Data Science & Machine Learning Market Snapshot 2022

We recently completed a Data, Insight & Analytics Employment Guide using data collected via a detailed survey of industry professionals. We then combined the data with ADLIB’s extensive internal data and knowledge gained from operating within the Data sector to provide this snapshot of the current Data Science & Machine Learning Market.

As data & cloud technology increases adoption, more and more companies have accessible data to be able to dive into and use for data science, predictive analytics and machine learning. Early companies in this space have now completed their research into uses cases in their businesses and have effective machine learning algorithms, and are now deploying these into production. This is causing the market to shift slightly and demand for data scientists with strong software development skills has increased, driving up salaries for candidates with these skills.

A general trend we are seeing is the split between core data scientists, generally working on business analytics, customer & marketing and automation projects implementing predictive analytics solutions; and Machine Learning Engineers who specialise in either NLP or Computer Vision who can build core features for apps and new products. Companies that are later to adopt cloud data technology are now also looking to hire data scientists to research potential use cases for predictive analytics across the business.

Our thoughts

What I’ve noticed the most over the past 2 years in the data science space is the massive rise in remote working. This has opened up the potential employer market massively for candidates, and they are more willing to travel a bit further for roles, if they are hybrid (only 1 once a week or once a fortnight). This seems to be a preference of candidates preferring hybrid roles as opposed to fully remote, enjoying the social side being in an office and opportunity to collaborate where necessary and strong opposition to being full time in the office.

Salaries have risen in general, candidates outside of London have been able to look for salaries similar to the London market as a result of hybrid and remote working. In London there’s a lot of competition for local talent as well which has further increased salaries, as well as top candidates being picked up in remote roles for Silicon Valley Tech firms. In previous years candidates usually have moved roles for a salary increase of around 10% however on average, now it tends to be closer to 20%. As the market is so competitive, counter offers and candidates getting multiple offers has also been a clear trend which further increases salaries.

I’ve also noticed some key trends in the technologies being used by businesses, with a growing exposure to working with cloud-based platforms being a clear trend. More and more businesses are using GCP, Azure and AWS for storing their data and processing their models. This has led to a growing demand for machine learning infrastructure and deployment tools as they productionize their models and deploy into live production. We are seeing a rise in demand for MLOPs / DataOps – these emerging fields of experience adding a premium to candidates’ salaries. In terms of core software skills, we see 80% of candidates using Python with 15% using R and 5% using SAS/MATLAB commercially. Python is a key draw for most data science candidates in the market.

In terms of an interesting motivation that candidates are looking to move for is fear of being left behind. As technology and in demand skills moves quickly candidates are looking to move on to ensure their skills are a up to date as possible to ensure their skills are still desirable. If a company does not have any interesting use cases for machine learning and get candidates to work more on automation or basic predictive analytics, we are seeing candidates looking for more advanced and complex work.  

Salary is a key motivator as well, with candidates noticing their colleagues and friends moving for increased salaries. This is motivating them to move on as well if their current company is unable to offer an increased salary (this has also been increased as a result of the cost of living crisis). It’s well worth doing some salary benchmarking with your Data Science or Machine Learning teams to aid retention. Candidates have been used to the flexibility offered as a result of the pandemic in location and working hours etc, if companies are looking to move back to a 5-day week in the office model, it can also be motivation for a move.

There is a general shortage in vacancies for junior, entry level and graduate level candidates – as the number of graduates increases in this space there is clearly a lack in opportunities for entry level candidates with companies preferring to hire more established and experienced candidates. This can be a great solution for businesses struggling to hire experienced candidates, fresh graduates are often well qualified and skilled candidates and can help address the raise in salaries and competition in the market. One thing that can help massively when hiring data scientists is the speed in hiring, the more successful and easier hiring processes tend to be more streamlined with candidates move through the process in around 2 weeks, any longer than that tends to add the risk of another company moving faster and clients missing out. When asking a candidate to do a technical task, it’s well worth having an introductory conversation first to gain buy in from the candidate and have a greater chance that they’ll complete technical tasks and remain in the interview process.

Top 5 key tech skills for candidates:-

  • Python
  • R
  • AWS
  • Azure
  • GCP


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