1
00:00:06,240 --> 00:00:09,440
Speaker 1: This is on the Job, a podcast about finding your

2
00:00:09,440 --> 00:00:12,000
Speaker 1: life's work on the job, is brought to you by

3
00:00:12,000 --> 00:00:16,160
Speaker 1: Express Employment Professionals. This season, we're bringing you stories of

4
00:00:16,200 --> 00:00:19,119
Speaker 1: folks following their passion to carve their own career path.

5
00:00:21,200 --> 00:00:24,320
Speaker 1: In a digital age, software engineers and data scientists are

6
00:00:24,320 --> 00:00:27,600
Speaker 1: the unsung heroes that make all of our convenient technology possible,

7
00:00:27,960 --> 00:00:30,280
Speaker 1: and while they dedicate their careers to making all of

8
00:00:30,280 --> 00:00:33,600
Speaker 1: our lives easier, they often go unnoticed. Well, today we

9
00:00:33,720 --> 00:00:36,199
Speaker 1: talked to a standout techie whose love for science took

10
00:00:36,200 --> 00:00:39,560
Speaker 1: her from jobs and computing to government agencies to working

11
00:00:39,560 --> 00:00:42,240
Speaker 1: with nonprofits on the front lines of the pandemic today.

12
00:00:44,680 --> 00:00:48,160
Speaker 1: As someone who immediately thinks that rumbas and Siri will

13
00:00:48,200 --> 00:00:50,480
Speaker 1: take over the world at some point, he was pretty

14
00:00:50,520 --> 00:00:53,640
Speaker 1: reassuring to hear that our interviewee today is not un

15
00:00:53,680 --> 00:00:59,120
Speaker 1: teamed rob apocalypse. Absolutely. My goal is certainly to leverage

16
00:00:59,200 --> 00:01:03,360
Speaker 1: data science and AI in service of humanity. This is

17
00:01:03,400 --> 00:01:06,640
Speaker 1: a Fua, so Fua Bruce. I am the chief program

18
00:01:06,680 --> 00:01:09,640
Speaker 1: officer at Data Kind and I'm based just outside of

19
00:01:09,640 --> 00:01:13,440
Speaker 1: the DC area. A fua is a wildly talented software

20
00:01:13,440 --> 00:01:17,000
Speaker 1: engineer with a pretty fascinating work history. She's currently in

21
00:01:17,040 --> 00:01:20,760
Speaker 1: a more managerial role at data Kind, a nonprofit organization

22
00:01:20,920 --> 00:01:24,800
Speaker 1: that connects other organizations in the social sector with technology

23
00:01:24,959 --> 00:01:28,240
Speaker 1: and scientists that can help them do their jobs more efficiently.

24
00:01:28,360 --> 00:01:31,440
Speaker 1: So Data Kind has been partnering with a couple of

25
00:01:31,480 --> 00:01:35,000
Speaker 1: different organizations and a couple of different African countries on

26
00:01:35,480 --> 00:01:39,679
Speaker 1: frontline health systems. Frontline health systems basically, you know when

27
00:01:39,680 --> 00:01:41,440
Speaker 1: you go to the doctor and you've got to fill

28
00:01:41,440 --> 00:01:44,760
Speaker 1: out tons and tons of information every time, explaining your

29
00:01:44,800 --> 00:01:48,200
Speaker 1: medical history or past doctors you've seen, Well, that process

30
00:01:48,280 --> 00:01:51,520
Speaker 1: really bogs down frontline health workers. At least over fifty

31
00:01:51,520 --> 00:01:55,560
Speaker 1: percent of a frontline health workers time is spent accessing

32
00:01:55,560 --> 00:01:59,200
Speaker 1: in updating patient records instead of caring for patients. And

33
00:01:59,240 --> 00:02:01,120
Speaker 1: so if you can look how do we improve the

34
00:02:01,200 --> 00:02:04,600
Speaker 1: data integrity of these systems, you can then let frontline

35
00:02:04,600 --> 00:02:06,440
Speaker 1: health workers do the things they want to do, which

36
00:02:06,520 --> 00:02:08,519
Speaker 1: is to care for patients and the things they're most

37
00:02:08,560 --> 00:02:12,520
Speaker 1: trained for. Right So, this is why a lot of

38
00:02:12,600 --> 00:02:16,840
Speaker 1: Data Kainan's work has been in I mean, your job

39
00:02:16,919 --> 00:02:20,480
Speaker 1: essentially right now is to make other people's jobs easier. Yeah,

40
00:02:20,680 --> 00:02:23,000
Speaker 1: you could sum it up that way. That's the goal

41
00:02:28,120 --> 00:02:30,840
Speaker 1: right now. Her projects are in a few African countries

42
00:02:30,840 --> 00:02:34,359
Speaker 1: and in India. Basically, she's making the records that doctors

43
00:02:34,360 --> 00:02:37,920
Speaker 1: and nurses and volunteers need more accessible. The fact that

44
00:02:38,000 --> 00:02:41,280
Speaker 1: frontline workers spend over fifty percent of their time on

45
00:02:41,280 --> 00:02:44,600
Speaker 1: this stuff was really surprising to me. I gotta believe

46
00:02:44,720 --> 00:02:46,640
Speaker 1: that it's not what a lot of doctors are thinking

47
00:02:46,639 --> 00:02:48,720
Speaker 1: about when they make the huge commitment to go to

48
00:02:48,760 --> 00:02:51,480
Speaker 1: medical school. I would say it's probably not high on

49
00:02:51,520 --> 00:02:54,760
Speaker 1: the priority list. Really making sure you understand how to

50
00:02:55,040 --> 00:02:59,440
Speaker 1: fill out different technical systems, how you enter data and systems,

51
00:02:59,480 --> 00:03:02,520
Speaker 1: and how track that data. I think people often go

52
00:03:02,560 --> 00:03:06,399
Speaker 1: to medical school to be able to practice medicine. These

53
00:03:06,400 --> 00:03:08,640
Speaker 1: projects are only a couple of years old, so she's

54
00:03:08,639 --> 00:03:10,440
Speaker 1: getting to see them work in the field for the

55
00:03:10,520 --> 00:03:14,079
Speaker 1: first time right now, but already organizations that use their

56
00:03:14,120 --> 00:03:17,679
Speaker 1: data systems and software have seen it drastically shave off

57
00:03:17,680 --> 00:03:21,080
Speaker 1: the time workers are spending looking through those records, so

58
00:03:21,200 --> 00:03:23,680
Speaker 1: you can take people who are doing that apply them

59
00:03:23,720 --> 00:03:26,600
Speaker 1: to other things that need to be done instead. It

60
00:03:26,639 --> 00:03:29,000
Speaker 1: also means that people who are looking at that data

61
00:03:29,040 --> 00:03:33,079
Speaker 1: to make decisions about what type of care is best,

62
00:03:33,200 --> 00:03:36,200
Speaker 1: what type of care should we recommend here, What are

63
00:03:36,200 --> 00:03:38,760
Speaker 1: trends that we're seeing here, What should we be changing

64
00:03:38,760 --> 00:03:42,720
Speaker 1: and how should we be reacting based on accurate data

65
00:03:42,840 --> 00:03:46,480
Speaker 1: of what actually happened. So in the way that the

66
00:03:46,520 --> 00:03:49,760
Speaker 1: best technology knows what you need and provides it for you,

67
00:03:50,080 --> 00:03:52,600
Speaker 1: maybe even before you know you need it. Data kind

68
00:03:52,680 --> 00:03:56,640
Speaker 1: systems are meant to be fluid, intelligent tools that substantially

69
00:03:56,680 --> 00:03:59,400
Speaker 1: cut down on the menial tasks that frontline workers are

70
00:03:59,440 --> 00:04:02,080
Speaker 1: currently with. And so this means that people can go

71
00:04:02,160 --> 00:04:04,920
Speaker 1: back to providing care to the people who need care

72
00:04:05,040 --> 00:04:07,640
Speaker 1: rather than looking at the streams of data that are

73
00:04:07,760 --> 00:04:17,240
Speaker 1: that's coming in. A fool plays it cool, but her

74
00:04:17,279 --> 00:04:21,680
Speaker 1: work has massive implications that are directly aimed at how

75
00:04:21,720 --> 00:04:24,719
Speaker 1: our health system works all over the world. It's huge.

76
00:04:25,160 --> 00:04:27,520
Speaker 1: But to back up, it all started off when she

77
00:04:27,600 --> 00:04:30,479
Speaker 1: was a young kid who just like numbers. Growing out,

78
00:04:30,520 --> 00:04:35,000
Speaker 1: my favorite subject was always math. I always really loved computers,

79
00:04:35,279 --> 00:04:38,400
Speaker 1: love playing games and computers. I love video games. Remember

80
00:04:38,400 --> 00:04:42,400
Speaker 1: my dad buying me remote control cars and figuring out

81
00:04:42,520 --> 00:04:45,039
Speaker 1: those were a lot of fun. She moved around a

82
00:04:45,040 --> 00:04:47,520
Speaker 1: lot as a kid. She lived in about nine states.

83
00:04:47,520 --> 00:04:50,080
Speaker 1: She was into dance and tennis, but even at a

84
00:04:50,160 --> 00:04:53,040
Speaker 1: young age, she was really into volunteering and did a

85
00:04:53,040 --> 00:04:55,320
Speaker 1: lot of social work, and now makes sense to be

86
00:04:55,440 --> 00:04:58,039
Speaker 1: on this side of it that I would find myself

87
00:04:58,080 --> 00:05:00,960
Speaker 1: in a career that let's you figure out how to

88
00:05:01,040 --> 00:05:05,840
Speaker 1: use technology to support communities. Who was a first generation American.

89
00:05:06,200 --> 00:05:08,599
Speaker 1: Both her parents are from Ghana, and the mentality in

90
00:05:08,640 --> 00:05:11,560
Speaker 1: her household growing up instill the importance of helping out

91
00:05:11,560 --> 00:05:15,159
Speaker 1: early on. Just I think the focus on community and

92
00:05:15,200 --> 00:05:17,920
Speaker 1: the focus on the people around you and really making

93
00:05:17,920 --> 00:05:20,359
Speaker 1: sure that you're teaming care of not just yourself, which

94
00:05:20,560 --> 00:05:23,800
Speaker 1: is important, but also you're meeting needs that you can

95
00:05:24,360 --> 00:05:27,880
Speaker 1: where possible. She went out to study computer engineering at

96
00:05:27,920 --> 00:05:30,960
Speaker 1: Purdue in Indiana, and while she was there, she started

97
00:05:30,960 --> 00:05:34,480
Speaker 1: working for IBM as a student. IBM hired her full

98
00:05:34,480 --> 00:05:36,920
Speaker 1: time as a software engineer right out of school, and

99
00:05:37,000 --> 00:05:40,880
Speaker 1: she loved it. I really liked being an engineer. I

100
00:05:40,920 --> 00:05:45,039
Speaker 1: really liked toting. I really liked the different the software

101
00:05:45,080 --> 00:05:47,359
Speaker 1: that we were building work on some of the IBM's

102
00:05:47,400 --> 00:05:52,240
Speaker 1: LURCH servers. I enjoyed IBM's culture. I really enjoyed the work.

103
00:05:53,160 --> 00:05:56,240
Speaker 1: Already having an impressive start to her career, she actually

104
00:05:56,320 --> 00:05:59,320
Speaker 1: decided to take a leave of absence from IBM going

105
00:05:59,360 --> 00:06:02,720
Speaker 1: back to school at University of Michigan for an MBA,

106
00:06:02,800 --> 00:06:05,039
Speaker 1: and she fully planned on going back to IBM with

107
00:06:05,120 --> 00:06:08,400
Speaker 1: her newly acquired business degree, but while she was on campus,

108
00:06:08,640 --> 00:06:15,559
Speaker 1: she got recruited as a special advisor for the FBI. Yeah,

109
00:06:16,000 --> 00:06:21,719
Speaker 1: and so I made an unexpected to everyone shift from

110
00:06:21,760 --> 00:06:24,719
Speaker 1: IBM to the FBI. And that was really the start

111
00:06:25,160 --> 00:06:28,160
Speaker 1: for myself of really seeing how my text goals could

112
00:06:28,160 --> 00:06:31,440
Speaker 1: really think more focused in the public interest, working in

113
00:06:31,480 --> 00:06:37,839
Speaker 1: government and working in the nonprofit sector. So when people

114
00:06:37,920 --> 00:06:40,400
Speaker 1: asked you what you did at that point and you said,

115
00:06:40,640 --> 00:06:44,360
Speaker 1: I worked for the FBI, it absolutely felt great to

116
00:06:44,360 --> 00:06:46,400
Speaker 1: say that I worked for the FBI. I really, I

117
00:06:46,440 --> 00:06:49,120
Speaker 1: really did really cool to say. It was definitely cool

118
00:06:49,160 --> 00:06:52,000
Speaker 1: to just say my name as a Foo Bruce and

119
00:06:52,000 --> 00:06:57,080
Speaker 1: I'm here representing the FBI. She worked a lot of

120
00:06:57,120 --> 00:07:01,559
Speaker 1: strategy and program management positions there the Science and Technology branch,

121
00:07:01,800 --> 00:07:03,080
Speaker 1: and she got to work on a lot of the

122
00:07:03,080 --> 00:07:06,040
Speaker 1: tech that the FBI uses. Really got to see how

123
00:07:06,160 --> 00:07:09,039
Speaker 1: tech support, what agents do? What do you mean like

124
00:07:09,279 --> 00:07:12,640
Speaker 1: Q James Bond kind of stuff. Yes, in facts, my

125
00:07:12,760 --> 00:07:15,840
Speaker 1: boss at the time would describe the division as Q.

126
00:07:16,120 --> 00:07:19,760
Speaker 1: That's where all the fun, toys were built and tested,

127
00:07:20,480 --> 00:07:23,880
Speaker 1: and so it's really it's really cool. Did you get to,

128
00:07:23,960 --> 00:07:27,400
Speaker 1: like work on stuff that you can't talk about? I

129
00:07:27,440 --> 00:07:30,960
Speaker 1: think inherently part of what the FBI does things that

130
00:07:31,040 --> 00:07:35,000
Speaker 1: you can't talk about. However, she did get to see

131
00:07:35,040 --> 00:07:37,320
Speaker 1: some of the stuff she was working on get notoriety

132
00:07:37,360 --> 00:07:40,160
Speaker 1: in upper management and even on the news, things like

133
00:07:40,240 --> 00:07:44,360
Speaker 1: her work on forensics technology, improving fingerprint tech and systems

134
00:07:44,360 --> 00:07:48,200
Speaker 1: for gun background checks. It was certainly easy to feel

135
00:07:48,240 --> 00:07:51,880
Speaker 1: a part of something bigger when you know that a

136
00:07:51,960 --> 00:07:54,600
Speaker 1: lot of the work that you do really matters. It

137
00:07:54,640 --> 00:07:57,120
Speaker 1: was important to keep Americans safe, it was important to

138
00:07:57,200 --> 00:08:00,160
Speaker 1: keep community safe. It was important to keep and I

139
00:08:00,200 --> 00:08:03,760
Speaker 1: say that it was important to keep children safe just

140
00:08:03,760 --> 00:08:05,880
Speaker 1: goes to show you we can be a cool government

141
00:08:05,880 --> 00:08:07,880
Speaker 1: crime fighter even if you don't carry a badge and

142
00:08:07,880 --> 00:08:09,680
Speaker 1: a gun. People used to ask a food if she

143
00:08:09,760 --> 00:08:12,160
Speaker 1: carried one all the time. And I always quote one

144
00:08:12,200 --> 00:08:15,000
Speaker 1: of my good colleagues in a fellow special advisor. He

145
00:08:15,120 --> 00:08:18,720
Speaker 1: used to say that our weapons will excel in PowerPoint.

146
00:08:21,560 --> 00:08:24,840
Speaker 1: So well, the job does sound very exciting. Sometimes it

147
00:08:24,880 --> 00:08:28,680
Speaker 1: really does boil down to simple tools into a simple

148
00:08:28,760 --> 00:08:31,280
Speaker 1: understanding and really figuring out how you can leverage your

149
00:08:31,280 --> 00:08:39,719
Speaker 1: skill set to really help move a mission forward. That's

150
00:08:39,760 --> 00:08:45,880
Speaker 1: exactly what an undercover agent would say. We'll get back

151
00:08:45,880 --> 00:08:50,400
Speaker 1: to our story in a second. Navigating the professional job

152
00:08:50,440 --> 00:08:53,280
Speaker 1: search is hard. You know the perfect job is out there,

153
00:08:53,440 --> 00:08:55,880
Speaker 1: you're just not sure how to find it. The good

154
00:08:55,880 --> 00:08:58,520
Speaker 1: news is you don't have to go it alone. You

155
00:08:58,559 --> 00:09:01,840
Speaker 1: need the Specialized Recruiting Group. We're here to guide you

156
00:09:02,080 --> 00:09:04,880
Speaker 1: and help you find a job that fits all without

157
00:09:04,880 --> 00:09:08,760
Speaker 1: costing a dime. We're the Specialized Recruiting Group and Express

158
00:09:08,800 --> 00:09:12,480
Speaker 1: Employment Professionals Company. Go to SRG express dot com for

159
00:09:12,600 --> 00:09:21,320
Speaker 1: free support and get on the right course. Now back

160
00:09:21,360 --> 00:09:24,520
Speaker 1: to on the job of who was next stop. She

161
00:09:24,559 --> 00:09:26,720
Speaker 1: got put on a two year assignment at the White

162
00:09:26,720 --> 00:09:29,920
Speaker 1: House in twenty fifteen. Under Obama, she served as the

163
00:09:29,960 --> 00:09:33,840
Speaker 1: executive director of the White House's National Science and Technology Council,

164
00:09:34,160 --> 00:09:38,160
Speaker 1: basically overseeing a massive think tank with tons of committees

165
00:09:38,160 --> 00:09:41,160
Speaker 1: and experts that convene and come up with plans for

166
00:09:41,400 --> 00:09:43,800
Speaker 1: how we as a country can use science and tech

167
00:09:43,840 --> 00:09:47,440
Speaker 1: to our benefit. There are a lot of experts throughout

168
00:09:47,440 --> 00:09:50,160
Speaker 1: the federal govermm who have spent a lot of time

169
00:09:50,200 --> 00:09:54,760
Speaker 1: and energy educating and being educated on and getting smart

170
00:09:54,800 --> 00:09:57,960
Speaker 1: on and working on both policy and technology development on

171
00:09:58,000 --> 00:10:00,360
Speaker 1: a lot of different areas, and so be able to

172
00:10:00,440 --> 00:10:03,959
Speaker 1: just create an environment where you can convene true expertise

173
00:10:04,360 --> 00:10:08,040
Speaker 1: and then produce something that can really guide future investments

174
00:10:08,040 --> 00:10:11,319
Speaker 1: in future work is is pretty awesome. At this point,

175
00:10:11,440 --> 00:10:13,719
Speaker 1: she really wanted to get back to working with on

176
00:10:13,760 --> 00:10:16,880
Speaker 1: the ground technical projects, which is how she found herself

177
00:10:16,920 --> 00:10:20,359
Speaker 1: at Data Kind where she is now. It really allows

178
00:10:20,400 --> 00:10:24,679
Speaker 1: me the opportunity to combine my data knowledge, my technology knowledge,

179
00:10:24,760 --> 00:10:29,240
Speaker 1: and really passion for working directly with communities and working

180
00:10:29,400 --> 00:10:32,880
Speaker 1: directly with nonprofits. So now she's back to what she

181
00:10:32,960 --> 00:10:36,040
Speaker 1: started off wanting to do, using data science to make

182
00:10:36,080 --> 00:10:39,400
Speaker 1: people's lives easier, which is something we all use every

183
00:10:39,480 --> 00:10:43,600
Speaker 1: day and speaking personally, take for granted, you can yell

184
00:10:43,679 --> 00:10:46,480
Speaker 1: at Siri to order you a pizza, and thirty minutes

185
00:10:46,559 --> 00:10:50,240
Speaker 1: later you get a pizza. Software engineers make that happen.

186
00:10:50,640 --> 00:10:54,600
Speaker 1: I appreciate data science sometimes for being able to log

187
00:10:54,679 --> 00:10:58,720
Speaker 1: in to whatever shopping platform I'm using at the time

188
00:10:58,800 --> 00:11:02,520
Speaker 1: and being suggested items that just happened to be what

189
00:11:02,679 --> 00:11:04,800
Speaker 1: I want to see and what I want to Where

190
00:11:05,280 --> 00:11:09,680
Speaker 1: customizis options presented to me and my life made easier

191
00:11:09,880 --> 00:11:12,800
Speaker 1: by data science that's run in the background. Yeah, I

192
00:11:12,840 --> 00:11:16,120
Speaker 1: mean I love that too, and yeah, yeah, the technology

193
00:11:16,440 --> 00:11:19,680
Speaker 1: is I don't even I don't get how it works.

194
00:11:19,440 --> 00:11:22,160
Speaker 1: It's it's like magic. It's crazy how advanced it is.

195
00:11:22,240 --> 00:11:26,400
Speaker 1: And then for that kind of tech to exist and

196
00:11:26,720 --> 00:11:30,360
Speaker 1: for you to be working on a project at Data Kind,

197
00:11:30,520 --> 00:11:36,160
Speaker 1: like making record systems more accessible for frontline workers, yeah,

198
00:11:36,520 --> 00:11:39,559
Speaker 1: like that's kind of crazy, right, Yeah. Yeah. One of

199
00:11:39,559 --> 00:11:42,439
Speaker 1: the Data Kind co founders used to say, I'm actually

200
00:11:42,480 --> 00:11:43,760
Speaker 1: not sure if it's his quote up to I forgot

201
00:11:43,760 --> 00:11:45,679
Speaker 1: it from someone else, but he used to say, the

202
00:11:45,720 --> 00:11:51,480
Speaker 1: future is here, it's just not evenly distributed, which is

203
00:11:51,480 --> 00:11:53,800
Speaker 1: something that really resonates for me, because you know, so

204
00:11:53,880 --> 00:11:56,800
Speaker 1: many times we have these technology systems to your point,

205
00:11:56,960 --> 00:11:59,520
Speaker 1: just we don't even question them anymore, we don't even

206
00:11:59,600 --> 00:12:02,959
Speaker 1: think about them. But in some sectors it just hasn't

207
00:12:02,960 --> 00:12:14,240
Speaker 1: been adopted yet. Technology often mirrors what our priorities are

208
00:12:14,280 --> 00:12:17,760
Speaker 1: as a society. They can be inspiring and also pretty

209
00:12:17,840 --> 00:12:20,520
Speaker 1: upsetting at the same time, Like back in the nineteen

210
00:12:20,640 --> 00:12:23,880
Speaker 1: sixty nine we put humans on the Moon and at

211
00:12:23,880 --> 00:12:26,520
Speaker 1: the same time back on Earth, cities all over the

212
00:12:26,559 --> 00:12:29,640
Speaker 1: country didn't have clean water systems or roads that work

213
00:12:29,880 --> 00:12:33,040
Speaker 1: and still don't today. I can yell at Syria to

214
00:12:33,040 --> 00:12:35,640
Speaker 1: get me a pizza in thirty minutes, and a frontline

215
00:12:35,679 --> 00:12:38,959
Speaker 1: worker during COVID might have to spend hours and hours

216
00:12:39,000 --> 00:12:42,400
Speaker 1: on the phone tracking down a patient's medical history so

217
00:12:42,440 --> 00:12:44,880
Speaker 1: that they know what they can or can't safely treat

218
00:12:44,920 --> 00:12:49,199
Speaker 1: them with. You know, I joined data kind because social

219
00:12:49,200 --> 00:12:52,480
Speaker 1: sector organizations who are out there saving lives, trying to

220
00:12:52,520 --> 00:12:55,200
Speaker 1: give people access to healthcare, give people access to housing,

221
00:12:55,240 --> 00:12:57,800
Speaker 1: give people access to food, don't always have the time

222
00:12:57,880 --> 00:13:01,280
Speaker 1: to invest in data systems or technology systems, and so

223
00:13:01,400 --> 00:13:04,600
Speaker 1: ways that data kind can really partner with those organizations

224
00:13:04,600 --> 00:13:08,360
Speaker 1: to help them do your mission more effectively incredibly important work.

225
00:13:10,600 --> 00:13:13,439
Speaker 1: A fool joined data kind right before the pandemic hit,

226
00:13:13,679 --> 00:13:16,240
Speaker 1: and she was hearing from organizations that were using data

227
00:13:16,320 --> 00:13:19,400
Speaker 1: kind systems about how much they helped with the crisis.

228
00:13:19,920 --> 00:13:22,840
Speaker 1: Organizations like Plentiful, an app that makes it easier for

229
00:13:22,880 --> 00:13:26,560
Speaker 1: individuals and families to get food from food pantries, and

230
00:13:26,600 --> 00:13:29,480
Speaker 1: so hearing that kind of feedback from an organization like

231
00:13:29,520 --> 00:13:32,840
Speaker 1: Plentiful or other organizations you know that will say the

232
00:13:32,840 --> 00:13:35,280
Speaker 1: work you did helps us save you know, the time

233
00:13:35,320 --> 00:13:38,800
Speaker 1: our drivers are out by fifteen percent or something like that,

234
00:13:38,920 --> 00:13:42,360
Speaker 1: and really refocus their efforts on again actually huting on

235
00:13:42,400 --> 00:13:44,760
Speaker 1: their mission, which is why people who joined nonprofits join

236
00:13:44,800 --> 00:13:48,640
Speaker 1: on profits. Is just really that's just really great to

237
00:13:48,679 --> 00:13:57,880
Speaker 1: hear again a foo is demeanor. I feel like it

238
00:13:57,920 --> 00:14:00,640
Speaker 1: doesn't match the gravity of the work that she's done,

239
00:14:00,880 --> 00:14:04,320
Speaker 1: but that's because she's really humble. From IBM to the FBI,

240
00:14:04,440 --> 00:14:06,800
Speaker 1: to the White House to her work with data kind

241
00:14:06,880 --> 00:14:10,240
Speaker 1: that could have massive implications around the world. The work

242
00:14:10,280 --> 00:14:14,600
Speaker 1: that she's doing and has done is monumental and it's

243
00:14:14,679 --> 00:14:18,079
Speaker 1: not flying under the radar. In twenty nineteen, she was

244
00:14:18,120 --> 00:14:20,800
Speaker 1: asked to be an ambassador for the if then Initiative,

245
00:14:21,120 --> 00:14:24,200
Speaker 1: the tagline being if we support a woman in STEM,

246
00:14:24,240 --> 00:14:27,640
Speaker 1: then she can change the world. About one hundred and

247
00:14:27,640 --> 00:14:32,360
Speaker 1: twenty women were selected from all different science, technology, engineering,

248
00:14:32,440 --> 00:14:36,760
Speaker 1: math professions and the goal of the if then initiative

249
00:14:36,920 --> 00:14:40,920
Speaker 1: is to highlight women in STEM so that girls get

250
00:14:40,920 --> 00:14:45,560
Speaker 1: excited about pursuing STEM career someday. One of the things

251
00:14:45,560 --> 00:14:47,800
Speaker 1: that the if then initiative did was take three D

252
00:14:47,920 --> 00:14:51,320
Speaker 1: scans of all the ambassadors and a week before our interview,

253
00:14:51,560 --> 00:14:54,520
Speaker 1: a FUA flew down to Dallas, Texas to see an exhibit.

254
00:14:54,520 --> 00:14:58,000
Speaker 1: They put up a grassy field filled with bright orange statues.

255
00:14:58,480 --> 00:15:01,360
Speaker 1: There are about one hundred and twenty to life size

256
00:15:01,400 --> 00:15:04,480
Speaker 1: statues of women in stem, one of them being me.

257
00:15:06,320 --> 00:15:10,440
Speaker 1: You have a statue. I do have a statue. Really

258
00:15:10,440 --> 00:15:18,120
Speaker 1: flattering and really humbling and really exciting. How did that

259
00:15:18,200 --> 00:15:22,840
Speaker 1: feel seeing a statue of yourself? Yeah, it was. It

260
00:15:22,880 --> 00:15:28,160
Speaker 1: was surreal walking around the statue exhibit looking for myself

261
00:15:28,160 --> 00:15:31,080
Speaker 1: and then just you know, seeing a life size me

262
00:15:31,640 --> 00:15:36,120
Speaker 1: holding my laptop by my side was pretty incredible. I

263
00:15:36,120 --> 00:15:39,080
Speaker 1: had the opportunity to see it for the first time

264
00:15:39,200 --> 00:15:42,080
Speaker 1: with my family, you know, watching my sisters take us

265
00:15:42,080 --> 00:15:54,120
Speaker 1: selfie with my statue also really surreal. Talking with the FUA,

266
00:15:54,360 --> 00:15:57,240
Speaker 1: you probably wouldn't guess this is a person who has

267
00:15:57,280 --> 00:16:00,440
Speaker 1: a statue made of her, not because she shouldn't, but

268
00:16:00,520 --> 00:16:04,360
Speaker 1: because we often dedicate statues to people who are the

269
00:16:04,400 --> 00:16:06,600
Speaker 1: face of a cause or at the front of a

270
00:16:06,640 --> 00:16:10,040
Speaker 1: historic event. And when we make statues of people, it's

271
00:16:10,080 --> 00:16:13,359
Speaker 1: because we want them to be idolized. They are figures

272
00:16:13,400 --> 00:16:18,040
Speaker 1: we choose to literally look up to. It makes me

273
00:16:18,080 --> 00:16:21,400
Speaker 1: pretty happy that as a society, we're choosing more and

274
00:16:21,480 --> 00:16:25,480
Speaker 1: more to look up to people like Afua, Smart, humble

275
00:16:25,520 --> 00:16:29,560
Speaker 1: people working intelligently and diligently every day to make all

276
00:16:29,600 --> 00:16:33,600
Speaker 1: of our lives easier. People whose weapons are Excel and PowerPoint.

277
00:16:34,040 --> 00:16:37,240
Speaker 1: It must have felt so cool to be recognized in

278
00:16:37,280 --> 00:16:40,680
Speaker 1: that way with a bunch of other people who are

279
00:16:40,720 --> 00:16:43,160
Speaker 1: behind the scenes, in a way that most people would

280
00:16:43,240 --> 00:16:47,280
Speaker 1: never know about them if they weren't orange statues in Dallas, Texas. Right.

281
00:16:48,280 --> 00:16:54,240
Speaker 1: I can't honestly say that I never imagine saying myself

282
00:16:54,280 --> 00:16:59,920
Speaker 1: as a statue. Having an opportunity to be recognized certain

283
00:17:00,320 --> 00:17:04,760
Speaker 1: selfishly makes me feel great. But again, I'm really hoping that,

284
00:17:04,840 --> 00:17:07,840
Speaker 1: you know, someone will read the description of what I do.

285
00:17:07,960 --> 00:17:11,520
Speaker 1: I'm a little plaque that's there next to my statue

286
00:17:11,520 --> 00:17:14,040
Speaker 1: and think, oh, I could also think of ways to

287
00:17:14,119 --> 00:17:18,000
Speaker 1: use science and technology to strengthen communities. That's great for

288
00:17:18,040 --> 00:17:20,840
Speaker 1: this person who's a statue, but it's something I could

289
00:17:20,840 --> 00:17:40,920
Speaker 1: do too. For On the Job, I'm Otis Gray. Thanks

290
00:17:40,920 --> 00:17:43,040
Speaker 1: for listening to On the Job, brought to you by

291
00:17:43,040 --> 00:17:46,960
Speaker 1: Express Employment Professionals. This season of On the Job is

292
00:17:47,000 --> 00:17:50,400
Speaker 1: produced by Audiation. The episodes were written and produced by

293
00:17:50,440 --> 00:17:54,439
Speaker 1: me Otis Gray. Our executive producer is Sandy Smallens. The

294
00:17:54,480 --> 00:17:57,359
Speaker 1: show is mixed by Matt Noble for Audiation studios at

295
00:17:57,400 --> 00:18:00,880
Speaker 1: The Loft and Bronxville, New York. Music by Blue Dot Sessions.

296
00:18:01,760 --> 00:18:05,840
Speaker 1: Find us on iHeartRadio and Apple Podcasts. If you liked

297
00:18:05,840 --> 00:18:08,440
Speaker 1: what you heard, please consider rating and reviewing the show

298
00:18:08,480 --> 00:18:12,159
Speaker 1: on Apple Podcasts or wherever you listen. We'll see you

299
00:18:12,200 --> 00:18:15,640
Speaker 1: next time. For more inspiring stories about discovering your life's work,

300
00:18:20,480 --> 00:18:21,120
Speaker 1: Audiation