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Duncan Gilchrist

VP Data @ Gopuff. Hiring! - Ex-Uber

Duncan Gilchrist is a highly accomplished professional with a background in Data Science, Economics, and Product Analytics.

He holds a Ph.D. in Business Economics from Harvard University, where he also completed his Bachelor of Arts in Applied Mathematics, graduating Cum Laude.

Throughout his career, Duncan has held key positions at renowned organizations like Uber, Gopuff, and Wealthfront Inc.

At Uber, he excelled as the Director, Head Of Delivery Marketplace And Courier Data Science, Head Of Pricing And Incentives Data Science And Product Analytics, and Head Of Rider Pricing And Fares Data Science.

Prior to his roles at Uber, Duncan served as a Principal Data Scientist at Wealthfront Inc. and as a Summer Associate at Morgan Stanley.

His academic achievements include a Ph.D. in Business Economics from Harvard University and valuable industry experience through roles like Operations Intern at Sia N'Son.

Duncan Gilchrist brings a wealth of knowledge and expertise in Data Science, Analytics Engineering, and Marketplace Experimentation to every role he undertakes.

Highlights

Aug 8 · linkedin

📢 Calling all Data Science Leaders!

As we're building Delphina we've been surprised to learn how many data science / ML / AI leaders are on their own when it comes to navigating hard pressing issues. They don't need what's on LinkedIn and Twitter — they need what actually works. But the reality is there just aren't many spaces for them to have open, honest dialogs.

So I’m thrilled today to announce Delphina Data Dialogs — a new bi-weekly application-only online private forum where top minds in data science, ML, and AI can learn, connect, and tackle pressing challenges together.

First session: 

🗓️ Thursday, August 22, 4:00 PM PT on Zoom 🎙️ Featured speaker: Brad Klingenberg, founder of Naro and former Chief Algorithms Officer at Stitch Fix  🎙️Moderated by Hugo Bowne-Anderson, host and creator of the Vanishing Gradients podcast  📌 Topic: "Navigating the AI Revolution: where's the data science?" — exploring the interplay between traditional ML and GenAI (the good, the bad, and the ugly) and how it's reshaping our field.

The Dialogs are space for authentic conversations. No recordings, no sales pitches — Chatham House Rules and real talk about timely topics.

We’ve got a deep roster of incredible speakers to come, spanning all facets of data science strategy, tactics, and infrastructure including Lilei Xu, Min Cai, Kane Sweeney, Ali Rauh, Victor Kostyuk and more

Check out the full details and apply to join us: https://lnkd.in/gsCXpz55

#DataScience #AI #MachineLearning #LeadershipForum

Mar 19 · linkedin

One of the most jarring parts of moving from academia to the private sector is going from a world where your CV and papers form an encyclopedic multi-year portable track record, to one where a 45 minute interview can decide everything.

If you’re a grad student or academic looking for a job in data science, at first this is very counterintuitive. But if you understand it, internalize it, and act on it, you’ll have an unfair advantage.

I’ve interviewed or advised hundreds of new PhDs trying to break into data science. One year at Uber I even hired five new Harvard economics PhD’s (~20% of the class).

It’s ridiculously common for super talented people to approach the job market like amateurs. No research. No practice. With a straight face, saying things like:

“Why do you want to hire someone with my skills?” “I didn’t expect a coding interview” “I’d prefer to do research; tech is my backup”

Don't do this. Ask yourself what a world-class performance would look like, and do that:

1/ Have a pitch. Companies hire you to drive results — so you should have 3-5 creative ideas for how you would drive impact. Why are your skills a great fit? What products should the company build? These jobs aren’t about intellectually deep research, they’re about building a stronger business.

2/ Do your homework. Know the people, companies, trends. Write a one-pager on any company you’re serious about. Be able to zoom out and talk about the big picture, and zoom in and talk about the nitty gritty.

3/ Speak the language. Tech companies are, well, tech companies. They build technology. So have talking points about why you want to build tech. Why you are excited to work with engineers; why you want to learn more engineering.

4/ Practice. Almost nothing that will come up is totally unpredictable, so don’t be caught by surprise: Practice. Practice your bio in front of a mirror. Practice your coding on leetcode. Practice case studies with your friends.

5/ Go the extra mile. Fly to San Francisco (or wherever) to get coffee in person with people who work at the companies you’re excited about. Send thank you emails. If you ask for an intro, send a blurb that someone can easily forward along instead of asking them to do the work. The small things count.

What am I missing?

#datascience #machinelearning #jobs

Duncan Gilchrist
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Location

Palo Alto, California, United States