Tuesday, May 24, 2016

Data Scientists vs Algorithms vs Real Solutions to Real Problems

A few weeks ago there was a short tweet in my tweetstream that kindled some thoughts.

"All people are biased, that's why we need algorithms!" "All algorithms are biased, that's why we need people!" via @pmarca

And a retweet/reply by Diego Kuonen:

"Algorithms are aids to thinking and NOT replacements for it"

This got me thinking about  a lot of work that I have been doing,  past job interviews and conversations I have had with headhunters and 'data science' recruiters, as well as a number of discussions or sometimes arguments about what defines data science and about 'unicorns' and 'fake' data scientists. I ran across a couple interesting perspectives related to this some years back:

What sets data scientists apart from other data workers, including data analysts, is their ability to create logic behind the data that leads to business decisions. "Data scientists extract data, formulate models and apply quantitative analysis in a proactive manner" -Laura Kelley, Vice President, Modis.

"They can suck data out of a server log, a telecom billing file, or the alternator on a locomotive, and figure out what the heck is going on with it. They create new products and services for customers. They can also interface with carbon-based lifeforms — senior executives, product managers, CTOs, and CIOs. You need them." - Can You Live Without a Data Scientist, Harvard Business Review.

I have seen numerous variations on Drew Conway's data science Venn Diagram, but I think Drew still nails it down pretty well. If you can write code, understand statistics, and can apply those skills based on your specific subject matter expertise, to me that meets the threshold of the minimum skills most employers might require for you to do value added work. But these tweets beg the question, for many business problems, do we need algorithms at all, or what kind of people do we really need?

 Absolutely there are differences in skillsets required for machine learning vs traditional statistical inference, and I know there are definitely instances where knowing how to set up a Hadoop cluster can be valuable for certain problems. Maybe you do need a complex algorithm to power a killer ap or recommender system.

I think part of the hype and snobery around the terms data science and data scientist might stem from the fact that they are used in so many different contexts and they mean so many things to so many people that there is fear that the true meaning will be lost along with one's relevance in this space as a data scientist. It might be better to forget about semantics and just concentrate on the ends that we are trying to achieve.

I think a vast majority of businesses really need insights driven by people with subject matter expertise and the ability to clean, extract, analyze, visualize, and probably most importantly, communicate. Sometimes the business need requires prediction, other times inference. Many times you may not need a complicated algorithm or experimental design at all, not as much as you need someone to make sense of the nasty transactional data your business is producing and summarize it all with something maybe as simple as cross tabs. Sometimes you might need a PhD computer scientist or Engineer that can meet the strictest of data science thresholds, but lots of times what you really may need is a statistician, econometrician, biometrician, or just a good MBA or business analyst that understands predictive modeling, causal inference, and the basics of a left join.
This is why one recommendation may be to pursue 'data scientists'  outside of some of the traditional academic disciplines assocaited with data science.

There was a similar discussion a couple years ago on the SAS Voices Blog in a post by Tonay Balan titled "Data scientist or statistician: What's in a name?" 

"Obviously, there are a lot of opinions about the terminology.  Here’s my perspective:  The rise and fall of these titles points to the rapid pace of change in our industry.  However, there is one thing that remains constant.  As companies collect more and more data, it is imperative that they have individuals who are skilled at extracting information from that data in a way that is relevant to the business, scientifically accurate, and that can be used to drive better decisions, faster.  Whether they call themselves statisticians, data scientists, or SAS users, their end goal is the same."

And really good insight from Evan Stubbs (in the comment section):

"Personally, I think it's just the latest attempt to clarify what's actually an extremely difficult role. I don't think it's synonymous with any existing title (including statistician, mathematician, analyst, data miner, and so on) but on the same note, I don't think it's where we'll finally end up.

From my perspective, the market is trying to create a shorthand signal that describes someone with:

* An applied (rather than theoretical) focus
* A broader (rather than narrow) set of technical skills
* A focus on outcomes over analysis
* A belief in creating processes rather than doing independent activities
* An ability to communicate along with an awareness of organisational psychology, persuasion, and influence
* An emphasis on recommendations over insight

Existing roles and titles don't necessarily identify those characteristics.

While "Data Scientist" is the latest attempt to create an all-encompassing title, I don't think it'll last. On one hand, it's very generic. On the other, it still implies a technical focus - much of the value of these people stems from their ability to communicate, interface with the business, be creative, and drive outcomes. "Data Scientist", to me at least, carries very research-heavy connotations, something that dilutes the applied and (often) political nature of the field. "

One thing is for certain, recruiters might have a better shot at placing candidates for their clients if role descriptions would just say what they mean and leave the fighting over who's a real data scientist to the LinkedIn discussion boards and their tweetstream.

See also:

Economists as Data Scientists

Why Study Agricultural and Applied Economics?

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