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  1. Home
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  3. Nnamdi Iregbulem
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Nnamdi Iregbulem

MBA Candidate
Stanford University
Country or state 
United States (California)
Available to 
North America
City 
Stanford
Fee 
Ask for pricing
Languages 
English
Volunteer
Yes

Personal Details

Bio

I'm a geek at heart.

I've been fascinated by technology for as long as I can remember. I spent countless hours as a teenager building computers (currently rocking a 7700K / 1080 Ti combo), coding up websites (R.I.P. PHP) and finding ways to game Google search (SEO is not what it used to be though). These days I spend just as many hours working with random forests, neural networks and statistical analysis in Python.

I love applying rigorous analytical frameworks and mental models to tough problems at the center of technology, business and life. In the last few years I've been exploring application infrastructure, the acceleration of developer productivity (DevOps), and applications of machine learning techniques to real-world business problems.

Currently, I am a student at the Stanford Graduate School of Business. Before that, I spent four formative years investing in growth-stage technology businesses at ICONIQ Capital, where I sourced investments in leading B2B software and internet companies like GitLab, Epic Games (Fortnite), ezCater and Brightwheel and helped drive investments in others like Age of Learning, Alteryx (AYX), Fastly, Relativity, SurveyMonkey (SVMK), and Uber.

While in college, I interned at J.P. Morgan, where I subsequently spent a year in Technology, Media, and Telecommunications investment banking. I also interned at McKinsey & Company and worked on campus as a Student Tech, helping my fellow students solve their most dire computer problems. I graduated from Yale University with a degree in Economics.

Los Angeles => New York => San Francisco

Current position (1)

MBA Candidate

Stanford University

Degrees (2)
MBA
Stanford University
2018 to 2010
Economics
Yale University
2009 to 2013
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Expertise (7)

Technology
Business
Venture Capital SaaS DevOps Cloud Machine Learning
Recommendations
1 Audience
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