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Why You Should Pursue A Career As A Data Scientist

Data science changed everything for me.

Since becoming a data scientist, without even applying to a single other job, recruiters have sent me numerous invitations to apply for similar positions.

Recently, I heard from two companies — one of them was centered in Reno, the other in New York.

I actually ended up interviewing for both of these companies, and guess what?

I received offers from both of them.

So I created an Excel sheet and explained to the guys in NY that in order to match my current income, I would need $10k more than their initial offer.

They offered me the additional $10k I asked for, plus a $20k increase in the signing bonus — as well as enough to cover relocation and cost of living for a month!

Once the Reno company learned this, they increased their initial offer by $30k and added enough money upfront to cover relocation.

But here’s the kicker:

When I gave my 2 weeks notice to the company where I was currently working, they changed my title to “Data Science Manager”, increased my salary by $40k, and bumped up my bonus plan by 2.5%.

Are you getting the impression that data scientists are in high demand?

You’re right.

Why Data Science Roles Are Becoming Popular

It’s no surprise to anyone that data scientists dig through a lot of data.

While working for a company, they might collect data from in-the-field sales personnel or key stakeholders, such as liaisons or application scientists.

A data scientist position is very numbers-heavy, and it can be fairly writing-heavy too.

This role will involve writing extensive written reports that take analyzed data and communicate it to other personnel.

But data scientists are too few in number.

McKinsey and Company predicted that going forward, data scientists will be in high demand, and that there is a serious shortage of analytical talent.

Of course, any time there’s a shortage, supply and demand come into effect.

For data scientists, high demand translates to a very high salary: according to Glassdoor, the U.S. national average is $117,000.

How Much Do You Really Know About Being A Data Scientist?

So, it’s clear that “data scientist” is one of the fastest-growing positions for PhDs right now.

But what exactly is a data scientist?

This isn’t an industry job that can be quickly explained — there is a huge range of capacities in which data scientists can be employed.

It was only in 2008 that the job title was coined, so there are a lot of misconceptions about what kind of educational background produces a data scientist.

You might guess that a data scientist needs to be intimately familiar with statistical calculation and computer programming.

But in reality, lots of different categories of study can lead PhDs toward industry careers in data science.

If you’re thinking about becoming a data scientist, this blog covers the foundations of what to know, what to do, and what to expect.

Know what it takes to be a data scientist.

A word of caution: you need to be careful about the particulars of a “Data Scientist” position.

This job title can be used to refer to a range of different roles within companies.

Verify the responsibilities of the position and how they align with your academic background before you apply.

One thing data scientists often have is a strong formal background in statistics.

But some prospective data scientists have a firm grasp on statistics without PhD-level training.

Even biologists and social scientists may find themselves digging their industry niche in the data science realm.

Basically, the question is this: can you professionally analyze data?

If so, you may be a viable candidate for a data scientist position.

In corporate management, there is a shortage of people with deep analytics training.

To put it another way, most managers don’t understand “big data”.

If you know general data analysis, you can find your way into a management position.

It will depend on the exact nature of the position and whichever company is hiring, but data analysis is always Part 1 of the foundation.

Part 2?

You need to not only mine the data but use it to create actionable insights and results.

Industry is all about results, and a data scientist’s ultimate purpose is to help provide them.

You have to be able to predict how information will influence future decisions.

A good Data Scientist has to develop business acumen.

Learn data scientist lingo and work it into your LinkedIn profile.

If you’re looking for an industry career in data science, get comfortable with the relevant lingo.

Some examples include:

  • Machine learning
  • Data mining
  • Predictive analytics

Even just being aware of how to define these things can help you during your job search.

These are the kind of terms you’ll want to plug into your LinkedIn profile as keywords.

But while the professional setting sees a lot of these and other official terms, you may already know the core concepts.

For example, data mining is just examining raw information and looking for trends.

And, machine learning represents the process of AI as it teaches itself to find data more effectively.

As you get started investigating data science, look into the topic and figure out where there exists overlap between your skill set and the profession.

As a PhD, you may be surprised to learn that you’ve got at least a handful of common data scientist skills.

Teach yourself the tools of the data scientist trade.

You don’t actually have to be a computer programmer or web developer to work in data science.

But don’t underestimate the utility of statistical programming languages.

Python, for instance, is a great language to pick up.

You’re a PhD, a doctor of philosophy. You’ve got knowledge and the ability to acquire it.

Why not use your ability to pick up a little coding?

A lot of these languages are actually getting easier to learn.

Many of them are no longer “black box” things that only advanced computer programmers can learn.

Database query languages like SQL will also be extremely helpful. If you have a background in STEM, you’ve probably already dabbled in this.

In any case, some companies will probably be happy to teach you languages like Python or SQL.

What they won’t teach you is how to find trends in large swaths of data.

Then again, they probably don’t need to, as analysis is a PhD’s bread and butter.

Look closely at your skill set and figure out how to transfer it.

A lot of PhDs can struggle to find industry work because they aren’t focusing on selling a certain aspect of their expertise.

That is, they don’t pitch their transferable skill sets to employers.

As a PhD, you undoubtedly have skills with the potential for industry transfer.

If you know how to mine data for trends, you can adapt this to the nature of the job you want.

Let’s say you’re looking to work in skincare. As a data scientist, this could mean digging through a lot of screening data to find compounds for a better product. You can see how mere familiarity with statistics would not be enough for a job like this; a background in chemistry or something similar would be crucial.

If you research your desired positions, you can express your transferable skills to employers in an applicable way.

And, once a product is on the market, you might be looking at consumer or user experience data.

The social sciences would translate well into a role where you mine data for trends in human behavior.

For prospective data scientists, some important questions are:

  • Can you turn raw data into insights?
  • Are you able to teach and explain these insights to people with no scientific background?
  • Do you know how to communicate in a way that helps industry employers make decisions?

A major key is to communicate your findings to industry employers in a language they understand.

There is a shortage of data scientists right now, but not for lack of PhDs who understand how to look at data.

Knowledge of data visualization tools like d3.js will be helpful, but communication is a must-have skill for all data scientists.

Rather, too few PhDs know how to apply transferable skills and use their findings to fuel a company’s actionable results.

If you’re intrigued by data science, there may be an exciting career ahead of you. This role represents a fulfilling and lucrative niche in the working world. A lot of PhDs might be surprised to learn that they are already well-equipped to work in data science. It’s a broad category, so a lot of different PhD backgrounds are great first steps toward professional data science. And, if you think you’d like to get serious about adopting this role, you’ll need to know what it takes to be a data scientist, learn data scientist lingo and work it into your LinkedIn profile, teach yourself the tools of the data scientist trade, and look closely at your skill set and figure out how to transfer it.

If you’re ready to start your transition into industry, you can apply to book a free Transition Call with our founder Isaiah Hankel, PhD or one of our Transition Specialists. Apply to book a Transition Call here.

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Shobeir K. S. Mazinani, PhD, is currently enjoying his career as a Data Scientist. A former Computational Quantum Physicist, Shobeir’s rare expertise combines neatly with a fluency in Python and its constituent libraries, making him a sought-after professional in his growing field. He is a New Yorker with a black belt in Taekwondo, but his chief battle is strictly scientific: Shobeir’s analytical work helps pave the way toward safer traffic in the U.S.

Shobeir Mazinani, PhD

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