How to become a Data Science contractor, in seven quick steps

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Data Science is an inherently rewarding career path to embark on, and will continue to be a highly regarded, competitive role in 2024, writes William Cokayne, recruitment consultant at staffing firm Leap29.

Data Science as a contractor in 2024 be like…

As Artificial Intelligence (AI) becomes more integrated into our daily lives, businesses are becoming increasing reliant on data-driven insights for their products and services.

So, data scientist contractors in 2024 have the opportunity to be part of an ongoing AI boom.

It’s worth noting that some data scientists working in permanent roles lament their lack of freedom to work on both different projects and the latest tech. The solution? To transition to work as a data science contractor!

The benefits of working as a contractor are numerous, but there are some necessary steps to take when making the transition if success is your goal.

Here’s my top tips on how to become a data science contractor, in seven quick steps.

1. Know your skillset versus the contract market’s requirements

What will probably come very naturally to a data scientist is to analyse their current skillset, and where it fits within the current IT contracting jobs market.

It is essential to identify what skills and talents you have, what’s in demand according to the latest online job postings and then -- which opportunities you are most interested in pursuing as a specialisation within data science.

2. Specialise

Specialisation and developing niche skills will make you a more attractive candidate as a contractor – and can help you charge more of a premium rate.

Becoming a specialist in your respective area of data science is imperative given the speed of technological advancement.

And it’s also massively important to carve out a niche in light of the relative abundance of “generalist” data scientists.

Whether you’ll specialise in statistical data science and analytics, Deep Learning or MLOps, it’s absolutely worth becoming an expert!

3.  Get some Data Science training /credentials under you belt

There are many ways to develop your skillset and begin to find your niche in a data science field.

The most common way is to pursue a degree from a university in a Computer Science-related area.

Highly respected universities can help here, such as MIT’s offering of its ‘Micro Masters’ course. It’s an online course currently starting at $1600 (£1,253), focused on the most cutting-edge trends and practices in data science.

Other online courses and ‘boot camps’ tend to offer short 3-6 month courses with a specialist focus on specific areas of data science.

Expanding your skillset and giving yourself a competitive edge in the skills market by getting a formal course to your name will make your transition to data science contracting a lot smoother – and you’ll have the credentials to prove your expertise and fall back on when the going gets tough.

4. Understand contracting's legalities

Another vital step in how to become a data science contractor is to understand the legalities and contractual obligations you must undertake.

Aside from handling tax obligations, data science contractors may be subject to various local regulations and licensing requirements. And these requirements vary from country to country.

It is important to familiarise yourself with these ‘rules and regs’ before starting to execute your freelance data science assignment.

Even investing in legal advice to ensure your obligations are met – and your client’s obligations are clear to you, can be very worthwhile. For example, you want to ensure your payment terms are clear, so there is no room for misunderstanding (intentional or otherwise) of your invoice ‘due date,’ and that project requirements or deliverables are unambiguous.

5. Be across the financial highs and lows

Being a contractor offers significantly more flexibility but there are some financial things to consider when making the transition if you’re reading this as a permanently employed data scientist.  

While you may not be subject to the same obligations that an employee has to consider; can enjoy higher pay (comparatively), and get to work on new and exciting projects every 6/12 months, there will invariably be a time in between contracts. And when you don’t work as a limited company data scientist, you simply don’t earn!

In addition to having this financial buffer, a financial safety net is a wise choice too if you’re going to be wholly freelance as a data scientist.

This is because you may find your client hasn’t renewed your contract and suddenly, you find yourself back on the market – which might not be as buoyant as when you were last ‘on the bench.’

6. Become a marketing guru/LinkedIn author

Perhaps the most undervalued weapon in a data science contractor’s arsenal is networking and the power of LinkedIn, whether that's as an author or just an avid networker!

Navigating the data science job market in 2024 can be tough, so having the right connections online and leveraging them can make succeeding as a freelance data scientist a lot easier.

Making sure your LinkedIn profile is up-to-date and accessible for prospective clients is imperative, particularly if you want direct-to-client opportunities, and particularly if you follow my advice above about specialisation as a data science contractor.

7. Go beyond the marketing norm with your new mindset switched on

 A mindset shift could be required if you were previously working in data science full-time.

As a freelance data scientist, you aren’t an employee that an employer is going to want to invest into long-term. Rather, your ‘job’ is to provide a service on a B2B basis, professionally and to the specification of an organisation (your client).

So get out there and sell yourself in this way! And ideally, go beyond the marketing norm like LinkedIn, by creating a portfolio of your data science work to demonstrate your expertise and real-world experience on GitHub. Keep it updated and contribute to it regularly, in line with projects in the area of data science you wish to work on or specialise in.

This will demonstrate to future prospective clients that you can address their requirements; even bridge their skills gap and positively for that upcoming interview or technical test, it reflects a mindset of continuous development. Good luck!

Written by

William Cokayne


Heading up Leap29’s Data and Analytics recruitment, William is a specialist data recruiter with experience working in the USA and Europe. Being a data specialist throughout his career, William has an extensive network of clients and candidates within data engineering/science and business intelligence/analytics. With a passion for mentoring and developing the careers of candidates who work with him, William strives to foster growth and success within the data community.

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