Data, Analytics, Machine Learning & AI
Data, Analytics, Machine Learning & AI recruitment
Covering all aspects of data engineering, analytics, and modelling recruitment
Specialists in data, analytics, machine learning and AI recruitment, we hear about all the latest developments and ensure companies hire the right people to stay ahead of the game.Get in touch
Data, Analytics, Machine learning and AI are essential to modern businesses’ success through harnessing the power of information. You need a partner as passionate as you are with the knowledge and tools to find the best talent with mutually aligned ambitions
Your skills have never been in more demand and imperative to industries’ success and the benefit to humanity. Without you, information is meaningless, but with you, we know the potential you have to make a huge difference. “Data is the new oil” – Clive Humby 2006
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It all starts with
Before we can identify how to create the perfect scenario for both candidate and employer, we have to understand your needs. For businesses, this means defining what you want to achieve with your new hire. For specialists, we’ll go over your experience, expectations and priorities for the future.
Sharpening the details
Finding a high-value match for both parties means everyone needs to bring their A-game. Throughout the search, selection and interview process, we ensure both businesses and specialists convey their value with clarity and honesty, so each understands and trusts what the other brings to the table.
Always there for you
At every stage, our team will keep you informed, answer any concerns and support you through any challenges that may arise. Our role is to anticipate obstacles, eliminate them before you know about them or calmly advise how we will handle them with as little stress as possible. And we will always be on hand for ongoing advice.
Frequently asked questions
The conventual way into data science, machine learning and AI is via university studying subjects such as mathematics, sciences, computing, and physics. Here, you will learn various data-related subjects and harness information for insights utilising various techniques and software and programming languages.
Alternatively, it is not uncommon to secure an internal move with your current employer and supplement yourself with data science, data analytical or computing accreditations with the industry knowledge you will already have to apply those analytical techniques. Breaking into this field is the hard part. If you love what you do and become an expert, the world is your oyster.
Like most career paths, the level at which you work is predominately determined by the level of expertise you have and in this area. Starting out as an analyst, engineer, or scientist is common. Then typically, between 1 to 3 years, someone would be promoted or move into a senior role, and then 1 to 3 years to a principal or lead. Some do, at this point, intentionally or unintentionally migrate into a mentioning or leadership position unless it doesn’t meet their goals or preferences. The subject matter has a strong part to place in terms of progression and path and some sector’s progression will be quick than others i.e. financial services v life sciences for the former being arguably quicker to progress in. But being a “manager”, AD, Director, VP are all possibilities depending on you and the organisation you work for.
A very common question and as a rule of thumb, the answer is “yes”. To add to this, it does depend on the stage of career and situation as it, broadly speaking it is an 80% yes! If you have no commercial experience, then 100% qualifications and certifications are imperative with the complexity of modern-day Data Science and more general Data Analytics.
If you want to change careers or sidestep again, the answer is yes, depending on the level of transferable skills. But accreditations alone could be a wiser choice. When it comes to new software or programming technics, gaining a strong foundation of what is new is always prudent.
The beautiful thing about the technology and life sciences sectors, in particular, is that with them, they are always those people, academics, and companies challenging the status quo and chasing innovation. We would not be where we are today after 50 years in data analytics if more scientific & technological intellects didn’t push the boundaries for greater knowledge and insight.
Data & analytics, ML and AI are breaking ground in numerous sectors now and in the more commonly related sectors in new and various ways and will continue to do so for many days, weeks, and years to come! So much so that almost anything can happen!
Depending on how much you know about this area, machine learning and AI will be part of our everyday lives in a huge way. It will be like imagining the world without modern-day smartphones. The technology we interact with daily will have ML and AI sitting behind an interface.
The most common “industries” with a higher degree of data engineering, data science, data analytics specialists would be life sciences (pharma / biotech), financial services, tech firms, consultancies, information agencies, & utilities. And that will continue to be the norm for the foreseeable.
Machine learning and AI this is evolving at a rapid rate with various technology innovations & advancements i.e. chatbots, autonomous vehicles, and diagnostics devices. Big Tech is and will continue to dominate recruitment growth with their development of new tools in such areas as logistics, e-commerce, aviation, manufacturing etc. We are in a similar era to the “app” where everyone rushed to develop mobile phone applications that worked better than the web-based platforms.