Questions started popping up in the group, generally related to the best ways to make certain graphs in ggplot2 or how to handle certain dataframe manipulation tasks. I ignored. Then a question caught my interest:
I've been asked how much the hourly rate is for a freelancer and I have no idea. Could anyone provide a ballpark range in US dollars?Going against my gut, I engaged with LinkedIn.
BACKGROUNDThe question was interesting, and I contract on occasion so I thought I should type a quick response. I clicked through on the question, and saw the following first answer:
Hi M----, I am a consulting statistician with many years experience using R. I charge $40/hour for my services.Things just got weird. I'm more familiar with contract rates in the $150-$200 per hour rate. Very weird. What could be going on, and why is this person's rate so low? First a few facts on data science salaries:
- Median salary for a data scientist in the US is about $112,000 (~$55 an hour + benefits)
- Mean salary for a data scientist in the US is about $125,000 (~$61 an hour + benefits)
- Contract or freelance gigs, in any field generally pay more on a per increment (hour basis) than full-time gigs of similar veracity, for a couple of reasons:
- Contractors have less stable employment and thus earn more as a hedge against instability and opportunity costs (time spent marketing, invoicing, etc).
- Businesses using contractors incur much lower total costs than if they hired a full-time employee, so they are willing to pay a premium to keep it in pay per increment.
FIGURING OUT DATAA lot of people ended up responding to the LinkedIn question, so I had a fairly large sample of analysts and their self-reported charge for contract work. Because it was LinkedIn I could also click-thru to their resumes and determine their experience and education backgrounds, as well as other demographic factors. Per the post, I also referenced the website Upwork, which is kind of like an Uber service for Freelancers in various fields (to increase my sample).
In the data, I found three basic groups in the posts:
- Workers living overseas, especially in South Asia who were willing to work for sub-par wages ($30-40 an hour). This group seemed to charge less for a number of reasons:
- Businesses incur a bit of risk in working in these areas, which shifts wage rates down.
- The exchange rates and local cost of living in these areas make lower wages more tolerable to data scientists living in the area.
- Data science and IT jobs in that region (substitute employment) pay less than in the United States.
- Workers living in US with thin or non-existent resumes, willing to work for sub-par wage ($30-50 an hour). These individuals generally had strong educational backgrounds (some with PhD's), but had resumes that lacked any substantive analytical experience. I know that there are some people who have great education backgrounds, but are unemployable for various reasons (e.g personality, work ethic) so this isn't hugely surprising to see high-education people out of work. A couple reasons that these are likely under-market:
- Many may be willing to work under market because they are CURRENTLY unemployed and/or unemployable.
- Much of a data scientist's value in the workplace comes from solving real-world business projects. These workers realize that they have substantively less to sell themselves on to large employers.
- For unemployable individuals, there is very little potential for substitute employment (getting a real data science job) so they are willing to work temporary work for far less.
- Workers living in US or similar countries, with long resumes, who use similar contracting rates ($120-$300 an hour). These were generally US residents with a background similar to mine, having worked in analytics and data science for many years within large companies. These are generally people with $100K+ day jobs, that will contract in their free time, if a company will "make it worth it."
Though data science contractors with business experience in the United States are extremely well compensated, those with limited experience or working overseas cost a fraction of the price. These overseas and low-experience resources are likely best suited for projects of low-level coding or entry-level data science, however, as one responder to the original LinkedIn post said, they come caveat emptor which may be the reason many businesses pay higher rates for more guaranteed talent.