Shadowing Practice: How to Land a Data Science Job in 2 Months - Learn English Speaking with Video

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most people think that going from a data analyst to a data scientist is very hard

and can take years however one of my recent clients billy

did this transformation in just two months billy joined my program on may 1st

and his goal was to land a senior data scientist position at global

or big company at that point in time billy has sent in over 30 applications

and didn't hear anything back he didn't know how to effectively communicate

and portray his skills on his LinkedIn

and resume he also didn't know how to communicate through recruiters during the interview process

and finally he didn't really fully understand how to position himself for these data science

and machine learning roles that he was targeting

but over the next couple of months Billy

and I worked very closely together to address

and solve these problems one by one Billy worked very hard every single day listen to the feedback

and then two months later he got offered a senior data science position at a global established company

and his total compensation went up by 30k usd how quickly

did it take you to get an offer out of curiosity i mean i know

but just like how quick was that process in terms of

when you start refining things to when you start getting offers it's uh one and

and a half months yeah it's much much I'm honest it's

much much than I expected yeah yeah I gave myself six

months yes to land my first author yeah yeah Billy is an absolute legend

and was a complete pleasure to work with in fact he's still in the program improving his skills even further

so in this video I want to break down the exact three -step system

that Billy followed

and is also a system I I've followed with all my other clients to help them land their dream data

or machine learning role.

Let's get into it.

So like I said Billy joined my program on May 1st

and he had so many questions about essentially how should he actually go from a data analyst to a data scientist.

At this point in time Billy had been working for just

over five years at an established big company here in the uk

and before that he was a journalist

so he spent most of his time at one company

and what he told me is that he enjoyed his work

but he felt a bit stagnant uh there wasn't much growth

and he was just a little bit he wanted something more exciting

because he wanted his career to really kick on

and reach those higher tier companies that he was after

when billy joined he asked questions like what type of data

science role should i target what kind of companies should i focus on

and can he move industries from media to other industries these are all great questions

and we started off by fixing his positioning

which was exactly in the first week so a thing I like to say

and it goes back to marketing is that

if you appeal to everyone you appeal to no one

and the same is true in this data field right I've

had people come to me who say there are a data analyst a data scientist

and a machine learning engineer not not even joking I literally had someone last week who had this exact profile

and I tell them you're not you can't be all three it's like saying you're a dentist a doctor

and you're also a psychiatrist you're just not right

and unfortunately you have to be specific and niche down in a particular area

so that you kind of swallow the market in

that let me show you an example so when someone asks me eagle you know what is your positioning?

What is your niche?

This is what I say.

I'm a machine learning engineer with over five years experience in the energy insurance and logistic industries, specializing in time series forecasting,

operations research, and apply machine learning.

It's very crystal clear and very specific, but that's deliberate because whenever someone LinkedIn searches machine learning engineer,

time series, optimization, London, I'm the first hit.

I swallowed the market in my positioning

and that's what enabled me to basically get offers fairly easily

because i'm the top here i don't go to people people come to me right

and this is the exact positioning i nailed every single client

as soon as they come into my program i literally sit down with them for half an hour

and i draft them out this two -centage summary so they're

so they are crystal clear on their positioning

and we did did this exact same thing for billy

and then billy's summary was journalists turned data scientists with five plus years of experience building machine learning forecasting

and statistical models across media products identifying 50 million pounds in revenue opportunities

that is just phenomenal summary right like when you hear

that you'd be like yeah this guy's good right he's condensed

that his revenue his specialisms and his industries

and also noticed he started already calling himself a data scientist you know before

that billy was a data analyst with some you know data data science work but we literally

or I literally forced him to call him a data scientist

because once you assume that profile and you kind of have

that goal and everything else you kind of tailor to

that it's like a mindset shift and you just assume

that identity and you just approach interviews applications in

that frame it's a real powerful psychological trick and like I said

that summary we just drafted Billy is very specific right like we mentioned his specialisms we mentioned

that he's in the media industry mainly and

that he's you know generated 50 million pounds in revenue i

mean like why wouldn't you want to hire this person particularly

if you're after someone in those industries it's just the positioning is

so nailed down that no wonder billy got a job

so quickly so after we got

that fleshed out billy then went on to update his resume

and his linkedin accordingly with that positioning

and as you can see from my comments here below on his resume review

that i gave him

that i was actually blown away by his resume now drafting a good resume is actually not that hard

and what it comes down to is people literally putting in

effort right it's just effort you know i've told many people i've got loads of videos on this channel

that your resume should have this this and this people still don't do it

but billy he literally listened to what i told him he literally went through every checklist i gave him

and he executed literally to the T, right?

That's why I was blown away because his resume was phenomenal.

But Billy's not an exception.

100

All of you can do this.

101

It's more that are you willing to put in 10 hours of work to create a really good resume?

102

What Billy did exceptionally well was that every bullet point in his experience section was phenomenal, right?

103

So he hit on everything.

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He explained exactly what he'd done clearly in English.

105

He added technical terms to show his knowledge, but he added them in organically he keyword padded as well

106

so not only did he have technical terms but he also added keywords

107

but he added them in a way

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that didn't seem like he was stuffing them in

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but they were added in very naturally he mentioned the metrics

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and the numbers he improved and finally he stated the exact financial

111

and business impact of his work

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which the main thing let me give you an example i'm

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sure billy won't mind me sharing this from his resume of an experience bullet point like this is just fantastic.

114

So listen to this very closely.

115

Implemented a Bayesian probabilistic license fee uplift model using Monte Carlo simulation, beta distributions and PERT analysis,

116

projecting a 900 % increase in detectable unlicensed accounts,

117

27k additional cases monthly and a 15 million annual revenue opportunity.

118

Deployed this model end -to -end pipeline with apache airflow and

119

and aws redshift just wow i mean it sounds impressive right

120

he built the model i understand what it did it had a metric improvement had a financial improvement

121

and he deployed it end -to -end like these are the bullet points we should all aim for

122

and no wonder

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if you write something like this on your resume someone be

124

like yeah again this guy knows what he's talking about

125

and that's the standard we want to adhere to

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and the fact is billy did it so perfectly that

127

because the competition not gonna lie to you is quite low no wonder he was able to land the role

128

so quickly because he just executed on the 80 of things

129

that most people just don't do

130

and a second thing is to polish my linking profile

131

and cv yeah because before i joined this program i applied for uh dozens of jobs

132

and uh the conversion rate if i can put it that way the conversion rate was quite low, if I'm honest.

133

But that was because I was not communicating my experience very effectively on my profile and my CV.

134

And Billy took this exact same framing and impact to his LinkedIn.

135

And literally within the first, I think, four weeks, he immediately started landing inbound interviews.

136

In fact, one of his first interviews, which was 27 days later from joining my program was at Spotify right Spotify this is a company

137

that people dream of getting into

138

and Billy got it simply within four weeks just by being

139

very precise in his positioning nailing his resume in LinkedIn

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and just really applying himself right and there's no secret he got

141

that because he was working very hard and taking on feedback

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and just listening to everything we were working through

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and again Spotify interview in four weeks that's some people's dream

144

so after there'll be fixed positioning resume in LinkedIn we then had to get those interviews

145

but at this point in time Billy was smashing through interview requests

146

or at least inbound and both outbound from recruiters he was getting

147

so many interviews it was actually crazy in fact Billy was landing interviews at companies like PayPal Amazon

148

and TikTok within a month

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and a half of joining my program I asked Billy like

150

why did why did he think he was landing all these interviews

151

and this was exact words he repositioned himself from analyst to

152

scientist makes sense he added all those revenue numbers on his cv resume

153

and he started dming recruiters and general colleagues at these companies using our referral templates

154

and as you can see from the screenshot this is you know i'm not saying we're the best

155

but like i like to think we know what we're talking

156

about billy said verbatim just like what you said guys just do it these things work

157

and during this program uh one thing very important is to always put revenue numbers on the cv right yeah and

158

that really helped me uh to land a lot of interviews

159

really a lot well i like to think well i do know what i'm talking about

160

but when you actually fully commit

161

which what billy did to the program he listened to everything

162

we told him he went through the steps we worked together

163

closely he asked for consistent feedback i'm not taking anything away from billy he worked very hard

164

but he applied everything we told him

165

and he also learned what worked for him there's no wonder

166

he got results what kind of stops people in a job search is commitment

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and simply being like i am going to do this no

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matter what i'm going to listen i'm going to take the

169

feedback on board i'm going to go through the emotions most people have drama problems

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and what i mean is most things in life are drama problems

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or maths problems and majority of things

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that happen to you are honestly drama problems like you know i can't refer i can't reach out for referrals

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because it's transactional

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or I'm shy you know oh I don't know any numbers

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on my resume like none of my work was linked to business impact these are simply excuses

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and Billy didn't have any right he just said okay

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if this is what I need to do I'm going to

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do it he didn't think about it he was like okay

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Igor told me to do this it's working for me

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so let me carry on doing it

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and that's of what you need to do the job search

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is hard getting a data science job is difficult it requires

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a lot of work we get paid 100k a year here

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in the uk 200k a year in the us it's not a walk in the park

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but if you leave your emotions out of it you approach it very objectively you will make progress

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because you won't have that drama thoughts in your head with all these interviews

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that billy was landing he even's dming me keeping me up

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to date with all the ones he was getting through the

189

pipeline he was literally getting a brand new inbound from top companies reach out to him almost every single day

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and he was just completely crushing it

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but at this point he was landing these interviews

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but again that's only really half the battle once you land

193

these interviews you also need to understand how to convert them to act well first you need to pass them

194

and then learn how to convert them to offers with all of these interviews

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that billy was landing we did mocks for recruiter screen hiring manager coding interviews case studies

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and behavioral we literally did 10 mock interviews in just three weeks so

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that billy felt prepared for all of these interviews he was

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facing at multiple companies to be fair to billy i i

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imagine it was exhausting like preparing for his interviews he also

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has you know i hope he doesn't mind me saying a

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full -time job a family like he had everything going on

202

yet he still managed to make time for applications recruiter reach out networking

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and also do 10 mock interviews with mean three weeks

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and you know i say again billy is a superhero in a way

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but he just really wanted the outcome

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and there's no wonder he got it right and

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so we did mock interview practice for all of these interviews consistently

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and he took upon the feedback

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and he started passing through the interview rounds paypal's coding round

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tiktok's coding round um a lot of the recruiter screens of

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these companies he was just smashing through um the interview rounds simply from doing practice applying the frameworks

212

and guides we gave him and just again leaving the drama out of it

213

and just thinking about this process completely objectively not only did we practice the interviews

214

but we also worked on how to actually communicate with recruiters throughout the interview process you see most people think

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that after you do an interview that's it right in reality it's like a date you know

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if i'm a man and uh

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and uh let's say i go on a date with a girl i would always ask

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if she got home safe right likewise

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if you're a girl probably the same thing right not only

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did we do mock interviews we also practiced on how to

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actually communicate with recruiters interviewers in between interview rounds is where you want to keep contact

222

and what that means is sending them a follow -up email saying how much you enjoyed talking to them

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and the ideas you have so one thing is basically just how to respond to recruiters

224

because before I joined this program I didn't really have much experience working with

225

and also answering questions from recruiters yeah so I think it really helps

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because I can just get feedback from the community

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and also I can also directly message you

228

and then you gave me some very useful feedback and

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so I knew how to like just respond to questions

230

that recruiters were asking just keeping our communication warm for the next interview

231

and this is exactly what billy and i worked with to ensure

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that he was progressing through these rounds with more we basically

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had a higher chance of progressing through the interview rounds

234

so again billy took on all his feedback executed on the frameworks

235

and then two months later he landed a senior data scientist role at a global company

236

that like he said is somewhere he definitely really wants to be

237

and he also got a 30k increase in total compensation

238

and i guess your role now is a role you wanted

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or like i mean you have to give the exact specifics

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on title well company title like any sort of responsibilities

241

or like the type of role you've got is something you're you're with

242

or is that what were your feelings about

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that yeah definitely i'm completely happy with this yeah

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because in our first call in our initial call i said

245

i wanted to find a uh senior a data scientist role in a big company global company yeah yeah

246

and i also want to do more machine learning in my day today

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and this role that i've got checked all of the boxes

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that i just mentioned is a it's a massive global company

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and also i will be doing a lot of machine learning deploying models

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so again billy you are a great person to work with you're a fantastic individual

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and he's even now carried on learning

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and pushing himself to you know improve even more so

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that way next time

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when he's looking for another role he knows exactly what he needs to do

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and he's kind of like learned this process very well this process

256

that we followed with Billy is not necessarily unique to him it's a system

257

that we've used to help land over 55 data professionals

258

and machine learning professionals in their dream role and

259

if you're interested in exactly

260

and in detail how this system works then i recommend you

261

check out this video where i literally break down the whole process in 90 minutes i'll see you there

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Bu dersin kelimeleri ve konuşma notları

Bu C1 seviyesindeki konuşma dersi “How to Land a Data Science Job in 2 Months” videosuna dayanıyor. En çok tekrarlanan kelimeler: billy, interview, resume, role. Bu videoda gölgeleme çalışması için 261 cümle ve 3123 kelime var. Konuşma bölümü 17:13 sürüyor. Konuşmacı hızlı konuşuyor, dakikada yaklaşık 181 kelime; bu yüzden birbirine bağlanan ve zayıflayan sesler duyacaksınız. Kelimelerin %83’i İngilizcede en sık kullanılan 3.000 kelime arasında; geri kalanına başlamadan önce bakmakta fayda var.

Bu videodaki önemli kelimeler

Videodaki en ileri düzey 15 kelime, telaffuzu ve anlamıyla:

Kelime	Telaffuz	Anlam
inbound sıfat	/ˈɪnbaʊnd/	gelen, gelmekte olan
phenomenal sıfat	/fɪˈnɒmɪnəl/	fenomenal
execute fiil	/ˈɛksɪˌkjuːt/	idam etmek
keyword isim	/ˈkiː.wɜːd/	anahtar kelime, anahtar sözcük
swallow fiil	/ˈswɑ.loʊ/	yutmak
pipeline isim	/ˈpaɪpˌlaɪn/	boru hattı
forecast isim	/ˈfɔːkɑːst/	tahmin
checklist isim	/ˈt͡ʃɛklɪst/	doğrulama listesi, kontrol listesi
condense fiil	/kənˈdɛns/	yoğuşmak
detectable sıfat	/dɪˈtɛktəbəɫ/	ortaya çıkarılabilir, keşfedilebilir
outbound sıfat	/ˈaʊtbaʊnd/	giden, gitmekte olan
probabilistic sıfat	/ˌpɹɑ.bə.bɪˈlɪs.tɪk/	olasılıksal
redshift isim	/ˈɹɛdˌʃɪft/	kırmızıya kayma
unlicensed sıfat	/ˌʌnˈlaɪsənst/	lisanssız
uplift fiil	/ʌpˈlɪft/	yükseltmek

Duyacağınız deyimsel fiiller

Kelime	Anlam
check out fiil	gözden geçirmek

Bu videodaki dil bilgisi

Konuşmacının en çok kullandığı yapılar, videodaki sözcüklerin aynısıyla:

Yapı	Videoda
Edilgen yapı be + fiilin üçüncü hâli — kimin yaptığı değil, ne olduğu önemli	was actually blown · was blown · were added
Present perfect have/has + fiilin üçüncü hâli — geçmişte olan ama şimdi de önemli olan bir eylem	has sent · I've followed · i've told

Dikkat edilecek telaffuzlar

Konuşmacı I'm, you're, didn't gibi kısaltılmış biçimleri 61 kez kullanıyor. Bunları duyduğunuz gibi kısa söyleyin.

“sh” ve “zh” sesleri: smash /smæʃ/, redshift /ˈɹɛdˌʃɪft/, specialize /ˈspɛʃəˌlaɪz/, transactional /trænˈzækʃ(ə)nəl/, optimization /ˌɑptəmaɪˈzeɪʃən/
Uzun kelimeler — vurguyu doğru yere koyun: phenomenal /fɪˈnɒmɪnəl/, objectively /əbˈd͡ʒɛktɪvli/, detectable /dɪˈtɛktəbəɫ/, organically /ɔːˈɡænɪkli/, probabilistic /ˌpɹɑ.bə.bɪˈlɪs.tɪk/

Türkçe konuşanların zorlandığı sesler:

/w/ — dudaklar yuvarlak, /v/ değil: keyword /ˈkiː.wɜːd/, swallow /ˈswɑ.loʊ/, likewise /ˈlaɪkˌwaɪz/
Kelime başındaki ünsüz kümesi — araya ünlü eklemeyin: swallow /ˈswɑ.loʊ/, smash /smæʃ/, probabilistic /ˌpɹɑ.bə.bɪˈlɪs.tɪk/, specialize /ˈspɛʃəˌlaɪz/, stagnant /ˈstæɡnənt/
/æ/ — “e”den daha açık: analyst /ˈænəlɪst/, smash /smæʃ/, organically /ɔːˈɡænɪkli/, stagnant /ˈstæɡnənt/, transactional /trænˈzækʃ(ə)nəl/

Bu videoyla nasıl çalışılır

Videonun tamamını konuşmadan bir kez dinleyin ve bilmediğiniz kelimeleri not edin.
0,75× hızla başlayın, cümle cümle tekrar edin ve kolaylaşınca normal hıza dönün.
Kendinizi kaydedin ve orijinaliyle karşılaştırın; inbound, phenomenal, execute gibi kelimelere özellikle dikkat edin.

Gölgeleme Tekniği Nedir?

Gölgeleme, başlangıçta profesyonel tercüman eğitimi için geliştirilen ve çok dilli Dr. Alexander Arguelles tarafından popüler hale getirilen, bilim destekli bir dil öğrenme tekniğidir. Yöntem basit ama güçlüdür: ana dili İngilizce olan bir sesi dinler ve hemen yüksek sesle tekrar edersiniz — konuşmacıyı 1-2 saniye gecikmeyle takip eden bir gölge gibi. Pasif dinleme veya dilbilgisi alıştırmalarının aksine, gölgeleme beyninizi ve ağız kaslarınızı gerçek konuşma kalıplarını eşzamanlı olarak işlemeye ve yeniden üretmeye zorlar. Araştırmalar, telaffuz doğruluğu, tonlama, ritim, bağlı konuşma, dinleme anlama ve konuşma akıcılığını önemli ölçüde geliştirdiğini göstermektedir — bu da onu IELTS Konuşma hazırlığı ve gerçek dünya İngilizce iletişimi için en etkili yöntemlerden biri yapar.

Shadowing tekniği: adım adım eksiksiz rehberi okuyun →

Shadowing Practice: How to Land a Data Science Job in 2 Months - Learn English Speaking with Video

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Bu dersin kelimeleri ve konuşma notları

Bu videodaki önemli kelimeler

Duyacağınız deyimsel fiiller

Bu videodaki dil bilgisi

Dikkat edilecek telaffuzlar

Bu videoyla nasıl çalışılır

Gölgeleme Tekniği Nedir?