There are some people you talk with about what they do, and the information and related fields adjacent to their work, and you don’t hear “work” or “a job” — you hear passion and… well… a cautiously considered Love. Jazmia Henry – Machine Learning maven & user of data for good – is such a person. Enjoy this Black Perspective.
Introducing: Jazmia Henry
SSD: Jumping right in, tell me a bit about yourself and what you do.
JH: I like to think of myself as someone that uses computational reasoning to make sense of the world around me. Right now, that means I build algorithms to create personalized experiences for people based on observed behaviors that engineers are able to capture through data collection. I also do research on how algorithms have been built in the past in ways that have made faulty and biased assumptions about historically marginalized groups and create new methods using statistical analysis to mitigate these biases. I identify as a dread-headed Black Afro-Latino from East Atlanta.
(SSD: I did some research on Jazmia and came across a presentation that she participated in pertaining to the application of data to an ethical matter. I figured it would be a solid example of some of the types of things she does with data. You can catch her starting around the 1:03 time mark of the video, but it’s cool to catch the entire thing.)
SSD: What inspired you to get into what you’re doing?
JH: I was always really into problem-solving, public speaking, and mathematics so, as you can imagine, everyone thought I would be in Politics or a lawyer. It wasn’t until I got turned down from being an Associate Equity Quant at my last job, after not doing well in an interview (that I crushed), that I decided to look at my skill set and think, “What is it that I truly want to do?”
In graduate school, I would fill my schedule with Quantitative methods courses using advanced analytics and econometrics to solve complex problems in Survey Design, Field Experimentation, and Judicial decision making – and I thought the only thing I could do with that was what I was doing: working in Finance building Portfolios. I just so happened to chat with the COO of Data Governance at Morgan Stanley at the time, who suggested I had the skills to be in Machine Learning.
Taking her advice, I read a book by Andriy Burkov that made me realize that all the things I learned in graduate school could also be used in Machine Learning. So, I gave up my Monte Carlo Simulations for Logistic Regression analysis and moved to the Data Strategist team. It was over from there.
SSD: What’s a tip that you’d give someone who sees you, is inspired, and wants to follow in your footsteps?
JH: When she was President of HBCU (Historically Black College and University), Bennett College, Dr. Julianne Malveaux told me, “When you are given the opportunity to be on that stage- and you will, because I can see it in you- you make sure you make it count, because you may only get one chance.”
At first, I thought this meant that I had to always be perfect – and from 18 (when she told me this) until about last year, that’s what I tried to be. I read everything, took every opportunity to fill my resume with impressive accolades, and made myself small so that I could fit into spaces I did not believe I had the right to be in.
It wasn’t until last year that I realized the most important part of Dr. Malveaux’s statement. She said, “I can see it in you,” which meant I already had all I needed inside of me. I did not have to chase perfection to prove my worth. It was already there.
So, if you want to move forward and accomplish amazing feats, remember to showcase yourself. You get one chance to show people who you are — take every opportunity to do so. Don’t try to be perfect or “me” or “some person you read in a book or watched on a video”. Be you, because within yourself is the answer to every question you will ever have.
SSD: Beautiful! And just because you mentioned books – I’m tossing a softball.
What is a book that you think everyone should read and what are you reading right now?
JH: Everyone should read Outliers by Malcolm Gladwell. Right now, I am re-reading Thinking in Bets by Annie Duke. It’s quickly becoming my favorite book.
SSD: Solid recs — Last question for this section…
What do you think is the biggest misconception people have about your field of work?
JH: I find that people have the same look on their faces when I say I work in Machine Learning that people have had in the past when someone says they’re an astronaut or a rocket scientist. There is this look of awe and fear, and this underlying assumption that anyone that does this must be a genius – and that couldn’t be any further than the truth.
Are Data Scientists and Machine Learning practitioners really stinkin’ smart? Absolutely. Well… definitely the ones I work with — but in the grand scheme of things, we are human. And humans make HORRIBLE mistakes when they are allowed to work without guardrails or scrutiny.
This is what causes bad abuses in Tech, as we’ve seen in the past few years. We must push Data Scientists and Machine Learning Engineers to be like any other scientist or engineer. We must have standards and practices that we have to follow with no one acting like we are demigods.