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my<00:00:12.840><c> name</c><00:00:13.019><c> is</c><00:00:13.139><c> Evie</c><00:00:13.469><c> um</c><00:00:13.559><c> Kim</c><00:00:13.830><c> I'm</c><00:00:14.070><c> leave</c><00:00:14.519><c> machine</c>

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my name is Evie um Kim I'm leave machine
 

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my name is Evie um Kim I'm leave machine
learning<00:00:15.150><c> engineer</c><00:00:15.540><c> at</c><00:00:15.690><c> Matt</c><00:00:15.900><c> Oberg</c><00:00:22.760><c> so</c><00:00:23.760><c> the</c>

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learning engineer at Matt Oberg so the
 

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learning engineer at Matt Oberg so the
interaction<00:00:25.740><c> was</c><00:00:25.859><c> really</c><00:00:26.220><c> good</c><00:00:26.400><c> so</c><00:00:27.029><c> I've</c><00:00:27.180><c> been</c>

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interaction was really good so I've been
 

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interaction was really good so I've been
to<00:00:27.660><c> some</c><00:00:28.050><c> other</c><00:00:28.230><c> talks</c><00:00:28.619><c> as</c><00:00:29.039><c> well</c><00:00:29.250><c> but</c><00:00:30.439><c> the</c>

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to some other talks as well but the
 

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to some other talks as well but the
people's<00:00:31.920><c> willingness</c><00:00:32.160><c> to</c><00:00:32.790><c> like</c><00:00:33.059><c> know</c><00:00:33.270><c> about</c>

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people's willingness to like know about
 

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people's willingness to like know about
what<00:00:34.590><c> I'm</c><00:00:34.860><c> talking</c><00:00:35.309><c> about</c><00:00:35.370><c> that</c><00:00:35.940><c> that</c>

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what I'm talking about that that
 

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what I'm talking about that that
enthusiasm<00:00:36.960><c> was</c><00:00:37.320><c> like</c><00:00:37.530><c> much</c><00:00:38.280><c> higher</c><00:00:38.760><c> than</c><00:00:39.149><c> the</c>

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enthusiasm was like much higher than the
 

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enthusiasm was like much higher than the
average<00:00:39.300><c> I</c><00:00:39.719><c> would</c><00:00:39.870><c> have</c><00:00:40.050><c> expected</c><00:00:40.500><c> I</c><00:00:41.100><c> got</c><00:00:41.340><c> a</c>

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average I would have expected I got a
 

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average I would have expected I got a
lot<00:00:41.520><c> of</c><00:00:41.550><c> questions</c><00:00:42.120><c> and</c><00:00:42.420><c> a</c><00:00:42.600><c> lot</c><00:00:42.629><c> of</c><00:00:43.110><c> follow-up</c>

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lot of questions and a lot of follow-up
 

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lot of questions and a lot of follow-up
questions<00:00:44.040><c> after</c><00:00:44.460><c> like</c><00:00:44.910><c> my</c><00:00:45.390><c> tough</c><00:00:45.660><c> so</c><00:00:45.930><c> I</c><00:00:45.989><c> had</c>

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questions after like my tough so I had
 

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questions after like my tough so I had
hard<00:00:46.649><c> time</c><00:00:46.829><c> coming</c><00:00:47.129><c> back</c><00:00:47.280><c> up</c><00:00:47.430><c> here</c><00:00:47.460><c> because</c>

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hard time coming back up here because
 

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hard time coming back up here because
there<00:00:48.000><c> many</c><00:00:48.660><c> people</c><00:00:48.960><c> like</c><00:00:49.110><c> asking</c><00:00:49.440><c> customs</c>

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there many people like asking customs
 

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there many people like asking customs
and<00:00:49.890><c> stuff</c><00:00:49.980><c> so</c><00:00:50.340><c> I</c><00:00:50.550><c> like</c><00:00:51.090><c> that</c><00:00:51.300><c> level</c><00:00:51.719><c> of</c>

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and stuff so I like that level of
 

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and stuff so I like that level of
enthusiasm<00:00:56.719><c> data</c><00:00:57.719><c> is</c><00:00:57.870><c> so</c><00:00:58.050><c> cool</c><00:00:58.289><c> and</c><00:00:58.410><c> exciting</c>

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enthusiasm data is so cool and exciting
 

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enthusiasm data is so cool and exciting
because<00:00:59.010><c> the</c><00:00:59.789><c> fact</c><00:01:00.059><c> that</c><00:01:00.210><c> we</c><00:01:00.480><c> are</c><00:01:00.510><c> using</c><00:01:00.780><c> the</c>

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because the fact that we are using the
 

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because the fact that we are using the
data<00:01:01.260><c> and</c><00:01:01.590><c> trying</c><00:01:02.070><c> to</c><00:01:02.340><c> coming</c><00:01:02.760><c> up</c><00:01:02.940><c> with</c><00:01:03.149><c> the</c>

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data and trying to coming up with the
 

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data and trying to coming up with the
better<00:01:04.229><c> future</c><00:01:04.739><c> is</c><00:01:04.769><c> like</c><00:01:05.309><c> not</c><00:01:05.549><c> polluting</c><00:01:06.299><c> it</c>

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better future is like not polluting it
 

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better future is like not polluting it
has<00:01:06.869><c> been</c><00:01:07.080><c> like</c><00:01:07.350><c> that</c><00:01:07.590><c> for</c><00:01:07.890><c> like</c><00:01:08.100><c> centuries</c>

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has been like that for like centuries
 

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has been like that for like centuries
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maybe like even from the origin of
 

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maybe like even from the origin of
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species that has been the case because
 

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species that has been the case because
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we learned for something from the our
 

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we learned for something from the our
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past history and trying to apply to the
 

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past history and trying to apply to the
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features that that's the natural thing
 

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features that that's the natural thing
but<00:01:20.400><c> it</c><00:01:21.030><c> can</c><00:01:21.780><c> be</c><00:01:21.990><c> done</c><00:01:22.259><c> in</c><00:01:22.530><c> much</c><00:01:22.890><c> better</c><00:01:23.310><c> faster</c>

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but it can be done in much better faster
 

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but it can be done in much better faster
scalable<00:01:25.170><c> way</c><00:01:25.439><c> these</c><00:01:26.159><c> days</c><00:01:26.659><c> thanks</c><00:01:27.659><c> to</c><00:01:27.869><c> all</c>

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scalable way these days thanks to all
 

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scalable way these days thanks to all
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the devices we have you can collect more
 

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the devices we have you can collect more
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data we can have better understanding
 

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data we can have better understanding
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and we can apply it for the creating the
 

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and we can apply it for the creating the
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better future in much massive scale with
 

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better future in much massive scale with
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better accuracy so things that we have
 

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better accuracy so things that we have
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been doing so far can be done in much
 

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been doing so far can be done in much
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better ways that to accept what's
 

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better ways that to accept what's
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exciting about data
 

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exciting about data
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so<00:01:55.170><c> the</c><00:01:56.170><c> part</c><00:01:56.950><c> that</c><00:01:57.130><c> I</c><00:01:57.220><c> wanted</c><00:01:57.610><c> the</c><00:01:58.329><c> part</c><00:01:58.689><c> that</c>

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so the part that I wanted the part that
 

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so the part that I wanted the part that
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I want to emphasize from my talk is that
 

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I want to emphasize from my talk is that
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so there are a lot of talks about the
 

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so there are a lot of talks about the
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machine learning about deep learning
 

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machine learning about deep learning
about<00:02:05.979><c> the</c><00:02:06.159><c> data</c><00:02:06.340><c> infrastructure</c><00:02:06.670><c> and</c><00:02:07.570><c> what's</c>

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about the data infrastructure and what's
 

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about the data infrastructure and what's
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the like better usage of it and stuff
 

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the like better usage of it and stuff
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but how to actually using this kind of
 

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but how to actually using this kind of
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machine learning solutions on the
 

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machine learning solutions on the
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rearward problem that connection seems
 

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rearward problem that connection seems
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to be less slightly loose connect
 

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to be less slightly loose connect
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compared to the other ones so I wanted
 

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compared to the other ones so I wanted
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to give a talk about that in a way that
 

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to give a talk about that in a way that
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I had this problem i turned it into the
 

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I had this problem i turned it into the
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machine learning problem by dice slice
 

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machine learning problem by dice slice
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and dice it and applied like disqus
 

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and dice it and applied like disqus
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inside for it so that's my intention of
 

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inside for it so that's my intention of
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the top and whomever interested in like
 

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the top and whomever interested in like
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how to solve the problem using machine
 

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how to solve the problem using machine
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so<00:02:53.210><c> the</c><00:02:53.630><c> term</c><00:02:53.960><c> data</c><00:02:54.260><c> scientist</c><00:02:55.190><c> itself</c><00:02:55.760><c> is</c>

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so the term data scientist itself is
 

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so the term data scientist itself is
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kind of vague these days just like
 

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kind of vague these days just like
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machine learning the big data like it's
 

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machine learning the big data like it's
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pretty buzzwords so whenever some
 

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pretty buzzwords so whenever some
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company says they are hiring data
 

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company says they are hiring data
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scientist actually if you take a look at
 

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scientist actually if you take a look at
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the job details like soon they are
 

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the job details like soon they are
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trying to hire it's all different
 

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trying to hire it's all different
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someone is trying to hire the data and
 

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someone is trying to hire the data and
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only someone is trying to hire the
 

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only someone is trying to hire the
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machine learning personnel someone's
 

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machine learning personnel someone's
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trying to hire the data infrastructure
 

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trying to hire the data infrastructure
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engineer so if you want to be data
 

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engineer so if you want to be data
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scientist and if you want to be great
 

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scientist and if you want to be great
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one one piece of advice that I can give
 

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one one piece of advice that I can give
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you is have some capability on
 

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you is have some capability on
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engineering itself it's really necessary
 

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engineering itself it's really necessary
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so coming up with inside coming up with
 

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so coming up with inside coming up with
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motor that's all great thing but when we
 

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motor that's all great thing but when we
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are talking about industry not about
 

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are talking about industry not about
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academia and data scientists are the
 

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academia and data scientists are the
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ones in the industry so you need to be
 

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ones in the industry so you need to be
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able to ship things and make it
 

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able to ship things and make it
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production eyes and for you to do the
 

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production eyes and for you to do the
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products and I do need engineering power
 

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products and I do need engineering power
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either have it yourself that would be
 

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either have it yourself that would be
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very helpful if not you can when you're
 

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very helpful if not you can when you're
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being interviewed for the data scientist
 

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being interviewed for the data scientist
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the company which data scientists take a
 

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the company which data scientists take a
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look at what the culture is like how the
 

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look at what the culture is like how the
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team is more mated formulated in a way
 

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team is more mated formulated in a way
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that if that team itself has some other
 

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that if that team itself has some other
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engineers who can build the things you
 

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engineers who can build the things you
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ship you want to that'd be great so
 

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ship you want to that'd be great so
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bottom line is in any way you need to
 

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bottom line is in any way you need to
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have some engineering capacity that you
 

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have some engineering capacity that you
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can put your hands on that's the answer
 

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can put your hands on that's the answer
you

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