Field Scientist Jobs

Demystifying Data Science: 4 Kinds of Data Science Jobs and 8 Skills that Will Get You Hired

Interested in landing a job as a data scientist? You’re in good company – a recent article by Thomas Davenport and D.J. Patil in the Harvard Business Review calls ‘data scientist’ the sexiest job of the 21st century.

But how can you get your foot in the door? Many resources out there may lead you to believe that becoming a data scientist requires comprehensive mastery of a number of fields, such as software development, data munging, databases, statistics, machine learning and data visualization.

Don’t worry. In my experience as a data scientist, that’s not the case. You don’t need to learn a lifetime’s worth of data-related information and skills as quickly as possible. Instead, learn to read data science job descriptions closely. This will enable you to apply to jobs for which you already have necessary skills, or develop specific data skill sets to match the jobs you want.

4 Types of Data Science Jobs

“Data scientist” is often used as a blanket title to describe jobs that are drastically different. Here are four types of data science jobs:

A Data Scientist is a Data Analyst Who Lives in San Francisco: All joking aside, there are in fact some companies where being a data scientist is synonymous with being a data analyst. Your job might consist of tasks like pulling data out of MySQL databases, becoming a master at Excel pivot tables, and producing basic data visualizations (e.g., line and bar charts). You may on occasion analyze the results of an A/B test or take the lead on your company’s Google Analytics account. A company like this is a great place for an aspiring data scientist to learn the ropes. Once you have a handle on your day-to-day responsibilities, a company like this can be a great environment to try new things and expand your skillset.

Please Wrangle Our Data!: It seems like a number of companies get to the point where they have a lot of traffic (and an increasingly large amount of data), and they’re looking for someone to set up a lot of the data infrastructure that the company will need moving forward. They’re also looking for someone to provide analysis. You’ll see job postings listed under both “Data Scientist” and “Data Engineer” for this type of position. Since you’d be (one of) the first data hires, there are likely many low-hanging fruit, making it less important that you’re a statistics or machine learning expert. A data scientist with a software engineering background might excel at a company like this, where it’s more important that a data scientist make meaningful data-like contributions to the production code and provide basic insights and analyses. Mentorship opportunities for junior data scientists may be less plentiful at a company like this. As a result, you’ll have great opportunities to shine and grow via trial by fire, but there will be less guidance and you may face a greater risk of flopping or stagnating.

We Are Data. Data Is Us: There are a number of companies for whom their data (or their data analysis platform) is their product. In this case, the data analysis or machine learning going on can be pretty intense. This is probably the ideal situation for someone who has a formal mathematics, statistics, or physics background and is hoping to continue down a more academic path. Data Scientists in this setting likely focus more on producing great data-driven products than they do answering operational questions for the company. Companies that fall into this group could be consumer-facing companies with massive amounts of data or companies that are offering a data-based service.

Reasonably Sized Non-Data Companies Who Are Data-Driven: A lot of companies fall into this bucket. In this type of role, you’re joining an established team of other data scientists. The company you’re interviewing for cares about data but probably isn’t a data company. It’s equally important that you can perform analysis, touch production code, visualize data, etc. Generally, these companies are either looking for generalists or they’re looking to fill a specific niche where they feel their team is lacking, such as data visualization or machine learning. Some of the more important skills when interviewing at these firms are familiarity with tools designed for ‘big data’ (e.g., Hive or Pig) and experience with messy, ‘real-life’ datasets.

Hopefully this gives you a sense of just how broad the title ‘data scientist’ is. Each of the four company ‘personalities’ above are seeking different skillsets, expertise, and experience levels. Despite that, all of these job postings would likely say “Data Scientist, ” so look closely at the job description for a sense of what kind of team you’ll join and what skills to develop.