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Here's a current listing for a hedge fund quant (emphasis mine):

  • Aid with data curation, data clean up and the buildout of models that will enable the team to identify data driven investment signals
  • Manage data throughout its lifecycle to ensure that the data can be retrievable for future use
  • Monitor and Mitigate Information for redundancy and transparency
  • Help with the process of curating, cleaning and integrating data to enable scalability of analysis
  • Creatively source, aggregate and analyze massive amounts of data
  • Use Machine Learning applications on unstructured data to extract investment insights

Now, like many industry quants, I certainly have experience with working with large amounts of data--it's used in calibration, backtesting, coming up with new signals, etc.--it's a given that you do these things if you have model development experience. I've never thought to emphasize this part of my job; however, I see more and more listings like the one above which seem to focus exclusively on these (presumably given) skills, and naturally want to align my resume with the skills that are being sought after.

I am keen to learn how others have adapted to this seemingly new regime, in terms of what exactly you changed about your resume to appear more competitive with these "new" requirements?

Obviously, my view is that the skills being sought after are not really anything new to those with "classical" model development experience; it's simply a matter of emphasizing this aspect of one's experience. Alternative views are welcomed and encouraged, of course :)

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I suspect that this (as it's career-focused rather than technical) is off-topic here, but my brief response would be to change your emphasis as you read the spec. The data is not the key, but the skills you can deploy to analyse it:

  • Aid with data curation... and the buildout of models that ... identify data driven investment signals
  • Manage data
  • Monitor Information
  • Help
  • Creatively source, aggregate and analyze massive amounts of data
  • Use Machine Learning applications on unstructured data to extract investment insights

So they're looking for data management/understanding (can you fix up incomplete data sets), and then ML-heavy analysis to drive investment insights.

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