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3 Ways to Break Into Data Science

With the popularity and demand for data scientists, and the well-documented shortage of skilled labor, more people are interested in data science as a career. Over time, I’ve gotten an increasingly large number of questions regarding how to start out as a data scientist. Like many other roles, landing the first job is typically the hardest, as having some experience under your belt is mandatory for many employers. This can create a vicious catch 22: how do you land your first job if they all require prior experience?

In this post, we’ll try to give you some advice — based on my own experience moving into data science several years back, and my current experience managing a data science department, interviewing dozens of candidates and reviewing hundreds of applications every year.

What’s your background?

People trying to start a career in data science can be split into three relatively distinct groups. It’s important to identify which of these you are most similar to, in order to figure out your best next steps.

1. The STEM career change — These are people with an advanced academic degree in a technical/scientific field who may already have several years’ work experience in an adjacent field. As the hype around data science has grown, they’ve started considering the option of transitioning. They typically have a strong mathematics and research background and can follow the linear algebra and statistics behind machine learning models. They have experience reading academic papers and aren’t intimidated by the formulas. Their transferable skills can help them become good data scientists relatively quickly.

2. The data science new grad — While it’s taken a few years, universities have started to address the industry demand and various faculties are now offering MSc programs in data science. Depending on the university, these might include the statistics, electrical engineering or industrial engineering departments. While these degrees can’t cover everything, they’re quickly becoming a gold standard for comprehensive data science training that a 3- or 6-month bootcamp can’t meet. A good program will also include a thesis (and publication/s), which gives the employer an opportunity to discuss your work in greater detail. Whenever interviewing new grads I deep dive into their thesis, making sure they understand alternative approaches, discuss why they made certain decisions and ascertain how they handle feedback. Due to the scope of a thesis, it’s usually a great way to evaluate how someone performs research and how well they really know their material, in a way that a Kaggle project they did a while back can’t achieve.

3. The optimist — This is someone who hasn’t gone through formal data science training nor do they have an extensive statistics/math background. They may have several years’ experience in data analytics within a specific vertical (finance, healthcare, etc) and want to complement their current skills to gradually move into a data science role. In the past, several people turned to me for consultation about their possibility to be a data scientist in fintech or some other specific vertical. While business acumen and experience in the vertical is important, this is the wrong mental mindset. The commonality between data science roles in various verticals is significant — the tools and algorithms solve generic mathematical problems, not vertical-specific ones. It’s easier to teach a good data scientist about a new domain than it is to train a business analyst with domain knowledge how to program, teach them statistics and machine learning. If you want to be a data scientist — you want to be just that, not a fintech data scientist.

If you’ve read this far, you probably know that there are a lot of online courses teaching everything data science related. While those courses are fundamental and deliver a ton of content, the vast majority try to give the most practical information as fast as possible. This typically means you’re going to learn a lot of machine learning models but only get the 30K foot explanation of how the algorithm actually works. Many courses won’t complicate matters with complex math so they can remain accessible to as big an audience as possible. While it’s definitely possible to train models and ‘do data science’ without understanding the intricacies of the algorithm, your capabilities will be limited.

How to break into data science

There are different ways to gain the minimal experience and knowledge to get your first data science position. When hiring for a junior position, the interviewer is going to look for a few things:

• Do you understand the fundamentals and theory of machine learning?
• Do you have the necessary coding skills (usually Python or R)?
• Can you demonstrate both of these points (e.g. walk the walk, not just talk the talk)?

As a candidate, you need to remember that the company’s loss function is asymmetric — hiring a bad candidate can have a much worse outcome than turning down a good hire. This means that companies are going to be cautious about taking risks on someone lacking a track record. You need to help the hiring manager as much as possible to demonstrate that you’re a low-risk and high-potential hire. This also means that your chances may be relatively low and you need to be emotionally prepared for a lot of rejections before getting an offer.

There are 3 main ways to gain the theoretical knowledge and expertise necessary for your first role, and they can be combined in various methods:

1. Masters Degree (with thesis) — As mentioned above, this is probably the gold standard for training today. While it can take 1–2 years, it is time well spent, especially if studying at a well known university. University pedigrees vary by location so it helps to understand what’s considered a good university in your vicinity.
2. Bootcamp — these typically run 3–6 months for full time immersive programs and much longer if they’re part-time. It’s best to pay close attention to the financial incentive the program has in regards to your future career. In some bootcamps it’s very straightforward — you pay for the training. On the other hand, the best bootcamps will also offer Income Share Agreements. In this scenario, after the bootcamp is complete you pay them a percentage of your salary only if it is above a threshold. The agreement is usually in effect for 2–4 years and is capped (e.g. 1.5–2X the upfront tuition cost). In Israel, ITC and Y-Data operate in this fashion and put a bigger focus on assisting their students land their first role. Other bootcamps work by keeping you on their payroll for 2 years following the training period, during which you work on a project for their client companies (e.g. Experis Academy in Israel). The bootcamp pays your salary directly and pockets the difference between it and their outsourcing fee, while typically offering the employee an exit clause (which covers their training expenses).
   Generally speaking, these bootcamps cover a wide range of topics and include theoretical machine learning knowledge, coding skills, statistics and (at least one) capstone project. As you can understand, different bootcamps have various levels of incentive to ensure your successful placement following their training. In some cases, it may be worthwhile to invest the time in a bootcamp, even if a fair chunk of the material is already known just to benefit from their assistance in landing the first position.
3. Online courses — the amount and quality of these courses has been transformational, enabling anyone around the world to learn from the top experts. The fact that such high quality content is now freely accessible to anyone has dramatically reduced the barrier to entry. At a very high level one can separate these courses into two types — intro level courses that try to cover a bit of everything in machine learning, and more advanced courses that dive deeper into specific areas. Several of the popular intro level courses can be completed in under 80 hours of dedicated effort. While this does require dedication (especially for something doing this on top of a full time job), it’s a relatively trivial time investment compared to many other high-paying professions (e.g. think of the time required to become a pilot, lawyer or doctor).  We agree that it’s a great course (it was the first one we took when transitioning to data science), but it was definitely not sufficient to qualify as a data scientist. You should be very wary of any course that claims to teach you the A-Z of ML. They might be a great intro into the field, but you should treat them as the first step in a long journey.

Compared to other high income, high demand professions, you don’t have to spend several years in medical school or log a thousand flight hours before you’re allowed to practice data science. While the demand for data scientists is high, most of that demand is for very skilled individuals who can demonstrate their value. You need to keep in mind that despite the lack of regulatory barriers, market forces still exist and companies won’t pay top dollar for someone with limited experience. More so, new data scientists require a lot of attention, training and support from more experienced data scientists. As the first few months are almost all investment by the company, it could take a year until a new data scientist’s contribution is back to zero. Paradoxically, this problem is exacerbated by the lack of experienced data scientists — they are really needed working on problems now and can only spend a certain amount of time training new people.

It’s not an easy path but it’s definitely rewarding. The world needs more great data scientists, so get to it.