“I had the pleasure of hiring Derek as the first Growth Product Manager at Nomad Health. From day one, his analytical aptitude stood out. His ability to clearly and deeply discuss success metrics and data analysis was impressive. Clearly, he had a strong growth mindset and perfectly aligned with our company's culture. During his year and a half at Nomad Health, Derek consistently demonstrated exceptional analytical thinking. He successfully implemented A/B testing and analyzed big data sets to optimize our strategies. His efforts significantly contributed to launching numerous tests that enhanced our funnel conversion. He also worked closely with our marketing team and served as the primary point of contact for marketing toward the end of his tenure. Derek is a skilled Growth Product Manager and Growth Hacker, and a great team player.”
About
Contributions
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How do you design and run effective A/B tests and multivariate tests?
"Failed" experiment results can sometimes be just as useful as "successful" experiment results, because learning what doesn't work IS valuable. By focusing on continuing to learn from your experiments and iterating based on those learnings, the results will come.
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How do you design and run effective A/B tests and multivariate tests?
Teams I've worked with usually prefer looking at statistically significant results using the 95% confidence level when running A/B tests; however, using the 90% confidence level can suffice if your sample sizes aren't large enough. Remember - at the end of the day, A/B test experiments are not intended to be scientific or academic exercises that require extreme accuracy. They are intended to help your team and company make a good decision with the data you have available. We can never be absolutely certain that a decision we are making is the most optimal, but making a calculated decision with less than 100% certainty is better than excessive time wasted due to analysis paralysis.
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How do you design and run effective A/B tests and multivariate tests?
Hypothesis-driven experimentation is a great way to avoid the "throwing spaghetti at the wall at see what sticks" testing strategy (which is a bad approach!). A good experiment design should have a clear hypothesis that will be tested. This ensures that whether your experiment results are considered a success or failure, you should always be learning something based on those results. These learnings should then be carried forward to help define and prioritize future experimentation.
Experience & Education
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Pocket Productions
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Courses
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Econometrics
Economics 103
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Financial Markets and Institutions
Economics 106M
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Fundamentals of Business Administration and Management
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International Finance Theory
Economics 122
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Introduction to C++ Programming
PIC 10A
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Introduction to Web Programming
PIC 40A
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Money and Banking
Economics 160
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Real Estate Finance
Management 180
Honors & Awards
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Valedictorian | 4.11 GPA
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Languages
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English
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Basic Spanish
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Organizations
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Alpha Kappa Psi - UCLA Alpha Upsilon
Director of Fundraising, Pledge Class President
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UCLA Club Taekwondo
Competitive Athlete
- Present• Undefeated in Pac West Taekwondo Conference during 2011-2012 season • Gold Medal recipient in 2013 Taekwondo National Championships Open Division in Chicago
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