. No wonder more than a dozen data scientists have left the company in the last few months. Turn over is insanely high. The gave every DS a big fat raise recently hoping to stop the bleeding. Did not help... The kind of problems the business is trying to solve do not require data science, machine learning or even basic statistics. Just some reliable data and SQL developers / Data Analysts would be more than sufficient.