Speaker 1: This is on the Job, a podcast about finding your
Speaker 1: life's work on the job, is brought to you by
Speaker 1: Express Employment Professionals. This season, we're bringing you stories of
Speaker 1: folks following their passion to carve their own career path.
Speaker 1: In a digital age, software engineers and data scientists are
Speaker 1: the unsung heroes that make all of our convenient technology possible,
Speaker 1: and while they dedicate their careers to making all of
Speaker 1: our lives easier, they often go unnoticed. Well, today we
Speaker 1: talked to a standout techie whose love for science took
Speaker 1: her from jobs and computing to government agencies to working
Speaker 1: with nonprofits on the front lines of the pandemic Today.
Speaker 1: As someone who admittedly thinks that rumbas and Siri will
Speaker 1: take over the world at some point, he was pretty
Speaker 1: reassuring to hear that our interviewee today is not on
Speaker 1: team row Apocalypse absolutely. My goal is certainly to leverage
Speaker 1: data science and AI and service of humanity. This is
Speaker 1: a FUA, the FUA Bruce. I am the chief program
Speaker 1: officer at Data Kind and I'm based just outside of
Speaker 1: the DC area. AFUA is a wildly talented software engineer
Speaker 1: with a pretty fascinating work history. She's currently in a
Speaker 1: more managerial role at Data Kind, a nonprofit organization that
Speaker 1: connects other organizations in the social sector with technology and
Speaker 1: scientists that can help them do their jobs more efficiently.
Speaker 1: The Data Kind has been partnering with a couple of
Speaker 1: different organizations and a couple of different African countries on
Speaker 1: frontline health systems. Frontline health systems basically, you know when
Speaker 1: you go to the doctor and you've got to fill
Speaker 1: out tons and tons of information every time, explaining your
Speaker 1: medical history or past doctors you've seen. Well, that process
Speaker 1: really bogs down frontline health workers. At least over fift
Speaker 1: of a frontline health workers time is spent accessing and
Speaker 1: updating patient records instead of caring for patients. And so
Speaker 1: if you can look how do we improve the data
Speaker 1: integrity of these systems, you can then let frontline health
Speaker 1: workers do the things they want to do, which is
Speaker 1: to care for patients and the things they are most
Speaker 1: trained for. Right So, this is why a lot of
Speaker 1: data Kin's work has been in I mean, your job
Speaker 1: essentially right now is to make other people's jobs easier. Yeah,
Speaker 1: you could sum it up that way. That's the goal.
Speaker 1: Right now. Her projects are in a few African countries
Speaker 1: and in India. Basically, she's making the records that doctors
Speaker 1: and nurses and volunteers need more accessible. The fact that
Speaker 1: frontline workers spend over fifty percent of their time on
Speaker 1: this stuff was really surprising to me. I gotta believe
Speaker 1: that it's not what a lot of doctors are thinking
Speaker 1: about when they make the huge commitment to go to
Speaker 1: medical school. I would say it's probably not high on
Speaker 1: the priority list. Really making sure you understand how to
Speaker 1: fill out different technical systems, how you enter data and systems,
Speaker 1: and how track that that data. Think people often go
Speaker 1: to medical school to be able to practice medicine. These
Speaker 1: projects are only a couple of years old, so she's
Speaker 1: getting to see them work in the field for the
Speaker 1: first time right now, but already organizations that use their
Speaker 1: data systems and software have seen it drastically shave off
Speaker 1: the time workers are spending looking through those records, so
Speaker 1: you can take people who are doing that apply them
Speaker 1: to other things that need to be done instead. It
Speaker 1: also means that people who are looking at that data
Speaker 1: to make decisions about what type of care is best,
Speaker 1: what type of care should we recommend here, what are
Speaker 1: trends that we're seeing here, what should we be changing
Speaker 1: and how should we be reacting based on accurate data
Speaker 1: of what actually happened. So in the way that the
Speaker 1: best technology knows what you need and provides it for you,
Speaker 1: maybe even before you know you need it. Data kind
Speaker 1: systems are meant to be fluid, intelligent tools that substantially
Speaker 1: cut down on the menial task that frontline workers are
Speaker 1: currently in with. And so this means that people can
Speaker 1: go back to providing care to the people who need
Speaker 1: care rather than looking at the streams of data that
Speaker 1: are that's coming in. A fool plays it cool, but
Speaker 1: her work has massive implications that are directly aimed at
Speaker 1: how our health system works all over the World's huge.
Speaker 1: But to back up, it all started off when she
Speaker 1: was a young kid who just like numbers. Growing out,
Speaker 1: my favorite subject was always math. I always really loved computers,
Speaker 1: love playing games and computers. I love video games. Remember
Speaker 1: my dad buying me, uh, you know, remote control cars
Speaker 1: and figuring out those were a lot of fun. She
Speaker 1: moved around a lot as a kid. She lived in
Speaker 1: about nine states. She was into dance and tennis, but
Speaker 1: even at a young age, she was really into volunteering
Speaker 1: and did a lot of social work, and now makes
Speaker 1: sense to me on this side of it that I
Speaker 1: would find myself in a career that lets you figure
Speaker 1: out how to use technology to support communities. Who was
Speaker 1: a first generation American. Both her parents are from Ghana,
Speaker 1: and the mentality in her household growing up instill the
Speaker 1: importance of helping out early on. Just I think the
Speaker 1: focus on community and the focus on the people around
Speaker 1: you and really making sure that you're taking care of
Speaker 1: not just yourself, which is important, but also you're meeting
Speaker 1: needs that you can where possible. She went out to
Speaker 1: study computer engineering at Purdue in Indiana, and while she
Speaker 1: was there, she started working for IBM as a student.
Speaker 1: IBM hired her full time as a software engineer right
Speaker 1: out of school, and she loved it. I really liked
Speaker 1: being an engineer. I really liked toting. I really like
Speaker 1: the different um the software that we were building work
Speaker 1: on some of the IBM S Lurch servers. I enjoyed
Speaker 1: IBMS culture, I really enjoyed the work. Already having an
Speaker 1: impressive start to her career, she actually decided to take
Speaker 1: a leave of absence from IBM, going back to school
Speaker 1: at University of Michigan for an m b a. And
Speaker 1: she fully planned on going back to IBM with her
Speaker 1: newly acquired business degree, but while she was on campus,
Speaker 1: she got recruited as a special advisor for the FBI. Yeah,
Speaker 1: and so I made a an unexpected to everyone shift
Speaker 1: from IBM to the FBI. And that was really the
Speaker 1: start for myself and really seeing how my textkills could
Speaker 1: really more focused in the public interest, working in government
Speaker 1: and working in the nonprofit sector. So when people asked
Speaker 1: you what you did at that point and you said,
Speaker 1: I worked for the FBI, it absolutely felt great to
Speaker 1: say that I worked for the FBI. I really, I
Speaker 1: really did really cool to say. It was definitely cool
Speaker 1: to just say my name as a Foo Bruce and
Speaker 1: I'm here representing the FBI. She worked a lot of
Speaker 1: strategy and program management positions there the Science and Technology branch,
Speaker 1: and you got to work on a lot of the
Speaker 1: tech that the FBI uses. Really got to see how
Speaker 1: tech support, what agents do? What do you mean like
Speaker 1: Q James Bond kind of stuff. Yes. In fact, my
Speaker 1: boss at the time would describe the division as Q.
Speaker 1: That's where all the fun toys were were built and
Speaker 1: tested um and so it's really it's really cool. Did
Speaker 1: you get to, like work on stuff that you can't
Speaker 1: talk about? I think inherently part of what the FBI
Speaker 1: does is things that you can't talk about. However, she
Speaker 1: did get to see some of the stuff she was
Speaker 1: working on, getting notoriety in upper management and even on
Speaker 1: the news, things like her work on forensics technology, improving
Speaker 1: fingerprint tech and systems for gun background checks. It was
Speaker 1: certainly easy to feel a part of something there when
Speaker 1: you know that a lot of the work that you
Speaker 1: do really matters. It was important to keep Americans safe,
Speaker 1: it was important to keep community saying it was important
Speaker 1: to keep and I said that it was important to
Speaker 1: keep children safe. It just goes to show you we
Speaker 1: can be a cool government crime fighter even if you
Speaker 1: don't carry a badge and a gun. People used to
Speaker 1: ask a food if she carried one all the time.
Speaker 1: And I always quote one of my good colleagues and
Speaker 1: a fellow special advisor. He used to say that our
Speaker 1: weapons will excel in power point. So well, the job
Speaker 1: does sound very exciting. Sometimes it really does boil down
Speaker 1: to simple tools into a simple understanding and really figuring
Speaker 1: out how you can leverage your skull set to really
Speaker 1: help move a mission forward. That's exactly what an undercover
Speaker 1: agent would say. We'll get back to our story in
Speaker 1: a second. Navigating the professional job search is hard. You
Speaker 1: know the perfect job is out there, you're just not
Speaker 1: sure how to find it. The good news is you
Speaker 1: don't have to go it alone. You need the Specialized
Speaker 1: Recruiting Group. We're here to guide you and help you
Speaker 1: find a job that fits all without costing a dime.
Speaker 1: We're the Specialized Recruiting Group and Express Employment Professionals Company.
Speaker 1: Go to s r G express dot com for free
Speaker 1: support and get on the right course. Now back to
Speaker 1: on the job. Who was next top? She got put
Speaker 1: on a two year assignment at the White House in
Speaker 1: two thousand fifteen. Under Obama, she served as the Executive
Speaker 1: director of the white Houses National Science and Technology Council,
Speaker 1: basically overseeing a massive think tank with tons of committees
Speaker 1: and experts that convene and come up with plans for
Speaker 1: how we as a country can use science and tech
Speaker 1: to our benefit. There are a lot of experts throughout
Speaker 1: the federal who have spent a lot of time and
Speaker 1: energy educating and being educated on and getting smart on
Speaker 1: and working on both policy and technology development on a
Speaker 1: lot of different areas, and so be able to just
Speaker 1: create an environment where you can convene true expertise and
Speaker 1: then produce something that can really guide future investments in
Speaker 1: future work is is pretty awesome. At this point, she
Speaker 1: really wanted to get back to working with on the
Speaker 1: ground technical projects, which is how she found herself at
Speaker 1: Data Kind where she is now. It really allows me
Speaker 1: the opportunity to combine my data knowledge, my technology knowledge,
Speaker 1: and really passion for working directly with communities and working
Speaker 1: directly with nonprofits. So now she's back to what she
Speaker 1: started off wanting to do, using data science to make
Speaker 1: people's lives easier, which is something we all use every
Speaker 1: day and speaking personally, take for granted, you can yell
Speaker 1: at Sirie to order you a pizza, and thirty minutes
Speaker 1: later you get a pizza. Software engineers make that happen.
Speaker 1: I appreciated a science sometimes for being able to log
Speaker 1: in to whatever shopping platform I'm using at the time
Speaker 1: and being suggested items that just happened to be what
Speaker 1: I want to see and what I want to wear.
Speaker 1: Customizis options presented to me and my life made easier
Speaker 1: by data science that's run in the background. Yeah, I
Speaker 1: mean I love that too, And yeah, yeah, the technology
Speaker 1: is I don't even I don't get how it works.
Speaker 1: It's it's like magic. It's crazy how advanced it is.
Speaker 1: And then for that kind of tech to exist and
Speaker 1: for you to be working on a project at Data Kind,
Speaker 1: like making record systems more accessible for frontline workers, yeah,
Speaker 1: like that's kind of crazy, right, Yeah. Yeah. One of
Speaker 1: the Data Kind co founders used to say, I'm actually
Speaker 1: not sure if it's his quote, I forgot it from
Speaker 1: someone else, but he used to say, the future is here,
Speaker 1: it's just not evenly distributed, which is something that really
Speaker 1: resonates for me, because you know, so many times we
Speaker 1: have these technology systems to your point, just we don't
Speaker 1: even question them anymore, we don't even think about them.
Speaker 1: But in some sectors it just hasn't been adopted yet.
Speaker 1: Technology often mirrors what our priorities are as a society.
Speaker 1: They can be inspiring and also pretty upsetting at the
Speaker 1: same time, Like back in the nineteen sixty nine we
Speaker 1: put humans on the Moon and at the same time
Speaker 1: back on Earth. Cities all over the country didn't have
Speaker 1: clean water systems or roads that work and still don't today.
Speaker 1: I can yell at Syria to get me a pizza
Speaker 1: in thirty minutes, and a frontline worker during COVID might
Speaker 1: have to spend hours and hours on the phone tracking
Speaker 1: down a patient's medical history so that they know what
Speaker 1: they can or can't safely treat them with. You know,
Speaker 1: I joined data kind because social sector organizations who are
Speaker 1: out there saving lives, trying to give people access to healthcare,
Speaker 1: give people access to housing, give people access to food,
Speaker 1: don't always have the time to invest in data systems
Speaker 1: or technology systems, and so ways that data kind can
Speaker 1: really partner with those organizations to help them do your
Speaker 1: mission more effectively, incredibly important work. Afoola joined data kind
Speaker 1: right before the pandemic hit, and she was hearing from
Speaker 1: organizations that we're using data kind systems about how much
Speaker 1: they helped with the crisis. Organizations like Plentiful, an app
Speaker 1: that makes it easier for individuals and families to get
Speaker 1: food from food pantries. And so hearing that kind of
Speaker 1: feedback from an organization like Plentiful or other organizations, you know,
Speaker 1: they will say the work you did helps us save
Speaker 1: you know, the time our drivers are out by or
Speaker 1: something like that, and really refocus their efforts on again
Speaker 1: executing on their mission, which is why people who joined
Speaker 1: nonprofits join on profits. Is just really um that's just
Speaker 1: really great to hear again a food is demeanor. I
Speaker 1: feel like it doesn't match the gravity of the work
Speaker 1: that she's done, but that's because she's really humble. From
Speaker 1: IBM to the FBI, to the White House to her
Speaker 1: work with data kind that could have massive implications around
Speaker 1: the world. The work that she's doing and has done
Speaker 1: is monumental and it's not flying under the radar. In
Speaker 1: two thousand nineteen, she was asked to be an ambassador
Speaker 1: for the if then Initiative, the tagline being if we
Speaker 1: support a woman in STEM, then she can change the world.
Speaker 1: About twenty women were selected from all different science, technology, engineering,
Speaker 1: math professions and the goal of the if then initiative
Speaker 1: is to highlight women in STEM so that girls get
Speaker 1: excited about pursuing STEM career someday. One of the things
Speaker 1: that the if then Initiative did was take three D
Speaker 1: scans of all the ambassadors, and a week before our interview,
Speaker 1: a FUA flew down to Dallas, Texas to see an exhibit.
Speaker 1: They put up a grassy field filled with bright orange statues.
Speaker 1: There are about a to life size statues of women
Speaker 1: in stem, one of them being me, Um, you have
Speaker 1: a statue. I do have a statue. Really flattering and
Speaker 1: really humbling, um and really exciting. How did that feel
Speaker 1: seeing a statue of yourself? Yeah, it was. It was
Speaker 1: surreal walking around the statue exhibit looking for myself and
Speaker 1: then just you know, seeing a life size me holding
Speaker 1: my my laptop by my side was was pretty incredible.
Speaker 1: I had the opportunity to see it for the first
Speaker 1: time with my family, you know, watching my sisters take
Speaker 1: us selfie with my statue also really surreal. Talking with
Speaker 1: the Fua, you probably wouldn't guess this is a person
Speaker 1: who has a statue made of her, not because she shouldn't,
Speaker 1: but because we often dedicate statues to people who are
Speaker 1: the face of a cause or at the front of
Speaker 1: a historic event. And when we make statues of people,
Speaker 1: it's because we want them to be idolized. They are
Speaker 1: figures we choose to literally look up to. It makes
Speaker 1: me pretty happy that as a society we're choosing more
Speaker 1: and more to look up to people like a FUA, Smart, humble,
Speaker 1: people working intelligently and diligently every day to make all
Speaker 1: of our lives easier. People whose weapons are Excel and
Speaker 1: power Point. It must have felt so cool to be
Speaker 1: recognized in that way with a bunch of other people
Speaker 1: who are behind the scenes, in a way that most
Speaker 1: people would never know about them if they were orange
Speaker 1: statues in Dallas, Texas. Right. I can't honestly say that
Speaker 1: I never imagined saying myself as a statue. Having an
Speaker 1: opportunity to be recognized, you know, certain selfishly makes me
Speaker 1: feel great. But again, I'm really hoping that, you know,
Speaker 1: someone will read the description of what I do and
Speaker 1: the lintal plaque that's there next to my statue and think, oh,
Speaker 1: I could also think of ways to use science and
Speaker 1: technology to strengthening communities. That's great for this person who's
Speaker 1: a statue, but that's something I could do too. For
Speaker 1: On the Job, I'm Otis Gray. Thanks for listening to
Speaker 1: On the Job, brought to you by Express Employment Professionals.
Speaker 1: This season of On the Job is produced by Audiation.
Speaker 1: The episodes were written and produced by me Otis Gray,
Speaker 1: our executive producer is Sandy Smallens. The show was mixed
Speaker 1: by Matt Noble for Audiation studios at The Loft and Bronxville,
Speaker 1: New York. Music by Blue Dot Sessions. Find us on
Speaker 1: I Heart Radio and Apple Podcasts. If you liked what
Speaker 1: you heard, please consider rating and reviewing the show on
Speaker 1: Apple Podcasts or wherever you listen. We'll see you next time.
Speaker 1: For more inspiring stories about discovering your life's work, Audiention