It's easy to do well in data and analytics. It's one of the few job markets that favors employees more than employers. It comes with good pay, interesting work, and an abundance of remote work opportunities.
It also offers a wide range of industries to work in. Here is just a sample of the work people in data have accomplished:
Optimizing the messaging used in advertising
Predicting box office revenue for new movies
Identifying police officers at risk of using excessive force
Predicting hospital patients at risk of sepsis – a life threatening condition
All of these outcomes wouldn’t be possible if it weren’t for the armies of data engineers, data analysts, data scientists, and many others who build programs to track, transform, and analyze the world’s data.
Whether you love programming, analysis, or storytelling, there's a place for you in data analytics.
On December 21st, 2020, I started my dream job. I had finally made it into the most prestigious role in data and analytics – I was a bonafide data scientist.
It’s funny because one year and two months earlier, I thought about leaving analytics for good. I found myself stuck in the reporting trap – where it seemed like the only thing I could ever produce was another Tableau dashboard – despite my best efforts to move towards analysis work.
I didn’t feel like I had control over my career. It seemed like other people made those decisions for me. Every time I did something outside dashboard work, I’d hear the most horrific words you could ever hear at work: “that’s not really your job to worry about.”
And so I drew a line in the sand. I said no more. I got in this field to use statistics and programming to find cool insights and make real world predictions. That’s what I’m going to do.
I left my job – one that paid well for easy work – without another lined up – a move that concerned family and friends alike.
I devoted myself to finding the data scientist role I always wanted and managed to finally do it one year later.
Many Smart People Never Become Data Scientists – Why Did My Story Turn Out Differently?
It’s hard not to feel like one of the lucky ones. Many other people aspire to become data scientists. It’s not uncommon to see openings for data scientists to have hundreds of applicants.
That’s understandable. On average, data scientists make about $120k a year. The work-life balance is generally pretty good and the work is intellectually rewarding.
After landing my dream job, it didn’t take long for other aspiring data scientists to contact me on LinkedIn to ask me – ”how can I land this sweet gig?”
Well… it depends on who you ask. I knew many other data scientists and I had asked them all the same thing. There were some overlaps in their answers, but there was also a lot of variation.
Some created project after project with Kaggle datasets. Others offered to work for free for existing data science teams. Others simply started doing the work of a data scientist at their current job and got promoted. And others just applied and got hired right out of the gate! Without a graduate degree no less!
And that’s the harsh truth about data science. Every data scientist has a different story for how they landed their dream job. As much as I wanted to find the silver bullet – the one true path guaranteed to get me a data scientist job – there really wasn’t one.
I had to find my own path and develop my own strategies. And it’s my hope that you learn from my journey with your own job search.
My Strategies for Becoming a Data Scientist
In the year between my last job and my new job, I had three broad strategies:
Demonstrating aptitude to potential employers through graduate school and side projects
Using my best medium (writing) to develop a reputation among the data science community
Just because the steps are simple and straightforward doesn’t mean they’re easy. I had to apply myself academically – for the better part of a year – and take significant financial risks – all on the chance that my plan would work.
Let’s go into more detail about how I used these strategies.
It’s easy to find work in data science and analytics – provided you have the right skills. While the pandemic certainly slowed things down, the demand for data skills remains strong. People with professional experience in SQL, Python, and R will likely find themselves hearing from recruiters more and more – much like they did before the pandemic.
When work naturally finds its way towards you, you might find yourself asking – does networking really matter in data science?
Back when I was in college, I had a lunch meeting with the chief financial officer and founding partner of a hedge fund. I was in my junior or senior year back then and never had any prior relationship with this person. I had merely emailed him out of the blue.
My roommate at the time had told me that she was amazed that I could, without hesitation, email someone so successful and ask to meet them for lunch – and that the person would actually say yes!
I’ve continued this habit of reaching out and meeting with successful people ever since then. And by successful, I mean those who are well-respected or well-recognized in their field. That includes people in senior management, well-known public speakers, and authors of widely read books.
Reaching out to successful people like this makes some people anxious though.
That’s how my roommate had felt when I had met with the hedge fund partner. That would’ve made her self-conscious, she said. She knew it was a smart thing to do, but she had that nagging voice in her head that told her she’d somehow offend or annoy the person.
That voice would ask her, “why would someone so successful want to talk to me?”
It’s that question that keeps people from networking with those who would make all the difference in their career.
People often make machine learning out to be something from a science fiction novel. It sounds like teaching a computer to become sentient or something even more outlandish.
In reality, machine learning is usually just statistics, but it's a very specific use of statistics. In fact, my graduate course on machine learning was called “statistical learning.” (I must say that “machine learning” is certainly better branding though.)
But how exactly is machine learning different from other statistics?
There’s two key differences:
We care more about predicting an outcome and less about explaining how that outcome happens*
We write computer programs to improve those predictions
Everyone wants to be a data scientist, nowadays. It’s the “sexiest job of the 21st century,” according to Harvard Business Review. It's intellectually rewarding, pays well, and has consistently strong job growth.
The hard part, though, is landing your first data scientist job. It seems every company has that "two years experience" requirement, which makes it a challenge for newcomers to break into the field.
It took me five years after graduating with my undergrad degree to land my first data scientist job. I know others who transitioned into the role sooner, but I had to network and grow my skill set through graduate school before I was ever seriously considered for the job.
Like most analytics jobs, data scientist roles require a solid foundation in technology, with the addition of statistical expertise. It simply doesn’t matter how good of a strategist or communicator you are – you can’t succeed in a data scientist role without those skills.
Ironically, long-term success requires the opposite focus. After you mastered the technical and statistical expertise, you have to actively work to expand your soft skills. That will ensure your talents translate into a positive impact for your stakeholders.
There's a long list of things to learn, but it's helpful to simplify what you should focus on to achieve this goal. Down below are six specific skill groups that will help you either land a data scientist job or improve in your existing data scientist role.
Do you know what p-values really means? It turns out it's closely related to the concept of the "null hypothesis." Both of these concepts are frequently misunderstood by analysts, data scientists, and researchers alike.
In my latest Towards Data Sciencearticle, I interviewed Dr. Robert Montgomery, an assistant professor in my data science grad program. He helps explain what the null hypothesis really means, what the p-value tells us about its probability, and when we can claim causality in statistics.
Many people believe that the best way for data science to have more impact on an organization is for the data scientists to learn better storytelling skills. Are they right?
In my Towards Data Science article, I interview data scientist Jasmine Samuel and Brent Dykes, author of Effective Data Storytelling, about how to use data storytelling to make a bigger impact with data science.