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Data Scientist Salaries

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  • Pragmatic Institute is the transformational partner for today’s businesses, providing immediate impact through actionable and practical training for product, design and data teams. Our courses are taught by industry experts with decades of hands-on experience, and include a complete ecosystem of training, resources and community. This focus on dynamic instruction and continued learning has delivered impactful education to over 200,000 alumni worldwide over the last 30 years.

data scientist salaries

data scientist salaries

By Michael Li

This article was originally published on January 26, 2018, on The Data Incubator.

 

At The Data Incubator (a Pragmatic Institute company), we’ve worked with hundreds of Fellows looking to enter the industry and our alumni work at companies including LinkedIn, Palantir, Amazon, Capital One, and the NYTimes.

Starting salary is one of the most common concerns for professionals entering any field, but as we’ve only been using the job title “Data Scientist” for about eight years it can be particularly challenging for prospective data scientists to find good information on their job market. LinkedIn and Facebook were the first to give employees on their data teams the title of data scientist, but now there are thousands of data scientists working across all industries alongside data engineers, data analysts, and quantitative analysts.

 

Salary Ranges

That variation in industry and responsibility understandably leads to a good deal of variation in salary. Data science salary bands fluctuate based on company size, team size, employee background, education, experience level, and many other factors. Data Incubator graduates generally see salaries in the $100,000 – 125,000  range with some salaries as high as $150,000.  Those numbers don’t include bonuses or equity, with some companies paying a higher base salary upfront, and others offering equity or stock options (hopefully) worth much more down the line. That’s just the beginning though. When considering an offer, we ask our Fellows to look at all of the other factors.

 

  1. Bonuses: These can be guaranteed, performance-based, or tied to other metrics and can range from the lion’s share of your annual salary to a small end of year bonus.
  2. Monetary Benefits: Many of our partners, especially the small companies, are generous with benefits such as paying a high percentage of healthcare coverage, 401k matching, transportation stipends, and unlimited time off. Those benefits (and the money you’ll save by having them) can add thousands of dollars to the total value of an offer.
  3. Non-Monetary Benefits: What is it worth to you to have a flexible work schedule? A shorter commute? The ability to telecommute? Those things aren’t written into an offer letter, but can make a huge difference, and in many cases may make a lower offer more attractive.

 

Contributing Factors

For Fellows graduating from our program, most base salary variations tend to be caused by two things: location and company size. Even though the majority of Fellows find jobs in bigger cities, there are still large cost-of-living differences between them. Companies in San Francisco will pay more than those in Washington DC.

With regards to company size, it’s easy to assume a smaller company or startup will automatically pay less, but we don’t always find that to be true. Large companies hiring several data scientists often have less room for salary or benefit negotiation (and are less inclined to negotiate when several people will be starting in the same position). Startup employees may be able to negotiate for more, especially with equity factored in.

 

Negotiating

Assisting with every part of the interview process also means helping with salary negotiations. There is an inherent tension in every salary negotiation because ideally, in the end, you’re going to be working with these people every day! And there’s no way around it, talking about money can be a little awkward. But it doesn’t need to be. If a company likes you enough to make an offer, they want to make it easy for you to feel good about accepting that offer. It’s important to be polite and professional, but firm and clear about your priorities and needs as you start a new position. We work with Fellows to understand both employer constraints as well as the best way to strike that balance.

The most important thing for our Fellows to keep in mind though is that while we certainly have a typical range, we’ve seen variation due to many of the factors mentioned above. There is no right number for a data scientist to be paid, but there is often a right opportunity. We work closely with each Fellow throughout the interview process to help them find it. That includes helping Fellows weigh every part of an employment offer – base, benefits, and bonuses – to help them land in the best possible place.

Author

  • Pragmatic Institute is the transformational partner for today’s businesses, providing immediate impact through actionable and practical training for product, design and data teams. Our courses are taught by industry experts with decades of hands-on experience, and include a complete ecosystem of training, resources and community. This focus on dynamic instruction and continued learning has delivered impactful education to over 200,000 alumni worldwide over the last 30 years.

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