6 ms·

The key takeaway from this for me is that there is no shortage of machine learning practitioners in the UK despite popular opinion that it is a good career move

onion2k 7y ago
I don't quite see how you can come to that conclusion. The report is only about companies that responded to the survey: "The survey asked firms to provide information on two ML case studies within their organisation." Any company that doesn't have two ML case studies to talk about isn't included. That might be 10% of finance companies or it might be 90%. We can't know from what's in the report.
- goatinaboat 7y ago
  Charts 11 and 12 show that lack of talent is a minor consideration. I would be willing to bet that most people would have expected it to be much higher.
  - chillydawg 7y ago
    Self selected for firms who have big enough teams to respond to the study. Bias.
bogle 7y ago
Where I am at the moment, a UK bank's retail data analysis service, any ML projects are set up on a "learn as you go" basis whilst still doing your main job. No specialised practitioners are deemed required. Whether or not this is a good idea is moot: this is in the bowels of the bank and not the bleeding edge of commerce.
- faceplanted 7y ago
  We don't really need specific ML practitioners for most jobs in the same way we don't need Genetic Algorithm practitioners in most jobs either, unless you're a researcher you can just read a book about it and start trying it out.
  - goatinaboat 7y ago
    Indeed. If you already understand the data and the business and you already know Python or R then you can download Keras in the morning and have something useful to the business running in the afternoon. Then you can add ML to your CV. It’s not clear where an ML expert who isn’t already familiar with the data and the business fits in or adds value, or even what an expert really means in industry.
    - mellosouls 7y ago
      Adding ML to your CV with the Mickey Mouse experience and understanding you describe would be a good way to get yourself drummed out of a job where you are actually expected to have the expertise you claim. Agree with your point about domain knowledge being important.
    - ska 7y ago
      Domain knowledge is really important, you can't overstate that. But you've also succinctly described why a huge percentage of ML as practiced in industry under performs or flat out fails. To a first approximation the person who reads a few tutorials and plays with example data sets, then sets out to apply it to their own domain, has no real idea what they are doing - and it shows.
  - atupis 7y ago
    It is more like do you specialist x, eg if you have fullstack engineer do you really need frontend developer or DevOps engineer, generally no but there is case where specialist might make big differences.
    - goatinaboat 7y ago
      fullstack engineer do you really need frontend developer These terms are meaningless in both ML and finance.
      - mellosouls 7y ago
        I read it as an analogy, I think that's what was intended.
- tixocloud 7y ago
  This comes down to how well your ML-leading exec is at articulating the business value of the project and coming up with a strategic roadmap to go from "learn as you go" to fully embedded while managing risk.
spratzt 7y ago
Absolutely true. I have several friends who are machine learning practitioners. Many are actively looking to get out. When you strip away the hype and BS the demand in London is very thin.
- tixocloud 7y ago
  There are likely very specific areas where ML can have a real impact and other areas that are nice, interesting but not really priority. From my experience, a large part of the demand also needs to be driven by the AI and ML execs' ability to sell to the business.
  - goatinaboat 7y ago
    If you have deep domain knowledge already then ML skills are the icing on the cake. But opportunities for those with just the ML part are minimal.
    - tixocloud 7y ago
      Agreed - many folks discount deep domain knowledge which is, my opinion, a mistake.
  - deleted 7y ago
    [deleted]
- shrikant 7y ago
  In my experience, this is usually because they realise that applying sexy algorithms and generating magic earth-shattering insights is about 2% of the job, and the rest is "boring" stuff like automation, data cleansing, performance at scale, and building and maintaining discipline around sound development practices. Edited to add: this isn't a diss on them or this phenomenon in general -- it keeps me employed :)
deleted 7y ago
[deleted]
m-i-l 7y ago
Indeed. The Bank of England's report was summarised by one news source yesterday as "Banks say there's no shortage of machine learning talent"[0]. [0] https://news.efinancialcareers.com/uk-en/3002418/machine-learning-jobs-banking https://news.efinancialcareers.com/uk-en/3002418/machine-lea...
angry_octet 7y ago
It just tells me that senior banking people in the UK have such a shortage of senior ML people that all they see is mickey mouse ML stuff by shysters and snake oilers. They would have to do real work on the antique back end to make it work, and that is hard.

onion2k 7y ago

I don't quite see how you can come to that conclusion. The report is only about companies that responded to the survey: "The survey asked firms to provide information on two ML case studies within their organisation." Any company that doesn't have two ML case studies to talk about isn't included. That might be 10% of finance companies or it might be 90%. We can't know from what's in the report.

goatinaboat 7y ago
Charts 11 and 12 show that lack of talent is a minor consideration. I would be willing to bet that most people would have expected it to be much higher.
- chillydawg 7y ago
  Self selected for firms who have big enough teams to respond to the study. Bias.

bogle 7y ago

Where I am at the moment, a UK bank's retail data analysis service, any ML projects are set up on a "learn as you go" basis whilst still doing your main job. No specialised practitioners are deemed required. Whether or not this is a good idea is moot: this is in the bowels of the bank and not the bleeding edge of commerce.

faceplanted 7y ago
We don't really need specific ML practitioners for most jobs in the same way we don't need Genetic Algorithm practitioners in most jobs either, unless you're a researcher you can just read a book about it and start trying it out.
- goatinaboat 7y ago
  Indeed. If you already understand the data and the business and you already know Python or R then you can download Keras in the morning and have something useful to the business running in the afternoon. Then you can add ML to your CV. It’s not clear where an ML expert who isn’t already familiar with the data and the business fits in or adds value, or even what an expert really means in industry.
  - mellosouls 7y ago
    Adding ML to your CV with the Mickey Mouse experience and understanding you describe would be a good way to get yourself drummed out of a job where you are actually expected to have the expertise you claim. Agree with your point about domain knowledge being important.
  - ska 7y ago
    Domain knowledge is really important, you can't overstate that. But you've also succinctly described why a huge percentage of ML as practiced in industry under performs or flat out fails. To a first approximation the person who reads a few tutorials and plays with example data sets, then sets out to apply it to their own domain, has no real idea what they are doing - and it shows.
- atupis 7y ago
  It is more like do you specialist x, eg if you have fullstack engineer do you really need frontend developer or DevOps engineer, generally no but there is case where specialist might make big differences.
  - goatinaboat 7y ago
    fullstack engineer do you really need frontend developer These terms are meaningless in both ML and finance.
    - mellosouls 7y ago
      I read it as an analogy, I think that's what was intended.
tixocloud 7y ago
This comes down to how well your ML-leading exec is at articulating the business value of the project and coming up with a strategic roadmap to go from "learn as you go" to fully embedded while managing risk.

spratzt 7y ago

Absolutely true. I have several friends who are machine learning practitioners. Many are actively looking to get out. When you strip away the hype and BS the demand in London is very thin.

tixocloud 7y ago
There are likely very specific areas where ML can have a real impact and other areas that are nice, interesting but not really priority. From my experience, a large part of the demand also needs to be driven by the AI and ML execs' ability to sell to the business.
- goatinaboat 7y ago
  If you have deep domain knowledge already then ML skills are the icing on the cake. But opportunities for those with just the ML part are minimal.
  - tixocloud 7y ago
    Agreed - many folks discount deep domain knowledge which is, my opinion, a mistake.
- deleted 7y ago
  [deleted]
shrikant 7y ago
In my experience, this is usually because they realise that applying sexy algorithms and generating magic earth-shattering insights is about 2% of the job, and the rest is "boring" stuff like automation, data cleansing, performance at scale, and building and maintaining discipline around sound development practices. Edited to add: this isn't a diss on them or this phenomenon in general -- it keeps me employed :)

deleted 7y ago

[deleted]

m-i-l 7y ago

Indeed. The Bank of England's report was summarised by one news source yesterday as "Banks say there's no shortage of machine learning talent"[0]. [0] https://news.efinancialcareers.com/uk-en/3002418/machine-learning-jobs-banking https://news.efinancialcareers.com/uk-en/3002418/machine-lea...

angry_octet 7y ago

It just tells me that senior banking people in the UK have such a shortage of senior ML people that all they see is mickey mouse ML stuff by shysters and snake oilers. They would have to do real work on the antique back end to make it work, and that is hard.