June 14, 2021 • 18 mins
Afua Bruce's career in data science and technology is dotted with a bunch of impressive acronyms: IBM, the FBI (!), even working for the POTUS (Obama) as Executive Director of the National Science and Technology Council. Now, she's taken on a new challenge as Chief Program Officer for Datakind, a global non-profit that harnesses the power of data science and AI in the service of humanity. Heck, she even has her own statue as part of the #IfThenSheCan women in STEM initiative (https://ifthenshecan.org). Tune in to hear an amazing story of a truly unique life path.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:06):
This is on the Job, a podcast about finding your
life's work on the job, is brought to you by
Express Employment Professionals. This season, we're bringing you stories of
folks following their passion to carve their own career path.
In a digital age, software engineers and data scientists are
the unsung heroes that make all of our convenient technology possible,
(00:28):
and while they dedicate their careers to making all of
our lives easier, they often go unnoticed. Well, today we
talked to a standout techie whose love for science took
her from jobs and computing to government agencies to working
with nonprofits on the front lines of the pandemic Today.
As someone who admittedly thinks that rumbas and Siri will
(00:48):
take over the world at some point, he was pretty
reassuring to hear that our interviewee today is not on
team row Apocalypse absolutely. My goal is certainly to leverage
data science and AI and service of humanity. This is
a FUA, the FUA Bruce. I am the chief program
officer at Data Kind and I'm based just outside of
(01:09):
the DC area. AFUA is a wildly talented software engineer
with a pretty fascinating work history. She's currently in a
more managerial role at Data Kind, a nonprofit organization that
connects other organizations in the social sector with technology and
scientists that can help them do their jobs more efficiently.
The Data Kind has been partnering with a couple of
(01:31):
different organizations and a couple of different African countries on
frontline health systems. Frontline health systems basically, you know when
you go to the doctor and you've got to fill
out tons and tons of information every time, explaining your
medical history or past doctors you've seen. Well, that process
really bogs down frontline health workers. At least over fift
(01:52):
of a frontline health workers time is spent accessing and
updating patient records instead of caring for patients. And so
if you can look how do we improve the data
integrity of these systems, you can then let frontline health
workers do the things they want to do, which is
to care for patients and the things they are most
trained for. Right So, this is why a lot of
(02:12):
data Kin's work has been in I mean, your job
essentially right now is to make other people's jobs easier. Yeah,
you could sum it up that way. That's the goal.
Right now. Her projects are in a few African countries
and in India. Basically, she's making the records that doctors
(02:34):
and nurses and volunteers need more accessible. The fact that
frontline workers spend over fifty percent of their time on
this stuff was really surprising to me. I gotta believe
that it's not what a lot of doctors are thinking
about when they make the huge commitment to go to
medical school. I would say it's probably not high on
the priority list. Really making sure you understand how to
(02:55):
fill out different technical systems, how you enter data and systems,
and how track that that data. Think people often go
to medical school to be able to practice medicine. These
projects are only a couple of years old, so she's
getting to see them work in the field for the
first time right now, but already organizations that use their
data systems and software have seen it drastically shave off
(03:17):
the time workers are spending looking through those records, so
you can take people who are doing that apply them
to other things that need to be done instead. It
also means that people who are looking at that data
to make decisions about what type of care is best,
what type of care should we recommend here, what are
trends that we're seeing here, what should we be changing
(03:38):
and how should we be reacting based on accurate data
of what actually happened. So in the way that the
best technology knows what you need and provides it for you,
maybe even before you know you need it. Data kind
systems are meant to be fluid, intelligent tools that substantially
cut down on the menial task that frontline workers are
(03:59):
currently in with. And so this means that people can
go back to providing care to the people who need
care rather than looking at the streams of data that
are that's coming in. A fool plays it cool, but
her work has massive implications that are directly aimed at
(04:21):
how our health system works all over the World's huge.
But to back up, it all started off when she
was a young kid who just like numbers. Growing out,
my favorite subject was always math. I always really loved computers,
love playing games and computers. I love video games. Remember
my dad buying me, uh, you know, remote control cars
and figuring out those were a lot of fun. She
(04:44):
moved around a lot as a kid. She lived in
about nine states. She was into dance and tennis, but
even at a young age, she was really into volunteering
and did a lot of social work, and now makes
sense to me on this side of it that I
would find myself in a career that lets you figure
out how to use technology to support communities. Who was
(05:04):
a first generation American. Both her parents are from Ghana,
and the mentality in her household growing up instill the
importance of helping out early on. Just I think the
focus on community and the focus on the people around
you and really making sure that you're taking care of
not just yourself, which is important, but also you're meeting
needs that you can where possible. She went out to
(05:26):
study computer engineering at Purdue in Indiana, and while she
was there, she started working for IBM as a student.
IBM hired her full time as a software engineer right
out of school, and she loved it. I really liked
being an engineer. I really liked toting. I really like
the different um the software that we were building work
on some of the IBM S Lurch servers. I enjoyed
(05:50):
IBMS culture, I really enjoyed the work. Already having an
impressive start to her career, she actually decided to take
a leave of absence from IBM, going back to school
at University of Michigan for an m b a. And
she fully planned on going back to IBM with her
newly acquired business degree, but while she was on campus,
she got recruited as a special advisor for the FBI. Yeah,
(06:16):
and so I made a an unexpected to everyone shift
from IBM to the FBI. And that was really the
start for myself and really seeing how my textkills could
really more focused in the public interest, working in government
and working in the nonprofit sector. So when people asked
(06:38):
you what you did at that point and you said,
I worked for the FBI, it absolutely felt great to
say that I worked for the FBI. I really, I
really did really cool to say. It was definitely cool
to just say my name as a Foo Bruce and
I'm here representing the FBI. She worked a lot of
strategy and program management positions there the Science and Technology branch,
(07:01):
and you got to work on a lot of the
tech that the FBI uses. Really got to see how
tech support, what agents do? What do you mean like
Q James Bond kind of stuff. Yes. In fact, my
boss at the time would describe the division as Q.
That's where all the fun toys were were built and
tested um and so it's really it's really cool. Did
(07:23):
you get to, like work on stuff that you can't
talk about? I think inherently part of what the FBI
does is things that you can't talk about. However, she
did get to see some of the stuff she was
working on, getting notoriety in upper management and even on
the news, things like her work on forensics technology, improving
fingerprint tech and systems for gun background checks. It was
(07:46):
certainly easy to feel a part of something there when
you know that a lot of the work that you
do really matters. It was important to keep Americans safe,
it was important to keep community saying it was important
to keep and I said that it was important to
keep children safe. It just goes to show you we
can be a cool government crime fighter even if you
(08:07):
don't carry a badge and a gun. People used to
ask a food if she carried one all the time.
And I always quote one of my good colleagues and
a fellow special advisor. He used to say that our
weapons will excel in power point. So well, the job
does sound very exciting. Sometimes it really does boil down
to simple tools into a simple understanding and really figuring
(08:29):
out how you can leverage your skull set to really
help move a mission forward. That's exactly what an undercover
agent would say. We'll get back to our story in
a second. Navigating the professional job search is hard. You
(08:51):
know the perfect job is out there, you're just not
sure how to find it. The good news is you
don't have to go it alone. You need the Specialized
Recruiting Group. We're here to guide you and help you
find a job that fits all without costing a dime.
We're the Specialized Recruiting Group and Express Employment Professionals Company.
Go to s r G express dot com for free
(09:12):
support and get on the right course. Now back to
on the job. Who was next top? She got put
on a two year assignment at the White House in
two thousand fifteen. Under Obama, she served as the Executive
director of the white Houses National Science and Technology Council,
(09:34):
basically overseeing a massive think tank with tons of committees
and experts that convene and come up with plans for
how we as a country can use science and tech
to our benefit. There are a lot of experts throughout
the federal who have spent a lot of time and
energy educating and being educated on and getting smart on
(09:55):
and working on both policy and technology development on a
lot of different areas, and so be able to just
create an environment where you can convene true expertise and
then produce something that can really guide future investments in
future work is is pretty awesome. At this point, she
really wanted to get back to working with on the
ground technical projects, which is how she found herself at
(10:17):
Data Kind where she is now. It really allows me
the opportunity to combine my data knowledge, my technology knowledge,
and really passion for working directly with communities and working
directly with nonprofits. So now she's back to what she
started off wanting to do, using data science to make
people's lives easier, which is something we all use every
(10:39):
day and speaking personally, take for granted, you can yell
at Sirie to order you a pizza, and thirty minutes
later you get a pizza. Software engineers make that happen.
I appreciated a science sometimes for being able to log
in to whatever shopping platform I'm using at the time
and being suggested items that just happened to be what
(11:02):
I want to see and what I want to wear.
Customizis options presented to me and my life made easier
by data science that's run in the background. Yeah, I
mean I love that too, And yeah, yeah, the technology
is I don't even I don't get how it works.
It's it's like magic. It's crazy how advanced it is.
And then for that kind of tech to exist and
(11:26):
for you to be working on a project at Data Kind,
like making record systems more accessible for frontline workers, yeah,
like that's kind of crazy, right, Yeah. Yeah. One of
the Data Kind co founders used to say, I'm actually
not sure if it's his quote, I forgot it from
someone else, but he used to say, the future is here,
(11:47):
it's just not evenly distributed, which is something that really
resonates for me, because you know, so many times we
have these technology systems to your point, just we don't
even question them anymore, we don't even think about them.
But in some sectors it just hasn't been adopted yet.
(12:11):
Technology often mirrors what our priorities are as a society.
They can be inspiring and also pretty upsetting at the
same time, Like back in the nineteen sixty nine we
put humans on the Moon and at the same time
back on Earth. Cities all over the country didn't have
clean water systems or roads that work and still don't today.
I can yell at Syria to get me a pizza
(12:33):
in thirty minutes, and a frontline worker during COVID might
have to spend hours and hours on the phone tracking
down a patient's medical history so that they know what
they can or can't safely treat them with. You know,
I joined data kind because social sector organizations who are
out there saving lives, trying to give people access to healthcare,
(12:53):
give people access to housing, give people access to food,
don't always have the time to invest in data systems
or technology systems, and so ways that data kind can
really partner with those organizations to help them do your
mission more effectively, incredibly important work. Afoola joined data kind
right before the pandemic hit, and she was hearing from
(13:14):
organizations that we're using data kind systems about how much
they helped with the crisis. Organizations like Plentiful, an app
that makes it easier for individuals and families to get
food from food pantries. And so hearing that kind of
feedback from an organization like Plentiful or other organizations, you know,
they will say the work you did helps us save
(13:34):
you know, the time our drivers are out by or
something like that, and really refocus their efforts on again
executing on their mission, which is why people who joined
nonprofits join on profits. Is just really um that's just
really great to hear again a food is demeanor. I
(13:57):
feel like it doesn't match the gravity of the work
that she's done, but that's because she's really humble. From
IBM to the FBI, to the White House to her
work with data kind that could have massive implications around
the world. The work that she's doing and has done
is monumental and it's not flying under the radar. In
two thousand nineteen, she was asked to be an ambassador
(14:19):
for the if then Initiative, the tagline being if we
support a woman in STEM, then she can change the world.
About twenty women were selected from all different science, technology, engineering,
math professions and the goal of the if then initiative
is to highlight women in STEM so that girls get
(14:40):
excited about pursuing STEM career someday. One of the things
that the if then Initiative did was take three D
scans of all the ambassadors, and a week before our interview,
a FUA flew down to Dallas, Texas to see an exhibit.
They put up a grassy field filled with bright orange statues.
There are about a to life size statues of women
(15:02):
in stem, one of them being me, Um, you have
a statue. I do have a statue. Really flattering and
really humbling, um and really exciting. How did that feel
seeing a statue of yourself? Yeah, it was. It was
(15:23):
surreal walking around the statue exhibit looking for myself and
then just you know, seeing a life size me holding
my my laptop by my side was was pretty incredible.
I had the opportunity to see it for the first
time with my family, you know, watching my sisters take
us selfie with my statue also really surreal. Talking with
(15:53):
the Fua, you probably wouldn't guess this is a person
who has a statue made of her, not because she shouldn't,
but because we often dedicate statues to people who are
the face of a cause or at the front of
a historic event. And when we make statues of people,
it's because we want them to be idolized. They are
figures we choose to literally look up to. It makes
(16:17):
me pretty happy that as a society we're choosing more
and more to look up to people like a FUA, Smart, humble,
people working intelligently and diligently every day to make all
of our lives easier. People whose weapons are Excel and
power Point. It must have felt so cool to be
recognized in that way with a bunch of other people
(16:39):
who are behind the scenes, in a way that most
people would never know about them if they were orange
statues in Dallas, Texas. Right. I can't honestly say that
I never imagined saying myself as a statue. Having an
opportunity to be recognized, you know, certain selfishly makes me
(17:01):
feel great. But again, I'm really hoping that, you know,
someone will read the description of what I do and
the lintal plaque that's there next to my statue and think, oh,
I could also think of ways to use science and
technology to strengthening communities. That's great for this person who's
a statue, but that's something I could do too. For
(17:30):
On the Job, I'm Otis Gray. Thanks for listening to
On the Job, brought to you by Express Employment Professionals.
This season of On the Job is produced by Audiation.
The episodes were written and produced by me Otis Gray,
(17:51):
our executive producer is Sandy Smallens. The show was mixed
by Matt Noble for Audiation studios at The Loft and Bronxville,
New York. Music by Blue Dot Sessions. Find us on
I Heart Radio and Apple Podcasts. If you liked what
you heard, please consider rating and reviewing the show on
Apple Podcasts or wherever you listen. We'll see you next time.
(18:12):
For more inspiring stories about discovering your life's work, Audiention
This is on the Job, a podcast about finding your
life's work on the job, is brought to you by
Express Employment Professionals. This season, we're bringing you stories of
folks following their passion to carve their own career path.
In a digital age, software engineers and data scientists are
the unsung heroes that make all of our convenient technology possible,
(00:28):
and while they dedicate their careers to making all of
our lives easier, they often go unnoticed. Well, today we
talked to a standout techie whose love for science took
her from jobs and computing to government agencies to working
with nonprofits on the front lines of the pandemic Today.
As someone who admittedly thinks that rumbas and Siri will
(00:48):
take over the world at some point, he was pretty
reassuring to hear that our interviewee today is not on
team row Apocalypse absolutely. My goal is certainly to leverage
data science and AI and service of humanity. This is
a FUA, the FUA Bruce. I am the chief program
officer at Data Kind and I'm based just outside of
(01:09):
the DC area. AFUA is a wildly talented software engineer
with a pretty fascinating work history. She's currently in a
more managerial role at Data Kind, a nonprofit organization that
connects other organizations in the social sector with technology and
scientists that can help them do their jobs more efficiently.
The Data Kind has been partnering with a couple of
(01:31):
different organizations and a couple of different African countries on
frontline health systems. Frontline health systems basically, you know when
you go to the doctor and you've got to fill
out tons and tons of information every time, explaining your
medical history or past doctors you've seen. Well, that process
really bogs down frontline health workers. At least over fift
(01:52):
of a frontline health workers time is spent accessing and
updating patient records instead of caring for patients. And so
if you can look how do we improve the data
integrity of these systems, you can then let frontline health
workers do the things they want to do, which is
to care for patients and the things they are most
trained for. Right So, this is why a lot of
(02:12):
data Kin's work has been in I mean, your job
essentially right now is to make other people's jobs easier. Yeah,
you could sum it up that way. That's the goal.
Right now. Her projects are in a few African countries
and in India. Basically, she's making the records that doctors
(02:34):
and nurses and volunteers need more accessible. The fact that
frontline workers spend over fifty percent of their time on
this stuff was really surprising to me. I gotta believe
that it's not what a lot of doctors are thinking
about when they make the huge commitment to go to
medical school. I would say it's probably not high on
the priority list. Really making sure you understand how to
(02:55):
fill out different technical systems, how you enter data and systems,
and how track that that data. Think people often go
to medical school to be able to practice medicine. These
projects are only a couple of years old, so she's
getting to see them work in the field for the
first time right now, but already organizations that use their
data systems and software have seen it drastically shave off
(03:17):
the time workers are spending looking through those records, so
you can take people who are doing that apply them
to other things that need to be done instead. It
also means that people who are looking at that data
to make decisions about what type of care is best,
what type of care should we recommend here, what are
trends that we're seeing here, what should we be changing
(03:38):
and how should we be reacting based on accurate data
of what actually happened. So in the way that the
best technology knows what you need and provides it for you,
maybe even before you know you need it. Data kind
systems are meant to be fluid, intelligent tools that substantially
cut down on the menial task that frontline workers are
(03:59):
currently in with. And so this means that people can
go back to providing care to the people who need
care rather than looking at the streams of data that
are that's coming in. A fool plays it cool, but
her work has massive implications that are directly aimed at
(04:21):
how our health system works all over the World's huge.
But to back up, it all started off when she
was a young kid who just like numbers. Growing out,
my favorite subject was always math. I always really loved computers,
love playing games and computers. I love video games. Remember
my dad buying me, uh, you know, remote control cars
and figuring out those were a lot of fun. She
(04:44):
moved around a lot as a kid. She lived in
about nine states. She was into dance and tennis, but
even at a young age, she was really into volunteering
and did a lot of social work, and now makes
sense to me on this side of it that I
would find myself in a career that lets you figure
out how to use technology to support communities. Who was
(05:04):
a first generation American. Both her parents are from Ghana,
and the mentality in her household growing up instill the
importance of helping out early on. Just I think the
focus on community and the focus on the people around
you and really making sure that you're taking care of
not just yourself, which is important, but also you're meeting
needs that you can where possible. She went out to
(05:26):
study computer engineering at Purdue in Indiana, and while she
was there, she started working for IBM as a student.
IBM hired her full time as a software engineer right
out of school, and she loved it. I really liked
being an engineer. I really liked toting. I really like
the different um the software that we were building work
on some of the IBM S Lurch servers. I enjoyed
(05:50):
IBMS culture, I really enjoyed the work. Already having an
impressive start to her career, she actually decided to take
a leave of absence from IBM, going back to school
at University of Michigan for an m b a. And
she fully planned on going back to IBM with her
newly acquired business degree, but while she was on campus,
she got recruited as a special advisor for the FBI. Yeah,
(06:16):
and so I made a an unexpected to everyone shift
from IBM to the FBI. And that was really the
start for myself and really seeing how my textkills could
really more focused in the public interest, working in government
and working in the nonprofit sector. So when people asked
(06:38):
you what you did at that point and you said,
I worked for the FBI, it absolutely felt great to
say that I worked for the FBI. I really, I
really did really cool to say. It was definitely cool
to just say my name as a Foo Bruce and
I'm here representing the FBI. She worked a lot of
strategy and program management positions there the Science and Technology branch,
(07:01):
and you got to work on a lot of the
tech that the FBI uses. Really got to see how
tech support, what agents do? What do you mean like
Q James Bond kind of stuff. Yes. In fact, my
boss at the time would describe the division as Q.
That's where all the fun toys were were built and
tested um and so it's really it's really cool. Did
(07:23):
you get to, like work on stuff that you can't
talk about? I think inherently part of what the FBI
does is things that you can't talk about. However, she
did get to see some of the stuff she was
working on, getting notoriety in upper management and even on
the news, things like her work on forensics technology, improving
fingerprint tech and systems for gun background checks. It was
(07:46):
certainly easy to feel a part of something there when
you know that a lot of the work that you
do really matters. It was important to keep Americans safe,
it was important to keep community saying it was important
to keep and I said that it was important to
keep children safe. It just goes to show you we
can be a cool government crime fighter even if you
(08:07):
don't carry a badge and a gun. People used to
ask a food if she carried one all the time.
And I always quote one of my good colleagues and
a fellow special advisor. He used to say that our
weapons will excel in power point. So well, the job
does sound very exciting. Sometimes it really does boil down
to simple tools into a simple understanding and really figuring
(08:29):
out how you can leverage your skull set to really
help move a mission forward. That's exactly what an undercover
agent would say. We'll get back to our story in
a second. Navigating the professional job search is hard. You
(08:51):
know the perfect job is out there, you're just not
sure how to find it. The good news is you
don't have to go it alone. You need the Specialized
Recruiting Group. We're here to guide you and help you
find a job that fits all without costing a dime.
We're the Specialized Recruiting Group and Express Employment Professionals Company.
Go to s r G express dot com for free
(09:12):
support and get on the right course. Now back to
on the job. Who was next top? She got put
on a two year assignment at the White House in
two thousand fifteen. Under Obama, she served as the Executive
director of the white Houses National Science and Technology Council,
(09:34):
basically overseeing a massive think tank with tons of committees
and experts that convene and come up with plans for
how we as a country can use science and tech
to our benefit. There are a lot of experts throughout
the federal who have spent a lot of time and
energy educating and being educated on and getting smart on
(09:55):
and working on both policy and technology development on a
lot of different areas, and so be able to just
create an environment where you can convene true expertise and
then produce something that can really guide future investments in
future work is is pretty awesome. At this point, she
really wanted to get back to working with on the
ground technical projects, which is how she found herself at
(10:17):
Data Kind where she is now. It really allows me
the opportunity to combine my data knowledge, my technology knowledge,
and really passion for working directly with communities and working
directly with nonprofits. So now she's back to what she
started off wanting to do, using data science to make
people's lives easier, which is something we all use every
(10:39):
day and speaking personally, take for granted, you can yell
at Sirie to order you a pizza, and thirty minutes
later you get a pizza. Software engineers make that happen.
I appreciated a science sometimes for being able to log
in to whatever shopping platform I'm using at the time
and being suggested items that just happened to be what
(11:02):
I want to see and what I want to wear.
Customizis options presented to me and my life made easier
by data science that's run in the background. Yeah, I
mean I love that too, And yeah, yeah, the technology
is I don't even I don't get how it works.
It's it's like magic. It's crazy how advanced it is.
And then for that kind of tech to exist and
(11:26):
for you to be working on a project at Data Kind,
like making record systems more accessible for frontline workers, yeah,
like that's kind of crazy, right, Yeah. Yeah. One of
the Data Kind co founders used to say, I'm actually
not sure if it's his quote, I forgot it from
someone else, but he used to say, the future is here,
(11:47):
it's just not evenly distributed, which is something that really
resonates for me, because you know, so many times we
have these technology systems to your point, just we don't
even question them anymore, we don't even think about them.
But in some sectors it just hasn't been adopted yet.
(12:11):
Technology often mirrors what our priorities are as a society.
They can be inspiring and also pretty upsetting at the
same time, Like back in the nineteen sixty nine we
put humans on the Moon and at the same time
back on Earth. Cities all over the country didn't have
clean water systems or roads that work and still don't today.
I can yell at Syria to get me a pizza
(12:33):
in thirty minutes, and a frontline worker during COVID might
have to spend hours and hours on the phone tracking
down a patient's medical history so that they know what
they can or can't safely treat them with. You know,
I joined data kind because social sector organizations who are
out there saving lives, trying to give people access to healthcare,
(12:53):
give people access to housing, give people access to food,
don't always have the time to invest in data systems
or technology systems, and so ways that data kind can
really partner with those organizations to help them do your
mission more effectively, incredibly important work. Afoola joined data kind
right before the pandemic hit, and she was hearing from
(13:14):
organizations that we're using data kind systems about how much
they helped with the crisis. Organizations like Plentiful, an app
that makes it easier for individuals and families to get
food from food pantries. And so hearing that kind of
feedback from an organization like Plentiful or other organizations, you know,
they will say the work you did helps us save
(13:34):
you know, the time our drivers are out by or
something like that, and really refocus their efforts on again
executing on their mission, which is why people who joined
nonprofits join on profits. Is just really um that's just
really great to hear again a food is demeanor. I
(13:57):
feel like it doesn't match the gravity of the work
that she's done, but that's because she's really humble. From
IBM to the FBI, to the White House to her
work with data kind that could have massive implications around
the world. The work that she's doing and has done
is monumental and it's not flying under the radar. In
two thousand nineteen, she was asked to be an ambassador
(14:19):
for the if then Initiative, the tagline being if we
support a woman in STEM, then she can change the world.
About twenty women were selected from all different science, technology, engineering,
math professions and the goal of the if then initiative
is to highlight women in STEM so that girls get
(14:40):
excited about pursuing STEM career someday. One of the things
that the if then Initiative did was take three D
scans of all the ambassadors, and a week before our interview,
a FUA flew down to Dallas, Texas to see an exhibit.
They put up a grassy field filled with bright orange statues.
There are about a to life size statues of women
(15:02):
in stem, one of them being me, Um, you have
a statue. I do have a statue. Really flattering and
really humbling, um and really exciting. How did that feel
seeing a statue of yourself? Yeah, it was. It was
(15:23):
surreal walking around the statue exhibit looking for myself and
then just you know, seeing a life size me holding
my my laptop by my side was was pretty incredible.
I had the opportunity to see it for the first
time with my family, you know, watching my sisters take
us selfie with my statue also really surreal. Talking with
(15:53):
the Fua, you probably wouldn't guess this is a person
who has a statue made of her, not because she shouldn't,
but because we often dedicate statues to people who are
the face of a cause or at the front of
a historic event. And when we make statues of people,
it's because we want them to be idolized. They are
figures we choose to literally look up to. It makes
(16:17):
me pretty happy that as a society we're choosing more
and more to look up to people like a FUA, Smart, humble,
people working intelligently and diligently every day to make all
of our lives easier. People whose weapons are Excel and
power Point. It must have felt so cool to be
recognized in that way with a bunch of other people
(16:39):
who are behind the scenes, in a way that most
people would never know about them if they were orange
statues in Dallas, Texas. Right. I can't honestly say that
I never imagined saying myself as a statue. Having an
opportunity to be recognized, you know, certain selfishly makes me
(17:01):
feel great. But again, I'm really hoping that, you know,
someone will read the description of what I do and
the lintal plaque that's there next to my statue and think, oh,
I could also think of ways to use science and
technology to strengthening communities. That's great for this person who's
a statue, but that's something I could do too. For
(17:30):
On the Job, I'm Otis Gray. Thanks for listening to
On the Job, brought to you by Express Employment Professionals.
This season of On the Job is produced by Audiation.
The episodes were written and produced by me Otis Gray,
(17:51):
our executive producer is Sandy Smallens. The show was mixed
by Matt Noble for Audiation studios at The Loft and Bronxville,
New York. Music by Blue Dot Sessions. Find us on
I Heart Radio and Apple Podcasts. If you liked what
you heard, please consider rating and reviewing the show on
Apple Podcasts or wherever you listen. We'll see you next time.
(18:12):
For more inspiring stories about discovering your life's work, Audiention
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