Careers and data science

[From a 2013 talk by Acculation’s CEO to undergraduate political science majors about careers in data science.]

Hi, I’m Dr. Werner Krebs, Ph.D., CEO of Acculation.  Hopefully, you’ll subscribe to our YouTube channel (click here) and checkout one of these other videos on our channel when you’re done with this one, and if you like this video be sure to click that like button.

Careers: what not to do when applying for a job?

A couple of approaches don’t work.

“Hi, I’m a really bright guy. Hire me! I have a new idea!”

With the possible exception of strategy consultants, academics, and think tanks, companies generally don’t hire people just because they’re smart. They hire people because they have an existing business process need.

So, they’re making widgets or making software and they have to too many order for the widgets or their
software. And so they look at Bob and they say, “Okay, well, we’ve got too many orders coming in.”

“So, what we want to do is: We want to take Bob over there and clone him.”

Except, of course, human cloning is illegal. So they take a step back and decide:

“Well, we can’t clone Bob even though he’s really good at making widgets.”

“So, what else can we do?”

They look at the job market and they say, “Can we find someone who is just like Bob? So, that we can meet our customer demand?” And along those lines: “Hi, I’m a really bright” or, “Hi, I graduated summa cum laude in English! You should hire me because I have this very impressive summa cum laude English degree!” That doesn’t work.

For most people that very impressive summa cum laude degree in English means that the only thing they’re really comfortable with is writing. And writing, unfortunately, by itself, does not pay very well because there are a
lot of other writers out there.

If you want to be hired just for being bright or for having a very impressive college degree consider consider
consulting. They’ll usually hire you because you have an MBA or other application of strategy consultant.

If you want to be hired because you have a new idea that you think will revolutionize the market consider doing a start
up. Go through an accelerator or incubator. Then, you’ll be employed by investors backing your new idea. I mentioned a couple of the other things that do employ people just because they’re very bright: academics, universities, and think-tanks.

So, who am I?

I’m Werner G. Krebs (longer online bio here) I began programming at a very early age. I was a University of Chicago undergraduate math major worked for a Nobel Laureate in economics. Did a Yale PhD in the this new field of bioinformatics, technically Molecular Biophysics & Biochemistry. So bioinformatics is computer science plus biology. This led to a career as a computer science or a data scientist worked for some very famous people, did some time in academia, at a software company and then on to finance as a senior analyst at what is now Bank of America managing their billion hedging portfolio.

Article continues next page.

Legal Note: From a 2013 talk by Acculation’s CEO to undergraduate political science majors about careers in data science. Video and this blog posting (C) 2017 Acculation. All Rights Reserved. If you find this message on an website other than acculation.com or youtube.com — and this has happened or this note wouldn’t be here — please help us out by contacting acculation.com.

Article continues from previous page.

>
 

Careers and data science

Then, I went on to the hedge fund Madison Tyler, now known as Virtu Financial, of the top high frequency trading firms in the world according to Wikipedia. I led a team at a firm did marketing consulting for 90% of
Fortune 100 CMOs and learned marketing modeling from some of the best people in the world in academic marketing. Math and programming made all of this possible. I managed a team of engineers and have now started my own company Acculation, which does data science, artificial intelligence consulting and software development among other things. So, I am a graduate of the Founders’ Institute startup accelerator. This not easy: 60% of the people in my program didn’t make it to through. It’s been compared to survivor.

I have interviewed hundreds of people at different companies and hedge funds and marketing and consulting group where I was hiring manager. I have been told in the past that over a hundred people were interviewed for a position that I was eventually selected for. I’m going to point out again that the important thing to understand is that businesses have business process. They hire people to fill those business process needs. They prefer people that can start right
away, but what they generally don’t do is hire people simply because they’re smart or have a specific college degree without reference to those pre-existing business needs. I’ll say a little bit more about now, and a little bit more at the end.

Careers: Book Recommendations on Why Industry Needs Data Science

So, I’m going to throw some books out at you. Thinking, Fast and Slow is a 2011 book by Daniel Kahneman. Its central thesis is that there’s an dichotomy between these two modes of thinking.

System I

What he called System I is the fast, instinctual, and emotional system. This is the process that makes
decisions when you’re in a hurry, when you’re being chased by a lion. It’s about making decisions from the seat of your pants because you’re being chased by a lion.

System II

System II is the slower, more deliberate, more logical system. People do not behave as predicted by classical economics.

Those of you who were economics majors, you know were brought up in this rational decision-making framework. The truth is organizations tend to behave rationally and there’s a lot of evolutionary pressure on organizations: the tribe, the
family, corporations, governments to behave rationally. Those organizations that do not behave rationally tend not to survive. Evolution does not work on individuals. At least for animals, it does not work on individuals. It works on the species or it works on groups. As a result, the evolutionary pressure to be rational was not applied at the individual level.

There is actually a lot of evolutionary pressure for individuals not to be rational. That evolutionary pressure was applied at the group level: on families, on tribes, and there’s a lot of evidence that individuals do not behave rationally. Individuals are loss adverse. They are more likely to act to avert a loss than to achieve a gain. Thinking slow: so, the scientific method is a form of thinking deliberately to find the truth. Is an outcome repeatable and reproducible, is it option falsifiable? It is the opposite of superstition.

Article continues next page.

Article continues from previous page.

 

Careers and data science

The opposite of science is superstition. Astrology which is one of the few religions that can be disproved. These things that appeal to emotion or system one: that fast way thinking. Examples in political science are global warming and it’s politically motivated deniers.

Thinking slow by thinking fast at the same time: so, if you have the resources — you have employees and software like an Excel spreadsheet, you have trainin, you have MBAs, you have the inclination: business processes and incentive, survival, profit. They try to think “slow” whenever possible if you’re a company.

So you going to try to use computer models. For example, Excel or spreadsheets for thinking slow. Business frequently try to formalize the most important and most competitive processes to try to ensure quality. Some examples are forms, procedures, assembly lines, quality assurance inspection tests.

Ideally, businesses would like their most important, frequent analyses to think both slow and fast: to think slow in a
sense of thinking that is deliberative and rational. Also fast, not because they’re using System I, but or thinking intuitively, but fast because the thing is being done I computer model that can think very quickly. Examples are high-frequency trading, marketing analytics.

So, another book for you!

The Signal and the Noise

The Signal and the Noise which is the 2012 bestseller by Nate Silver.

The full title is: The signal and the Noise:Why most prediction fail but some don’t. It talks about building mathematical models. The synopsis is that to build a really good mathematical model you really need to understand the field.

Article continues next page.


Article continues from previous page.

 

Careers and data science

For example, in the baseball statistics in the example he cites, knowing which parameters are important and are reliable to select for in that model, rather than relying purely on [automated] statistical tests. And using a Bayesian
approach to model building.

Another example is Moneyball (both the book and the movie). And, of course, Lewis (the author of Moneyball) also did some books on trading and and high frequency trading, where mention some people I’ve worked with.

Careers in Big data

So another book: Big Data: the revolution that will transform how we live, work, and think came out in 2013. It talks about Big Data being a consequence of Kryder’s Law, which is Moore’s Law for disk space. We can now store, slice,
and analyze complete data sets. For example, all of Amazon purchases. We have enough data to build the statistical models on some obscure phenomenon, something that was not previously possible. It’s only become possible in the last two years [i.e., as of 2013, when the book and this lecture were both written]. Previously, we needed to subsample data to build statistical models. Now, we can program a computer to go through all of that data, build it’s own statistical models, and try to discover correlation and causation. For example, Google discovering certain queries where highly correlated to Swine Flu Virus. Modeling and political Science.

Careers: Big data in political science & public policy

So example, of a business is Robert Pape’s 2005 book: “Dying to Win: the strategic logic of [CENSORED — the video has the topic].”

Robert Pape from the University of Chicago. Most [CENSORED to avoid unnecessarily exciting Internet bots– the video has the topic], according to his research, are altruistic, well-educated, nationalist, motivated, and they’re fully witting, and dedicated to their fatal mission as a service to their community.

So, this is an example of where you can you use big data to discover things and use that to try to learn things about what motivates people and how you can potentially discourage this. Another example in political science is something I was a little bit involved with, which is Prof. Heckman’s work on the Job Training Partnership Act (JTPA). And, this was used to tweak legislated eligibility requirements to try to maximize access to the intended audience, which in this case with the the truly unemployed needing skills, and try to minimize free-riders. Some of the potential free riders were seasonally unemployed teachers who might appear eligible because of that summer break, and then people who are potentially quitting their jobs try to meet the requirements for getting this potentially very valuable training. By using data they were able to tweak the legislation so that seasonally unemployed teachers would not be eligible by requiring the person the unemployed for more than three months and putting in certain other things to try to ensure that the people who are applying really needed the training.

[Author’s note: some of the topics in this lecture — climate change & the use of data and science to inform sound public policy at places like airports were not especially controversial ideas in 2013 when this lecture was written. In 2017, at time of this posting, these ideas have suddenly become controversial. Science, thinking, and the preference of fact over fiction seem to have suddenly become anathema in some quarters. System I-style of weak & fuzzy thinking has suddenly come into vogue in places where one would not expect it. Very strange.]

What you should know to get a career in data?

So people ask me, “What should you know to get a career in data?” at a minimum possible you should try to learn SQL and things that are SQL-like as well as NoSQL databases these days. Now, people ask, “What if you only want to learn one programming language? What should it be?” The lowest common denominator for analysts these days is VBA. This is the programming language that is built into Excel….

Article continues next page.


Article continues from previous page.

 

Careers and data science

End of transcript excerpt. For the rest of the transcript or talk, you’ll have to watch the video (which is fully closed-captioned for those needing machine translations).

Remaining topics include:

• Computer languages and software packages to know for Wall Street and Data Science careers.
• If you only want to list one programming language on your resume, which should you list for Wall Street careers and jobs?
• Which college and graduate degrees to get, what college skills pay the most
• Different career paths. What if you aren’t good at quantitative (math) skills?
• How your personality affects what career path you should take.
• Additional career advice
• Why different types of professions have very different income distributions (The Black Swan discussion)
• Startup incubators and accelerators: what do they do, and are they worth it?
• The Internet of Things, inexpensive/ubiquitous computing, and the future of data science and computing
• And much more in the video!

 

 

Further Reading

1. Kahneman, Daniel (2011). Thinking, Fast and Slow. New York: Farrar, Straus and Giroux, 1st ed., ISBN 0374275637
2. Silver, Nate (2012). Signal and the Noise. New York: Penguin Press, 1st ed., ISBN 159420411X
3. Lewis, Michael (2003). Moneyball: The Art of Winning an Unfair Game. New York: W. W. Norton & Company, 1st ed., ISBN 0393057658
4. Brad Pitt, Robin Wright, Jonah Hill, Philip Seymour Hoffman, et al. (2012). Moneyball. Blu-Ray. Directed by Bennett Miller. Los Angeles: Columbia.
5. Mayer-Schönberger, Viktor and Cukier, Kenneth (2013). Big Data: A Revolution that will Transform How We Live, Work and Think. New York: Hodder & Stoughton, 1st ed., ISBN 1848547900
6. Pape, Robert (2005). Dying to Win. New York: Scribe Publications, 1st ed., ISBN 1920769579
7. Taleb, Nassim Nicholas (2007). The Black Swan: The Impact of the Highly Improbable. New York: Random House, 1st ed., ISBN 1400063515