Preparing Yourself for a Job in Data Science, Part 3: finding a job

Preparing Yourself for a Job in Data Science, Part 3: finding a job

From Build a Career in Data Science by Emily Robinson and Jacqueline Nolis

You’ve got the skills, and you’ve got the portfolio — all you’re missing is the data science job!

In this article, we’ll focus on how to look for data science jobs. You’ll first learn all the places where you can find jobs, making sure you won’t unknowingly narrow your options. We’ll cover how to decode these descriptions to find out what skills you need (spoiler: it’s not all of them) and what the job might be like. Finally, you’ll learn how to choose which ones are best suited for you, using your knowledge about data science skills and company archetypes.



Finding jobs

Before worrying about crafting the “perfect” resume and cover letter, you need to know where to send them. Job boards like LinkedIn, Indeed, and Glassdoor are a good place to start your search. It’s worth looking at more than one website, because not all companies post on each one. If you’re part of an underrepresented group in tech, you should also look for job sites targeted specifically at you, such as POCIT and Tech Ladies, for people of color and women in technology respectively. The type of job you’re applying to might also influence where you look. There are job boards for specific types of companies like start-ups (AngelList) and technology (Dice).

If you’re not quite ready to start the job hunt, check out part 1 (attending a bootcamp) and part 2 (building a portfolio) of our series to help get prepared.

Make sure to browse widely. Data science jobs go by many names besides data scientist. Different companies use different names for similar roles, and some are even are changing what their titles mean. All the people who were data analysts one year might be data scientists the next, with no change in responsibility!

Some examples of titles you might encounter include:

  • Data analyst: This is more often a junior position and can be a great way to start in the field if you don’t have a STEM degree and haven’t done any data analysis for a company before. As we’ll discuss later in this article, you want to be extra careful with data analyst positions to make sure that the role involves programming and statistics or machine learning.
  • Quantitative, product, research or other non-data analyst: These roles have even more diversity than data analyst in terms of your responsibilities. You may be doing exactly the same type of work as “data scientists” at other companies, or you may be spending your days with legacy Excel spreadsheets.
  • Machine learning engineer: As implied by the title, these focus on the machine learning part of data science. They usually ask for a strong engineering background — if you have a degree in computer science or have been working as a software engineer, this could be a great role.
  • Research scientist: These positions often require a PhD, though there might be some negotiation room if you have a master’s in computer science, statistics, or a closely related field.

When you’re first starting your search, try searching for “data” on one of these job boards and spending an hour reading through job posts. This gives you a better idea of what industries are represented in your area and what types of positions are open. You’ll pick up on patterns that let you skim through new listings more quickly. Finding jobs which are a good match for you, rather than all the available jobs, narrows the field down to a manageable number. Don’t worry too much about the title — use the description to evaluate fit.

business-intelligence data data-science

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