Are you curious about a career in data science? Are you looking to join a company that has a focus on machine learning and uses technology to help save lives? Well then, you’ve come to the right place.

I’m sure you’ve seen that there are plenty of data science job openings, yet it can seem impossible to land a job in the field, especially for those early in their career. Here I will give you some tips on how to get in the game with a focus on the machine learning branch of data science.

The Problems With Today’s Job Market

I believe there are two main reasons why it’s difficult to secure a career in data science. First, there’s the big catch-22: in order to get a job you need experience and in order to get experience, you need a job. It is a little bit more extreme in data science since it is a relatively new field and there are fewer mid-career and senior practitioners.

The second problem is more unique to data science. Since it currently has enormous popularity and a reputation of being a magical answer to every business problem, a lot of companies are eager to hire a data scientist — or even an entire team of them. Often there is little to no plan or even need for a data scientist, but they still feel pressure to hire one. Alternatively, they might have a need for one but lack any infrastructure in place to support a data science team. Such job descriptions have completely unreasonable requirements, and yet nothing specific. And just like there are questionable job postings, there are a lot of people who are trying to enter the market while inadequately prepared. Below are a few tips on how to stand out from the crowd.

Tips for Landing a Career in Data Science

Even if you have a relevant degree and a few internships under your belt, it might still be hard to land a job in the current market. However, below are a few tips that can certainly help!

  • Join an organization where it’s possible to eventually transfer into a data science role. Find a company that already has a strong data science group or a high potential for machine learning projects, even if your first role is not in data science. Perhaps you join as a business analyst, software engineer, or QA analyst, and then prove yourself by getting involved in machine learning and data science projects and showing initiative. Most companies are eager to transfer an internal employee to a data science position rather than hire someone new.
  • You don’t need a data science degree to become a data scientist. You read that right. While it certainly wouldn’t hurt to have a data science degree, it’s not always necessary. In that same regard, I’ve found that online data science courses and boot camps have been immensely popular, however, as a hiring manager, I don’t see strong candidates after just a boot camp or a few courses. In some way they are the worst of both worlds: the fast-paced all-encompassing curriculum barely provides even the basic theoretical base and a cookie-cutter capstone project does not resemble anything remotely comparable to a project on a data science job. Instead of trying to pick up all too many skills at the same time, I suggest starting off learning the absolute basics of machine learning, statistics and visualization, and then focusing on building strong programming skills. If you do not have proper computer science education, you might want to take on extensive online classes and write a showcase programming project. Such focused effort will pay off more than a dispersed chase after every single data science skill at once. A candidate who can program well and is curious about machine learning will have a significant edge in the modern data science market over a candidate who knows some machine learning and statistical concepts but is a weak programmer. Having general-purpose programming skills create the strongest foundation for future data scientists. Focusing on machine learning applications within programming is also very advisable.
  • Stay flexible! Data science is not just machine learning. In fact, data science has a lot of subfields. It’s always in flux and changing. Keep learning on your own, follow the current trends, keep applying, and keep trying different things. If there is one truth about data science it’s that it does not have one straight path that leads you to a career. Find your path — one that might look backward or too challenging at first could end up bringing you to a rewarding place.

My Journey to Data Science: from academia to Airspace

When I was starting out in the field, finding an internship in data science was my way “in.” My internship was not only a ton of fun but also opened many doors for me. However, over the years in the industry, I became very suspicious of the companies who only thought they needed a data scientist, but had no clear plan for one, no roadmap, and were looking for a “unicorn” who could come in and solve all their problems.

When I was being recruited to join Airspace as the first-ever data scientist, I was questioning whether they even needed a full-time data scientist. After learning more about the company and the technology, I realized that they actually needed an entire data science team!

Airspace is a time-critical logistics company powered by machine learning and automation. It is the perfect place to work on multiple exciting and challenging data science projects and see how they transform the entire company. Being a mid-sized, rapidly growing start-up, Airspace provides an opportunity to work with just about every department, from design to engineering, analytics, and operations. The data science team is building exciting tech that makes the company faster, more scalable, and capable of relying on accurate machine learning-powered decisions.

One of the best things about Airspace is that we practice “benevolent data science.” Many data scientists work with personal information — think of the social media giants and their “big brother” vibe. At Airspace, there’s zero “creepy” factor because we do not work with personally identifiable data, meaning we aren’t selling personal data or using data against our customers — instead, we use data to help save lives and expedite logistics. And while we don’t work with big data — more “medium data,” I would say — it is best to start your career (via an internship or a new grad position) in this environment, to make sure you understand machine learning and data science core concepts first. Learning to work with big data and the tools to scale up is always easier when you know exactly what you are scaling up to.

How my current company hires

We have ambitious, yet very well outlined plans to grow and hire across all experience levels in 2021, from more senior-level data scientists to summer machine-learning interns.

At Airspace we hold an unusual point of view: we welcome junior data science candidates. While they might be less experienced than their senior counterparts, they are often way more enthusiastic, eager to learn, and have an amazing ability to mold themselves into a specialist that the company needs.

If you are an ambitious, smart person with enough skill to be able to independently write great code and have an insatiable thirst to learn more about machine learning, data science, and engineering — the right companies looking for you! Your background, experience, and education aren’t a deal-breaker — as long as you are the right match, you will be hired!

Browse all of Airspace’s job openings here.

AI manager at Airspace Technologies and a mother of two.

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