career advise – Boris Gorelik

Career advice. Becoming a freelancer immediately after finishing a masters degree

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Will Cray [link] is a fresh M.Sc. in Computer Science and considers becoming a freelancer in the Machine Learning / Artificial Intelligence / Data Science field. Will asked for advice on the LocallyOptimistic.com community Slack channel. Here’s will question (all the names in this post are used with people’s permissions).

Thread #1 – Michael Kaminsky

This is a copy/paste from Slack.

Michael Kaminsky

LocallyOptimistic.com — a valuable source for data folks

My hunch is that it’s going to be pretty tough to get started, though not impossible. You’re probably looking at a pretty lean year or two to build up a reputation out of the gate

Michael Kaminsky

AI work in general is sort of difficult to contract out — so you might have more luck if you team up with a larger consulting outfit that can handle the other non-AI parts of the work

Michael Kaminsky

very rarely is someone like “we have all of the data pipeline and pieces working, now we just need to hire someone to do the AI part” — in general, the model-fitting part of an AI project is the easiest and fastest

Will Cray

Thank you so much for the info–it’s really helping me getting a better understanding of the landscape. Would your opinion, especially regarding that last message, change if the AI work I was doing was more custom model/agent design and training, rather than doing something quick like .fit() in sklearn?

Michael Kaminsky

ummm maybe? but like who needs custom model/agent design and training that doesn’t already have in-house data scientists working on it?

Michael Kaminsky

I don’t want to dissuade you, but my point is that you should think about who your customers are, and how you can market your services in such a way that it will provide them value. If you don’t have a clear map of the three concepts in italics, it could get rough — you can definitely figure it out by doing it, but that’s what you’ll be up against

Will Cray

You mentioned “larger consulting outfits” earlier–do you have any examples of organizations that you think could be a good fit?

Michael Kaminsky

so Brooklyn Data Company and 4 mile consulting are the two that jump to my mind — they specialize in BI and data but might want flex capacity into DS — they might be able to give you deal flow, etc. I know there are a number of others, maybe even folks in this channel

Thread #2 – Boris Gorelik

This is a copy/paste from Slack with some later edits and additions.

Boris Gorelik

Another thing to consider is what your risks are. If there are people who depend on you financially, starting with a freelance career might be too risky, especially if you don’t have 1-2 (better 2) customers who already committed to paying you for your services.

If you can afford several months without a steady income, or no income at all, being a freelancer might expose you to a larger variety of companies and business models in the market. I know some people who used to work as freelancers and gradually “adopted” one customer and moved to full employment. In these cases, freelance projects were, in fact, mutual trial periods where both sides decided whether there is a good fit.

Will Cray

I greatly appreciate this insight. I have little risks. I’m single, my living expenses are low, and I have some financial runway. Part of the reason I like the idea of freelancing is for the reason you stated–I’ll get to see many different business models. As an aspiring entrepreneur, I think diversity of experiences and exposure would be useful to me. I also think being flexible in how many hours I work will allow me to allocate more time to developing my own ideas/projects; although, I understand that’s a luxury that comes with being an established freelancer. I don’t have any clients currently. Do you have any recommendations for channels to try and garner clients?

Boris Gorelik

> As an aspiring entrepreneur, I think ….

Even though a freelancer and an entrepreneur’s legal status may be the same, they are different occupations and careers. An entrepreneur creates and realizes business models; a freelancer sells their time and expertise to fulfill someone else’s ideas. That’s true that most of the time (not always), combining freelance with entrepreneurship is easier than combining entrepreneurship with being a full-time employee in a traditional company.

> Do you have any recommendations for channels to try and garner clients?

Nothing except the regular facebook/linkedin/ but mostly friends and former coworkers and, in your case, teachers/lecturers. I got my first job interview via my Ph.D. advisor. Later, when I helped in hiring processes, I asked him and other professors to refer me to proper candidates. So yeah, make sure your professors know your status.

Calling bullshit on “persistence leads to success”

Did you know that J.K. Rowling, the author of Harry Potter, submitted her books 13 times before it was accepted? Did you know that Thomas Edison tried again and again, even though his teachers thought he was “too stupid to learn anything?” Did you know that Lior Raz (Fauda’s creator and lead actor) was an anonymous actor for more than ten years before he broke the barrier of anonymity? What do these all people have in common? They persisted, and they succeeded. BUT, and there is a big but.

People keep telling us: follow your dream, and if you persist, it will come true. You will learn from your mistakes, improve, and adapt, and finally, will reach your goal. I call bullshit

Think of the Martingale betting strategy. In theory, it works. Why doesn’t it work in practice? Because nobody has infinite time and infinite pockets. The same is right with chasing your dream. We need to pay for the shelter above our heads, the food on our tables, the clothes that we wear. Often other people depend on us. Time passes by. I had to be a party pooper, but some people who chase their dreams will eat all their savings and will either have to give up or declare bankruptcy (and then give up).

Survivorship bias

But what about all those successful failers? What we see a typical example of survivorship bias, the logical error of concentrating on the people or things that made it past some selection process and overlooking those that did not, typically because of their lack of visibility. We know the names Rowling, Edison, Raz, and others not because of their multiple failures but DESPITE them. For every Rowling, Edison, and Raz, there are thousands of failed writers, engineers, and actors who ended up broke and caused sorrow to their families.

So, should I quit?

I don’t know. Maybe. Maybe not. It’s your life, your decision.

Gartner: More than 40% of data science tasks will be automated by 2020. So what?

Recently, I gave a data science career advice, in which I suggested the perspective data scientists not to study data science as a career move. Two of my main arguments were (and still are):

The current shortage of data scientists will go away, as more and more general purpose tools are developed.
When this happens, you’d better be an expert in the underlying domain, or in the research methods. The many programs that exist today are too shallow to provide any of these.

Recently, the research company Gartner published a press release in which they claim that “More than 40 percent of data science tasks will be automated by 2020, resulting in increased productivity and broader usage of data and analytics by citizen data scientists, according to Gartner, Inc.” Gartner’s main argument is similar to mine: the emergence of ready-to-use tools, algorithm-as-a-service platforms and the such will reduce the amount of the tedious work many data scientists perform for the majority of their workday: data processing, cleaning, and transformation. There are also more and more prediction-as-a-service platforms that provide black boxes that can perform predictive tasks with ever increasing complexity. Once good plug-and-play tools are available, more and more domain owners, who are not necessary data scientists, will be able to use them to obtain reasonably good results. Without the need to employ a dedicated data scientist.

Data scientists won’t disappear as an occupation. They will be more specialized.

I’m not saying that data scientists will disappear in the way coachmen disappeared from the labor market. My claim is that data scientists will cease to be perceived as a panacea by the typical CEO/CTO/CFO. Many tasks that are now performed by the data scientists will shift to business developers, programmers, accountants and other domain owners who will learn another skill — operating with numbers using ready to use tools. An accountant can use Excel to balance a budget, identify business strengths, and visualize trends. There is no reason he or she cannot use a reasonably simple black box to forecast sales, identify anomalies, or predict churn.

So, what is the future of data science occupation? Will the emergence of out-of-box data science tools make data scientists obsolete? The answer depends on the data scientists, and how sustainable his or her toolbox is. In the past, bookkeeping used to rely on manual computations. Has the emergence of calculators, and later, spreadsheet programs, result in the extinction of bookkeepers as a profession? No, but most of them are now busy with tasks that require more expertise than just adding the numbers.

The similar thing will happen, IMHO, with data scientists. Some of us will develop a specialization in a business domain — gain a better understanding of some aspect of a company activity. Others will specialize in algorithm optimization and development and will join the companies for which algorithm development is the core business. Others will have to look for another career. What will be the destiny of a particular person depends mostly on their ability to adapt. Basic science, solid math foundation, and good research methodology are the key factors the determine one’s career sustainability. The many “learn data science in 3 weeks” courses might be the right step towards a career in data science. A right, small step in a very long journey.

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