WEBVTT - The Unlikely Journey of a Civic-Minded Tech Ninja

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<v Speaker 1>This is on the Job, a podcast about finding your

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<v Speaker 1>life's work on the job, is brought to you by

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<v Speaker 1>Express Employment Professionals. This season, we're bringing you stories of

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<v Speaker 1>folks following their passion to carve their own career path.

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<v Speaker 1>In a digital age, software engineers and data scientists are

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<v Speaker 1>the unsung heroes that make all of our convenient technology possible,

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<v Speaker 1>and while they dedicate their careers to making all of

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<v Speaker 1>our lives easier, they often go unnoticed. Well, today we

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<v Speaker 1>talked to a standout techie whose love for science took

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<v Speaker 1>her from jobs and computing to government agencies to working

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<v Speaker 1>with nonprofits on the front lines of the pandemic Today.

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<v Speaker 1>As someone who admittedly thinks that rumbas and Siri will

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<v Speaker 1>take over the world at some point, he was pretty

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<v Speaker 1>reassuring to hear that our interviewee today is not on

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<v Speaker 1>team row Apocalypse absolutely. My goal is certainly to leverage

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<v Speaker 1>data science and AI and service of humanity. This is

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<v Speaker 1>a FUA, the FUA Bruce. I am the chief program

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<v Speaker 1>officer at Data Kind and I'm based just outside of

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<v Speaker 1>the DC area. AFUA is a wildly talented software engineer

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<v Speaker 1>with a pretty fascinating work history. She's currently in a

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<v Speaker 1>more managerial role at Data Kind, a nonprofit organization that

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<v Speaker 1>connects other organizations in the social sector with technology and

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<v Speaker 1>scientists that can help them do their jobs more efficiently.

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<v Speaker 1>The Data Kind has been partnering with a couple of

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<v Speaker 1>different organizations and a couple of different African countries on

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<v Speaker 1>frontline health systems. Frontline health systems basically, you know when

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<v Speaker 1>you go to the doctor and you've got to fill

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<v Speaker 1>out tons and tons of information every time, explaining your

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<v Speaker 1>medical history or past doctors you've seen. Well, that process

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<v Speaker 1>really bogs down frontline health workers. At least over fift

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<v Speaker 1>of a frontline health workers time is spent accessing and

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<v Speaker 1>updating patient records instead of caring for patients. And so

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<v Speaker 1>if you can look how do we improve the data

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<v Speaker 1>integrity of these systems, you can then let frontline health

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<v Speaker 1>workers do the things they want to do, which is

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<v Speaker 1>to care for patients and the things they are most

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<v Speaker 1>trained for. Right So, this is why a lot of

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<v Speaker 1>data Kin's work has been in I mean, your job

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<v Speaker 1>essentially right now is to make other people's jobs easier. Yeah,

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<v Speaker 1>you could sum it up that way. That's the goal.

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<v Speaker 1>Right now. Her projects are in a few African countries

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<v Speaker 1>and in India. Basically, she's making the records that doctors

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<v Speaker 1>and nurses and volunteers need more accessible. The fact that

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<v Speaker 1>frontline workers spend over fifty percent of their time on

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<v Speaker 1>this stuff was really surprising to me. I gotta believe

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<v Speaker 1>that it's not what a lot of doctors are thinking

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<v Speaker 1>about when they make the huge commitment to go to

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<v Speaker 1>medical school. I would say it's probably not high on

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<v Speaker 1>the priority list. Really making sure you understand how to

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<v Speaker 1>fill out different technical systems, how you enter data and systems,

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<v Speaker 1>and how track that that data. Think people often go

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<v Speaker 1>to medical school to be able to practice medicine. These

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<v Speaker 1>projects are only a couple of years old, so she's

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<v Speaker 1>getting to see them work in the field for the

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<v Speaker 1>first time right now, but already organizations that use their

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<v Speaker 1>data systems and software have seen it drastically shave off

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<v Speaker 1>the time workers are spending looking through those records, so

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<v Speaker 1>you can take people who are doing that apply them

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<v Speaker 1>to other things that need to be done instead. It

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<v Speaker 1>also means that people who are looking at that data

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<v Speaker 1>to make decisions about what type of care is best,

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<v Speaker 1>what type of care should we recommend here, what are

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<v Speaker 1>trends that we're seeing here, what should we be changing

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<v Speaker 1>and how should we be reacting based on accurate data

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<v Speaker 1>of what actually happened. So in the way that the

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<v Speaker 1>best technology knows what you need and provides it for you,

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<v Speaker 1>maybe even before you know you need it. Data kind

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<v Speaker 1>systems are meant to be fluid, intelligent tools that substantially

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<v Speaker 1>cut down on the menial task that frontline workers are

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<v Speaker 1>currently in with. And so this means that people can

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<v Speaker 1>go back to providing care to the people who need

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<v Speaker 1>care rather than looking at the streams of data that

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<v Speaker 1>are that's coming in. A fool plays it cool, but

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<v Speaker 1>her work has massive implications that are directly aimed at

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<v Speaker 1>how our health system works all over the World's huge.

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<v Speaker 1>But to back up, it all started off when she

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<v Speaker 1>was a young kid who just like numbers. Growing out,

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<v Speaker 1>my favorite subject was always math. I always really loved computers,

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<v Speaker 1>love playing games and computers. I love video games. Remember

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<v Speaker 1>my dad buying me, uh, you know, remote control cars

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<v Speaker 1>and figuring out those were a lot of fun. She

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<v Speaker 1>moved around a lot as a kid. She lived in

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<v Speaker 1>about nine states. She was into dance and tennis, but

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<v Speaker 1>even at a young age, she was really into volunteering

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<v Speaker 1>and did a lot of social work, and now makes

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<v Speaker 1>sense to me on this side of it that I

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<v Speaker 1>would find myself in a career that lets you figure

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<v Speaker 1>out how to use technology to support communities. Who was

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<v Speaker 1>a first generation American. Both her parents are from Ghana,

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<v Speaker 1>and the mentality in her household growing up instill the

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<v Speaker 1>importance of helping out early on. Just I think the

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<v Speaker 1>focus on community and the focus on the people around

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<v Speaker 1>you and really making sure that you're taking care of

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<v Speaker 1>not just yourself, which is important, but also you're meeting

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<v Speaker 1>needs that you can where possible. She went out to

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<v Speaker 1>study computer engineering at Purdue in Indiana, and while she

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<v Speaker 1>was there, she started working for IBM as a student.

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<v Speaker 1>IBM hired her full time as a software engineer right

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<v Speaker 1>out of school, and she loved it. I really liked

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<v Speaker 1>being an engineer. I really liked toting. I really like

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<v Speaker 1>the different um the software that we were building work

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<v Speaker 1>on some of the IBM S Lurch servers. I enjoyed

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<v Speaker 1>IBMS culture, I really enjoyed the work. Already having an

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<v Speaker 1>impressive start to her career, she actually decided to take

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<v Speaker 1>a leave of absence from IBM, going back to school

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<v Speaker 1>at University of Michigan for an m b a. And

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<v Speaker 1>she fully planned on going back to IBM with her

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<v Speaker 1>newly acquired business degree, but while she was on campus,

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<v Speaker 1>she got recruited as a special advisor for the FBI. Yeah,

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<v Speaker 1>and so I made a an unexpected to everyone shift

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<v Speaker 1>from IBM to the FBI. And that was really the

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<v Speaker 1>start for myself and really seeing how my textkills could

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<v Speaker 1>really more focused in the public interest, working in government

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<v Speaker 1>and working in the nonprofit sector. So when people asked

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<v Speaker 1>you what you did at that point and you said,

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<v Speaker 1>I worked for the FBI, it absolutely felt great to

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<v Speaker 1>say that I worked for the FBI. I really, I

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<v Speaker 1>really did really cool to say. It was definitely cool

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<v Speaker 1>to just say my name as a Foo Bruce and

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<v Speaker 1>I'm here representing the FBI. She worked a lot of

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<v Speaker 1>strategy and program management positions there the Science and Technology branch,

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<v Speaker 1>and you got to work on a lot of the

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<v Speaker 1>tech that the FBI uses. Really got to see how

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<v Speaker 1>tech support, what agents do? What do you mean like

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<v Speaker 1>Q James Bond kind of stuff. Yes. In fact, my

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<v Speaker 1>boss at the time would describe the division as Q.

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<v Speaker 1>That's where all the fun toys were were built and

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<v Speaker 1>tested um and so it's really it's really cool. Did

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<v Speaker 1>you get to, like work on stuff that you can't

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<v Speaker 1>talk about? I think inherently part of what the FBI

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<v Speaker 1>does is things that you can't talk about. However, she

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<v Speaker 1>did get to see some of the stuff she was

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<v Speaker 1>working on, getting notoriety in upper management and even on

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<v Speaker 1>the news, things like her work on forensics technology, improving

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<v Speaker 1>fingerprint tech and systems for gun background checks. It was

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<v Speaker 1>certainly easy to feel a part of something there when

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<v Speaker 1>you know that a lot of the work that you

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<v Speaker 1>do really matters. It was important to keep Americans safe,

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<v Speaker 1>it was important to keep community saying it was important

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<v Speaker 1>to keep and I said that it was important to

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<v Speaker 1>keep children safe. It just goes to show you we

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<v Speaker 1>can be a cool government crime fighter even if you

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<v Speaker 1>don't carry a badge and a gun. People used to

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<v Speaker 1>ask a food if she carried one all the time.

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<v Speaker 1>And I always quote one of my good colleagues and

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<v Speaker 1>a fellow special advisor. He used to say that our

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<v Speaker 1>weapons will excel in power point. So well, the job

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<v Speaker 1>does sound very exciting. Sometimes it really does boil down

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<v Speaker 1>to simple tools into a simple understanding and really figuring

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<v Speaker 1>out how you can leverage your skull set to really

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<v Speaker 1>help move a mission forward. That's exactly what an undercover

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<v Speaker 1>agent would say. We'll get back to our story in

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<v Speaker 1>a second. Navigating the professional job search is hard. You

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<v Speaker 1>support and get on the right course. Now back to

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<v Speaker 1>on the job. Who was next top? She got put

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<v Speaker 1>on a two year assignment at the White House in

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<v Speaker 1>two thousand fifteen. Under Obama, she served as the Executive

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<v Speaker 1>director of the white Houses National Science and Technology Council,

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<v Speaker 1>basically overseeing a massive think tank with tons of committees

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<v Speaker 1>and experts that convene and come up with plans for

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<v Speaker 1>how we as a country can use science and tech

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<v Speaker 1>to our benefit. There are a lot of experts throughout

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<v Speaker 1>the federal who have spent a lot of time and

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<v Speaker 1>energy educating and being educated on and getting smart on

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<v Speaker 1>and working on both policy and technology development on a

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<v Speaker 1>lot of different areas, and so be able to just

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<v Speaker 1>create an environment where you can convene true expertise and

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<v Speaker 1>then produce something that can really guide future investments in

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<v Speaker 1>future work is is pretty awesome. At this point, she

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<v Speaker 1>really wanted to get back to working with on the

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<v Speaker 1>ground technical projects, which is how she found herself at

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<v Speaker 1>Data Kind where she is now. It really allows me

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<v Speaker 1>the opportunity to combine my data knowledge, my technology knowledge,

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<v Speaker 1>and really passion for working directly with communities and working

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<v Speaker 1>directly with nonprofits. So now she's back to what she

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<v Speaker 1>started off wanting to do, using data science to make

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<v Speaker 1>people's lives easier, which is something we all use every

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<v Speaker 1>day and speaking personally, take for granted, you can yell

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<v Speaker 1>at Sirie to order you a pizza, and thirty minutes

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<v Speaker 1>later you get a pizza. Software engineers make that happen.

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<v Speaker 1>I appreciated a science sometimes for being able to log

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<v Speaker 1>in to whatever shopping platform I'm using at the time

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<v Speaker 1>and being suggested items that just happened to be what

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<v Speaker 1>I want to see and what I want to wear.

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<v Speaker 1>Customizis options presented to me and my life made easier

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<v Speaker 1>by data science that's run in the background. Yeah, I

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<v Speaker 1>mean I love that too, And yeah, yeah, the technology

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<v Speaker 1>is I don't even I don't get how it works.

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<v Speaker 1>It's it's like magic. It's crazy how advanced it is.

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<v Speaker 1>And then for that kind of tech to exist and

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<v Speaker 1>for you to be working on a project at Data Kind,

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<v Speaker 1>like making record systems more accessible for frontline workers, yeah,

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<v Speaker 1>like that's kind of crazy, right, Yeah. Yeah. One of

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<v Speaker 1>the Data Kind co founders used to say, I'm actually

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<v Speaker 1>not sure if it's his quote, I forgot it from

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<v Speaker 1>someone else, but he used to say, the future is here,

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<v Speaker 1>it's just not evenly distributed, which is something that really

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<v Speaker 1>resonates for me, because you know, so many times we

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<v Speaker 1>have these technology systems to your point, just we don't

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<v Speaker 1>even question them anymore, we don't even think about them.

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<v Speaker 1>But in some sectors it just hasn't been adopted yet.

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<v Speaker 1>Technology often mirrors what our priorities are as a society.

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<v Speaker 1>They can be inspiring and also pretty upsetting at the

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<v Speaker 1>same time, Like back in the nineteen sixty nine we

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<v Speaker 1>put humans on the Moon and at the same time

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<v Speaker 1>back on Earth. Cities all over the country didn't have

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<v Speaker 1>clean water systems or roads that work and still don't today.

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<v Speaker 1>I can yell at Syria to get me a pizza

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<v Speaker 1>in thirty minutes, and a frontline worker during COVID might

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<v Speaker 1>have to spend hours and hours on the phone tracking

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<v Speaker 1>down a patient's medical history so that they know what

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<v Speaker 1>they can or can't safely treat them with. You know,

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<v Speaker 1>I joined data kind because social sector organizations who are

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<v Speaker 1>out there saving lives, trying to give people access to healthcare,

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<v Speaker 1>give people access to housing, give people access to food,

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<v Speaker 1>don't always have the time to invest in data systems

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<v Speaker 1>or technology systems, and so ways that data kind can

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<v Speaker 1>really partner with those organizations to help them do your

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<v Speaker 1>mission more effectively, incredibly important work. Afoola joined data kind

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<v Speaker 1>right before the pandemic hit, and she was hearing from

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<v Speaker 1>organizations that we're using data kind systems about how much

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<v Speaker 1>they helped with the crisis. Organizations like Plentiful, an app

0:13:21.920 --> 0:13:24.760
<v Speaker 1>that makes it easier for individuals and families to get

0:13:24.800 --> 0:13:27.720
<v Speaker 1>food from food pantries. And so hearing that kind of

0:13:27.760 --> 0:13:31.880
<v Speaker 1>feedback from an organization like Plentiful or other organizations, you know,

0:13:31.880 --> 0:13:34.120
<v Speaker 1>they will say the work you did helps us save

0:13:34.720 --> 0:13:38.160
<v Speaker 1>you know, the time our drivers are out by or

0:13:38.200 --> 0:13:41.520
<v Speaker 1>something like that, and really refocus their efforts on again

0:13:41.760 --> 0:13:43.960
<v Speaker 1>executing on their mission, which is why people who joined

0:13:43.960 --> 0:13:48.080
<v Speaker 1>nonprofits join on profits. Is just really um that's just

0:13:48.160 --> 0:13:57.360
<v Speaker 1>really great to hear again a food is demeanor. I

0:13:57.400 --> 0:13:59.960
<v Speaker 1>feel like it doesn't match the gravity of the work

0:14:00.040 --> 0:14:02.720
<v Speaker 1>that she's done, but that's because she's really humble. From

0:14:02.760 --> 0:14:05.839
<v Speaker 1>IBM to the FBI, to the White House to her

0:14:05.880 --> 0:14:09.079
<v Speaker 1>work with data kind that could have massive implications around

0:14:09.080 --> 0:14:11.840
<v Speaker 1>the world. The work that she's doing and has done

0:14:12.000 --> 0:14:16.640
<v Speaker 1>is monumental and it's not flying under the radar. In

0:14:16.679 --> 0:14:19.400
<v Speaker 1>two thousand nineteen, she was asked to be an ambassador

0:14:19.440 --> 0:14:22.600
<v Speaker 1>for the if then Initiative, the tagline being if we

0:14:22.640 --> 0:14:25.720
<v Speaker 1>support a woman in STEM, then she can change the world.

0:14:26.880 --> 0:14:32.360
<v Speaker 1>About twenty women were selected from all different science, technology, engineering,

0:14:32.440 --> 0:14:36.720
<v Speaker 1>math professions and the goal of the if then initiative

0:14:36.920 --> 0:14:40.920
<v Speaker 1>is to highlight women in STEM so that girls get

0:14:40.920 --> 0:14:45.560
<v Speaker 1>excited about pursuing STEM career someday. One of the things

0:14:45.560 --> 0:14:47.800
<v Speaker 1>that the if then Initiative did was take three D

0:14:47.920 --> 0:14:51.320
<v Speaker 1>scans of all the ambassadors, and a week before our interview,

0:14:51.560 --> 0:14:54.520
<v Speaker 1>a FUA flew down to Dallas, Texas to see an exhibit.

0:14:54.560 --> 0:14:58.000
<v Speaker 1>They put up a grassy field filled with bright orange statues.

0:14:58.520 --> 0:15:02.720
<v Speaker 1>There are about a to life size statues of women

0:15:02.760 --> 0:15:06.520
<v Speaker 1>in stem, one of them being me, Um, you have

0:15:06.560 --> 0:15:11.120
<v Speaker 1>a statue. I do have a statue. Really flattering and

0:15:11.160 --> 0:15:18.480
<v Speaker 1>really humbling, um and really exciting. How did that feel

0:15:18.480 --> 0:15:22.960
<v Speaker 1>seeing a statue of yourself? Yeah, it was. It was

0:15:23.000 --> 0:15:28.280
<v Speaker 1>surreal walking around the statue exhibit looking for myself and

0:15:28.280 --> 0:15:32.040
<v Speaker 1>then just you know, seeing a life size me holding

0:15:32.080 --> 0:15:36.000
<v Speaker 1>my my laptop by my side was was pretty incredible.

0:15:36.000 --> 0:15:38.840
<v Speaker 1>I had the opportunity to see it for the first

0:15:38.880 --> 0:15:41.920
<v Speaker 1>time with my family, you know, watching my sisters take

0:15:42.000 --> 0:15:53.480
<v Speaker 1>us selfie with my statue also really surreal. Talking with

0:15:53.480 --> 0:15:56.680
<v Speaker 1>the Fua, you probably wouldn't guess this is a person

0:15:56.800 --> 0:16:00.000
<v Speaker 1>who has a statue made of her, not because she shouldn't,

0:16:00.360 --> 0:16:03.960
<v Speaker 1>but because we often dedicate statues to people who are

0:16:04.200 --> 0:16:06.520
<v Speaker 1>the face of a cause or at the front of

0:16:06.520 --> 0:16:09.480
<v Speaker 1>a historic event. And when we make statues of people,

0:16:09.880 --> 0:16:12.720
<v Speaker 1>it's because we want them to be idolized. They are

0:16:12.800 --> 0:16:17.960
<v Speaker 1>figures we choose to literally look up to. It makes

0:16:17.960 --> 0:16:21.280
<v Speaker 1>me pretty happy that as a society we're choosing more

0:16:21.320 --> 0:16:25.480
<v Speaker 1>and more to look up to people like a FUA, Smart, humble,

0:16:25.520 --> 0:16:29.560
<v Speaker 1>people working intelligently and diligently every day to make all

0:16:29.600 --> 0:16:32.960
<v Speaker 1>of our lives easier. People whose weapons are Excel and

0:16:33.000 --> 0:16:35.800
<v Speaker 1>power Point. It must have felt so cool to be

0:16:36.440 --> 0:16:39.480
<v Speaker 1>recognized in that way with a bunch of other people

0:16:39.520 --> 0:16:42.160
<v Speaker 1>who are behind the scenes, in a way that most

0:16:42.200 --> 0:16:45.080
<v Speaker 1>people would never know about them if they were orange

0:16:45.080 --> 0:16:50.800
<v Speaker 1>statues in Dallas, Texas. Right. I can't honestly say that

0:16:51.120 --> 0:16:57.280
<v Speaker 1>I never imagined saying myself as a statue. Having an

0:16:57.280 --> 0:17:01.440
<v Speaker 1>opportunity to be recognized, you know, certain selfishly makes me

0:17:01.520 --> 0:17:05.040
<v Speaker 1>feel great. But again, I'm really hoping that, you know,

0:17:05.119 --> 0:17:08.000
<v Speaker 1>someone will read the description of what I do and

0:17:08.119 --> 0:17:12.280
<v Speaker 1>the lintal plaque that's there next to my statue and think, oh,

0:17:12.440 --> 0:17:15.000
<v Speaker 1>I could also think of ways to use science and

0:17:15.040 --> 0:17:18.680
<v Speaker 1>technology to strengthening communities. That's great for this person who's

0:17:18.680 --> 0:17:30.440
<v Speaker 1>a statue, but that's something I could do too. For

0:17:30.560 --> 0:17:41.520
<v Speaker 1>On the Job, I'm Otis Gray. Thanks for listening to

0:17:41.600 --> 0:17:44.600
<v Speaker 1>On the Job, brought to you by Express Employment Professionals.

0:17:45.720 --> 0:17:48.159
<v Speaker 1>This season of On the Job is produced by Audiation.

0:17:48.560 --> 0:17:51.560
<v Speaker 1>The episodes were written and produced by me Otis Gray,

0:17:51.960 --> 0:17:55.160
<v Speaker 1>our executive producer is Sandy Smallens. The show was mixed

0:17:55.200 --> 0:17:58.560
<v Speaker 1>by Matt Noble for Audiation studios at The Loft and Bronxville,

0:17:58.560 --> 0:18:02.320
<v Speaker 1>New York. Music by Blue Dot Sessions. Find us on

0:18:02.400 --> 0:18:05.919
<v Speaker 1>I Heart Radio and Apple Podcasts. If you liked what

0:18:05.960 --> 0:18:08.560
<v Speaker 1>you heard, please consider rating and reviewing the show on

0:18:08.640 --> 0:18:12.600
<v Speaker 1>Apple Podcasts or wherever you listen. We'll see you next time.

0:18:12.640 --> 0:18:21.160
<v Speaker 1>For more inspiring stories about discovering your life's work, Audiention