Data Scientists are hot.
It’s no secret just how in demand they are, with Indeed recently reporting that job posts for Data Scientists increased by 75% between 2015 and 2018. The 2017 LinkedIn US Emerging Jobs Report also noted that there are over 1,800 open Machine Learning Engineering positions on the networking site, with Machine Learning Engineer, Data Scientist and Big Data Engineer all ranking at the top of the emerging jobs list.
With such high demand for top tier-talent, there is a lot of competition amongst companies to find the right people for the job. You need to be confident in finding the right person and be able to convey your company as an attractive option for them in order to stand out as a company that Data Scientists will want to work at. It can be hard navigating this market, so here are some pointers to help you on your way.
First of all – is your company really ready for a Data Scientist? With high demand comes even higher salaries. In last years’ Salary Report 2017/18, we found that the average salary for a Data Scientist in the USA is $120,253. It’s not cheap to hire great talent! Glassdoor has also revealed that in San Jose, the average Data Scientist salary is over 18% higher than the national average – reaching up to $164,766. So, if you’re starting out building a data science team for the first time you need to make sure you are ready for it – ask yourself are you collecting large amounts of data that you are ready to scale? How will they fit in with your Business Intelligence and Engineering teams?
If you’re a smaller company and can’t stretch your budget for a full-time Data Scientist, or, you might just need a project getting over the line – you might find that hiring a contract data scientist would be more beneficial to you.
You need to set out what your data science goals are for your business. When finding the right person for the job you’ll need to know what they’re going to be working on, and what you want them to achieve for your business. Different projects require different skill sets and no two data scientists are the same! Align these goals to the individual’s credentials and experience. Ensure you have a list of specific requirements that’ll help the selection process. What machine learning tools do you need them to be proficient in? What programming languages are required? Do they need to have a specialism in a certain area of Data Science? Have they worked in the same, or similar industry, to yours before?
A lot of the time, if you work in a specialist industry, you may need to look for data scientists with specialist experience rather than a generalist. While a generalist can take on a more diverse range of problems, a specialist is able to hone in on more specific ones – for example, computer vision or natural language processing. When narrowing down the skill set for your desired candidate you need to decide which one would benefit your business the most.
Once you have the technical skills nailed down, you also need to assess soft skills and other attributes you look for in an employee. Culture fit is one of the most talked about work-life concepts right now and it’s incredibly important for people when considering a new job. Data Scientists are typically natural problem solvers, extremely curious and able to dig deep into data and derive valuable insights. In order to have business impact, they’re also going to have to be great communicators, able to tell the story behind the data to colleagues and decision makers at all levels. If you already have a team in place, how would another person fit into the dynamics?
Even when you’ve got all this perfected, there is still going to be competition amongst companies to hire these in demand candidates. If you need help with growing or building your team, we can help.
Matt Reaney, Founder