Would you hire a good software engineer who knows data science OR a pure data engineer / data scientist ?

Greetings

Welcome to the first edition of my newsletter

I believe that I offer a unique perspective to Artificial Intelligence

I have an academic background due to my teaching in Oxford for Artificial Intelligence. I am also the chief AI officer of a venture funded start-up in Germany (and we are recruiting) digitty.io. I have implemented commercial AI projects (mostly in bioinformatics and cybersecurity) and finally I am on the board of a New York based cybersecurity company castellum.ai and I am neuro diverse.

I hope you will find it useful

In this edition, I ask the question

Would you hire a good software engineer who knows data science OR a pure data engineer / data scientist

This is an important question because so many people are attempting to transition their career to AI.

Recently, we advertised for a data scientist / data engineer

I was leaning towards a pure data engineer and we got many applicants - but finally - we chose a person who was a 'good software engineer who is a problem solver and also knows data science and data engineering' (to paraphrase their words)

So, when would you hire a software engineer and retrain them or choose a pure data engineer/ data scientist?

Here are my thoughts (note these are general ideas - not concrete steps)

1) If you are in a large company, the roles between a data scientist, data engineer and devops are clearly demarcated (based on the MLOps model). Hence, you probably need a specialist

2) Do you aspire to be an AI leader? - this could need both specialists and generalists (expanded in future editions)

3) Are you specialised in a Cloud platform? - you could use a generalist. Because Cloud platforms make things easier overall

4) If you are a start-up - you may be ok with a generalist because the work may be dynamic and rapidly changing

5) If you want to develop unique IPR - you need more specialists (more on this in subsequent editions)

6) If you are working with complex problems, you need specialists (more on this in subsequent editions)

7) If you are hiring fresh out of of Uni, try to choose a software engineer i.e. generalist - and you can invest in them to build up their skills. 

Finally, there is an entirely different way to look at recruiting i.e. think of the difference between an Engineer and a Scientist

Scientists do fundamental work and engineers do applied work.

Most people mix the two. Most companies need engineers and not scientists. But its no longer a dichotomy and these two roles (scientist and engineer) are merging.

If a person is asking, "why does this happen?" they are a scientist. Thus, no matter where on the spectrum they stand, they are looking toward fundamental issues. If a person is asking, "How do I make this work?" they are an engineer, and are looking toward the applied end. source northwestern Uni

So, the meta question you should be thinking of is: Do we need a scientist or do we need an engineer? or conversely, as a candidate, Am I comfortable as a scientist or as an engineer?

These are broad guidelines - not hard and fast rules. There are no right answers and everyone is somewhere in between but leaning more to one way or the other.

However, most recruiters do not think in this level of granularity - to the detriment of both the candidate and the client.

Companies also rely on methods like leetcode which hamper diversity in tech

But the AI job market is vibrant - even in COVID times.

My long time mentor and friend Dr Kirk Borne is recruiting and also the insightful Karolis Urbanos from AWS is also recruiting

But this also raises other questions - for example:

What is an example of a 'complex problems' that need deeper / PhD level thinking in AI?

That's the subject for the next edition

Finally, if you are interested in studying in our courses at #universityofoxfod please see this course Developing artificial intelligence applications using python and tensorflow

Image source adapted from northwestern Uni