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I Learned These 3 Things, And Then I Got Hired As A Data Scientist

phd at interview for data scientist job

Contributing Author: Shobeir K. Mazinani

Data science has changed my life.

That’s not an exaggeration.

Since becoming a data scientist, recruiters have invited me to apply for positions in their own organization.

It wasn’t always like this though…

It used to be the academic life for me – lengthy hours, little pay, and even less appreciation.

But I made a decision to join Cheeky Scientist and take my career into my own hands.

Now, it’s normal to receive multiple job offers, which I negotiate for higher salary and improved signing bonuses.

Does this sound like a luxury?

Like a distant achievement that you could never claim?

PhDs never need to feel that way.

Data science is a growing industry, and it needs PhDs just like you.

Why Data Scientists Are In High Demand

First and foremost, 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 like liaisons or application scientists.

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

This role involves analyzing large data sets and writing extensive reports explaining these complicated analyses in a lay language that decision-makers can understand.

Few people have the skills to do this, so there simply aren’t enough data scientists right now.

McKinsey and Company predicted that the demand for data scientists will only grow in the coming years.

They assert that there is a serious shortage of analytical talent in industry.

And any time there’s a shortage, supply and demand begin to define the market in a major way.

For PhDs, this high demand means lots of job opportunities.

Here’s some more good news: For data scientists, high demand also translates to a very high salary.

According to Glassdoor, the U.S. national average is $117K…

Ready To Become A Data Scientist? Here Are 3 Important Facts For PhDs

So “data scientist” is one of the fastest-growing positions for PhDs right now – that much is clear.

But what exactly is a data scientist?

This isn’t an industry job that can be quickly explained – data scientists work on different projects in almost all industries. 

The job title was only coined in 2008, so it’s not yet fully defined, and can vary a lot depending on the company.

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

Here are 3 major things you should know before you start applying for data scientist positions.

1. The core skills of a data scientist.

“Data scientist” can be used to refer to a range of different roles within companies.

Make sure you understand 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 PhDs from many backgrounds such as economics, life science, and even the social sciences, can make for excellent data scientists as long as they have some statistics knowledge.

The main question is: 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 how to extract patterns from data, you can find a data scientist position that is right for you.

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

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

Another thing to address is coding.

You can be a successful data scientist without learning any programming languages…

You just need to apply your analytic skills to large data sets. 

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

Learning a programming language will allow you to tackle big data, which are too big for traditional data-processing methods.

Remember that you’re a PhD, a doctor of philosophy. 

You’ve got knowledge and the ability to learn any language you need.

Plus, programming languages are getting easier to learn. 

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

Many programming languages 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.

2. Work data scientist langage 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

Knowing the definition of these concepts and having an idea of how to apply them will give you an edge during your job search.

These are the 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.

Do some research on the skills set of a data scientist and figure out how they overlap with your current skills set. 

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

3. Learn how your skill set can transfer to a data scientist role.

A lot of PhDs can struggle to find industry work because they don’t focus on selling their expertise.

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

As a PhD, you undoubtedly have skills that are sought after in industry.

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

By researching your desired positions, you will know how to present your transferable skills in an applicable way.

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… 

And to analyze consumer or user experience data once a product is on the market

You can see how mere familiarity with statistics would not be enough for a job like this – a background in chemistry or a similar field would be crucial.

And a background in the social sciences will come in handy in a role where you mine data for trends in human behavior.

Here are some important questions for prospective data scientists:

  • 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.

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

If you’re interested in 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 working toward this role, you’ll need to know the core skills of a data scientist. You’ll also need to work data scientist language into your LinkedIn profile and learn how your skill set can transfer to a data scientist role.

To learn more about becoming an industry data scientist, including instant access to our exclusive training videos, case studies, industry insider documents, transition plan, and private online network, get on the waitlist for the Cheeky Scientist Association.

Shobeir Mazinani, PhD


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