How much is a freelance data scientist’s hourly rate? There is no simple answer and it doesn’t make much sense to talk about average rates. The main factors determining a freelance data scientist’s hourly rate are location, speciality and experience level, not to mention the data scientist’s business acumen and negotiation skills.

It can be hard to find out exactly what hourly rate a freelance data scientist can charge because freelance work is not always advertised and when it is, the rate is usually negotiable.

How do qualifications and experience level affect a freelance data scientist’s hourly rate?

Let’s imagine a number of scenarios. I am quoting some typical figures for UK- or US-based freelance data scientist hourly rates but the numbers would vary by location.

Novice freelance data scientist hourly rate

A novice freelance data scientist who is fresh out of university can probably use online marketplaces such as Upwork to apply for small freelance data science gigs, and will be competing with people of similar experience levels worldwide. The competition will include people in low-income countries and people who have learnt data science on a bootcamp course rather than a degree programme, and the clients may not always know how to distinguish qualified freelancers from unqualified, so there will be a race to the bottom on novice freelance data scientist hourly rates. You can read my post on how to become a freelance data scientist for more information about joining Upwork and other marketplaces, and what to expect.

Hourly rates are advertised on Upwork and most likely the novice freelance data scientist can charge an hourly rate of $50. Most of the gigs they take on will be relatively low-level, perhaps mundane tasks in Python rather than cutting-edge machine learning, and this is reflected in an inexperienced freelance data scientist’s hourly rate. However there do exist some highly-paid expert jobs, mainly under the “UK only” or “US only” filter, which will pay a market rate better suited to your location.

Moderately experienced freelance data scientist hourly rate

A freelance data scientist with a moderate amount of experience can charge a slightly higher hourly rate. Imagine a freelance data scientist with a Masters or PhD and a few years of experience working in small but unknown companies.

A moderately experienced freelance data scientist can use their connections and network on LinkedIn to find more challenging work. Perhaps they will have a degree of experience in a particular industry and they can leverage that. A moderately experienced freelance data scientist could charge an hourly rate of about $100.

I have found a few interesting freelance jobs simply from in-person and online networking. For example, a university alumnus might recommend a colleague who has an interesting problem in NLP and which might be right up my street. These kinds of leads can be very valuable when they come in.

Contractor freelance data scientist hourly rate

A freelance data scientist with a decade or more of experience can charge much higher hourly rates. Let us imagine a freelance data scientist who has been working in machine learning since before the term “data science” was widely used. This person has worked across a range of industries. This freelance data scientist prefers to work in comfortable long term contracts for a single client, alongside the client’s permanent employees. The rates for contracting are good and a contractor freelance data scientist can charge hourly rates up to $200.

In the UK it’s possible to get some feel for what hourly rates a freelance data scientist can charge, simply by looking at the advertised rates for contractor roles.

I’ve taken a sample of 54 data science contract positions in natural language processing which were advertised in London in 2018-2020 on a variety of marketplaces. I have taken the daily or hourly rates which were either advertised or stated by the recruiter when I spoke on the phone. You can see that there is a lot of variation but the contracted hourly rates averaged around $115/hour. In general, freelance data science contracts in natural language processing or computer vision tended to pay higher hourly rates than contracts in general data science, and the hourly rates are also higher than the rates for employment.

It should be noted that I avoided startups and small companies when choosing the advertised contract roles, so these are generally the higher paying roles on the UK market.

Expert consultant freelance data scientist hourly rate

Finally, there are the consultants. A consultant data scientist does not apply for jobs or gigs on any kind of marketplace, but rather uses networking and even direct sales pitches. This person operates effectively as a small company and may compete against small and medium-sized consultancies, and this is reflected in the consultant freelance data scientist’s rates.

The consultant may have two decades of experience, will have written a series of books and may be an in-demand speaker or lecturer. A consultant will help companies with their long term data strategy, rather than simply complete a pre-defined task. Large companies know to look for the consultant for difficult problems.

An expert consultant freelance data scientist does not have an explicit hourly rate, but charges clients on a per-project basis. They may waive fees if a project does not deliver the expected result. The sales process may involve a series of presentations and unpaid proof-of-concepts, and the consultant may enlist a sales representative to help with sales. Working from the fixed price charged to clients, the expert consultant freelance data scientist has an effective hourly rate of $500 and up.

How does a freelance data scientist’s hourly rate vary by location?

Location is a very important factor in determining a freelance data scientist’s hourly rate. In general, the USA, especially the West Coast, has the highest demand and highest rates. The geographical variation in data scientist salaries and rates is dampened slightly by the fact that a lot of work can be done remotely, however the rates still vary hugely between locations.

I was unable to obtain comprehensive data on freelance data scientists’ hourly rates between countries, so as a proxy I have used permanent data scientists’ reported salaries to estimate the geographical variation in four countries.

We can see that North America has higher rates than Europe across the board. Within countries there is also considerable variation, with cities paying considerably more. In addition, the wealth of a country is not necessarily a predictor of the hourly rate. Data science is a very important field in the USA, but less so than in, for example, France, which is a much more conservative country when it comes to technology.

I suspect also that larger countries such as the USA, UK and Germany tend to have more demand for data scientists than wealthy smaller countries, because companies in large countries have huge customer bases and larger datasets to work with. Within Europe I have definitely found a divide between north and south, with the UK, Germany, the Netherlands and Scandinavia having very highly paid freelance and permanent data science jobs on offer, while southern European countries such as Spain and Italy do not have such a high demand for data science services and consequently pay much lower freelance data science hourly rates.

How does a freelance data scientist’s hourly rate vary by speciality?

In recent years, demand has grown for data science specialists who can work with more generalist data scientists and analysts. For example, experts in natural language processing, computer vision, and deep learning libraries are in high demand, and can consequently charge hourly rates much higher than a data scientist who works with the basic toolkit in Scikit-Learn. I would venture to say that freelance data scientists in these niche areas could charge double the hourly rates of their generalist counterparts, although I do not currently have data to back this up.

Conclusion

There are many factors affecting a freelance data scientist’s hourly rate.

First of all, the data scientist’s location is an important factor, with many companies in the US preferring to hire US-based freelancers, even if the work is completely remote.

Secondly, freelancers with an in-demand speciality such as natural language processing can increase their hourly rates accordingly, as they do not need to compete with so many people.

Thirdly, the freelance data scientist must know how to negotiate rates, and find clients directly. If the freelancer uses marketplaces such as Upwork, the competition will force a race to the bottom on cost, and the freelancer will wish to avoid this.

The first time you take on a data science project yourself as a freelance data scientist, it’s tempting to think of the project breakdown like this:

25% data cleaning → 50% data science → 25% deployment

Of course, if you take on a project with this expectation, it’s likely to over-run. Deploying a data science model can take much longer than expected, and cleaning data can also be surprisingly complex.

I’ve seen a lot of tongue-in-cheek blog posts saying that the breakdown is more like this:

However, I think that there’s another major stage that people are missing, which is the stage of gaining a client’s trust, getting NDAs signed, and getting access to the data. I’ve seen projects where it took 6 months to get hold of the data from the client, for 1 month of data science work. I have also seen a fair number of projects fail for the same reason.

So my take on this is that the breakdown is more like this:

Why is getting hold of data such a problem?

As data scientists, we encounter two kinds of data:

• public data which is accessible to everyone. Examples include worldwide coronavirus statistics, weather data, the Titanic dataset.
• private data, which is held inside organisations.

A paid data science project will usually involve private data. For many companies, its customer dataset, or manufacturing logs, or user data, are the crown jewels. The data is fiercely guarded. You will need to sign an NDA before you’re allowed to look at it. The consequences of a data leak are severe (just ask Ashley Madison).

Companies don’t like to give data to outsiders, least of all to freelance data scientists. Even if the stakeholder in the business wants to share the data, several people would need to sign off on it, and it takes objections from just one of them to stall the project.

What can we do about it?

Data scientists have to accept that getting hold of data will always be an obstacle. The best way to mitigate the risk of data not showing up is to schedule a kick-off meeting, ideally a month before the project. Request the data from the client at this meeting, and follow up regularly. Make sure that the data is available before the first billable day of the project. This means that any blockers can be dealt with in a timely fashion.

Conclusion

A large and often overlooked obstacle to getting a data science project off the ground is getting data from the client. The process can involve lots of meetings, contracts and NDAs before you even begin to sign a contract for the work.

The best way to mitigate this obstacle is to anticipate it and to plan for 1 month of ‘waiting for data’. This month involves kick-off meetings with a client, NDA signing, and regular follow-ups. If this step is taken, the probability of success is much higher.

This article inspired a post on fastdatascience.com.