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Harnham have over a decade’s experience in partnering with some of the industries most exciting data-driven organisations and are at the forefront of the Data Science recruitment space.






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What We Do

We help the best talent in the Data Science market find rewarding careers.

Data drives business and, in the 21st century, the Data Scientist is the “rock star” of the technology world.  Companies today know insights are the way to a higher ROI, a healthier bottom line, and, ultimately, a loyal client and customer base. To stay ahead of the competition, companies must continuously look for new and innovative ways to extract insights from the large volumes of data they acquire. 

Our aptitude in matching the best talent with the best companies is second to none. Our clients range from FTSE 100 organisations to small start-ups looking to shake-up the status quo. If you’re looking for your next challenge in the Data Science industry then get in touch and take the first step on that journey.

how We Do it

We pride ourselves on keeping our pulse on trends across the Data Science industry.

Our dedicated Data Science consultants have immersed themselves fully in the market and are able to provide industry-leading advice. Whether you’re looking to make your next move, or a find a new member of your team, Harnham have a wealth of knowledge and will help you make the process as efficient as possible.

We pride ourselves on keeping our pulse on trends in the industry and offer educational programmes to help keep our candidate’s skills sharp.

What sets us apart?

We place considerable emphasis on getting to know you, your motivations and your skills.

We do this to ensure we only introduce you to companies that suit you. By taking the time to listen to and explore our clients’ briefs, we soon know whether candidates fit their culture or not.

As a genuine specialist in Data Science recruitment, we have developed long-standing partnerships within the marketplace. These relationships allow us to provide our candidates with access to the best opportunities in the sector.

If you are looking for that next career step in Data Science, let us help you find it.


As working from home becomes ever more common, you can now search Remote Data Science Jobs.

Meet the team

Latest Jobs

Data Science Manager
Salary

£80000 - £95000 per annum + Additional Benefits

Location

London

Description

Join one of the largest data driven companies, using their data to create innovative solutions, that drive change throughout the data science community.

Data Scientist
Salary

£36000 - £50000 per annum + Yes

Location

Brighton, East Sussex

Description

Data Scientist, Brighton, East Sussex, United Kingdom.

Data Scientist - Insurance
Salary

£65000 - £70000 per annum

Location

London

Description

An award winning insure tech start-up changing the landscape of the industry!

VP of Data Science
Salary

£80000 - £180000 per annum

Location

London

Description

Are you an experienced Data Scientist who is looking for their next challenge working for a global company building out their UK-based data team?

GCP DevOps Engineer
Salary

£500 - £600 per day

Location

City of London, London

Description

A chance to help build a cloud infrastructure in GCP and help transfer data to the front end

Head of Machine Learning
Salary

£60000 - £80000 per annum

Location

London

Description

This is an exciting new opportunity for an experienced Data Scientist to join a growing start-up in the Fashion-Tech industry.

Lead Data Scientist
Salary

750000kr - 840000kr per annum

Location

Stockholm

Description

You'll take ownership of business-focused Machine Learning and AI roadmap, and help identify opportunities for data science / ML value creation.

Data Scientist
Salary

£45000 - £70000 per annum + + Benefits

Location

London

Description

A fantastic opportunity to join a well established data science team, giving you the ability to advance your data science skills!

Search Relevance Engineer
Salary

£55000 - £80000 per annum

Location

London

Description

This is an exciting new opportunity for a Search Relevance Engineer to join a wider Data & Engineering team in a successful tech start-up.

MACHINE LEARNING ENGINEER
Salary

600000kr - 850000kr per annum

Location

Stockholm

Description

As the Machine Learning Engineer, you will work on projects across the whole enterprise - logistics and manufacturing - forecasting sales etc

Data Scientist
Salary

£70000 - £85000 per annum + flexible working, performance bonus

Location

City of London, London

Description

Do you want to make a positive difference towards revolutionizing the UK energy industry using advanced machine learning techniques?

Decision and Data Science Director
Salary

£140000 - £141000 per annum + Yes

Location

London

Description

Director of Data Science, London United Kingdom

Harnham blog & news

With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

Visit our Blogs & News portal or check out our recent posts below.

From Broken Data Pipelines to Broken Data Headlines

This week's guest post is written by Moray Barclay. Two things have caused the UK’s Test & Trace application to lose 16,000 Covid-19 test results, both of which are close to my heart. The first is the application’s data pipeline, which is broken. The second is a lack of curiosity. The former does not necessarily mean that a data application will fail. But when compounded by the latter it is certain. Data Pipelines All data applications have several parts, including an interesting part (algorithms, recently in the news), a boring part (data wrangling, never in the news), a creative part (visualisation, often a backdrop to the news), and an enabling part (engineering, usually misunderstood by the news).  Data engineering, in addition to the design and implementation of the IT infrastructure common to all software applications, includes the design and implementation of the data pipeline. As its name suggests, a data pipeline is the mechanism by which data is entered at one end of a data application and flows through the application via various algorithms to emerge in a very different form at the other end. A well architected data application has a single pipeline from start to finish. This does not mean that there should be no human interaction with the data as it travels down the pipeline but it should be limited to actions which can do no harm. Human actions which do no harm include: pressing buttons to start running algorithms or other blocks of code, reading and querying data, and exporting data to do manual exploratory or forensic analysis within a data governance framework. The data pipeline for Test & Trace will look something like this:    a patient manually fills out a web-form, which automatically updates a patient listfor each test, the laboratory adds the test result for that patientthe lab sends an Excel file to Public Health England with the ID’s of positive patientsPHE manually transpose the data in the Excel file to the NHS Test & Trace systemthe NHS T&T system pushes each positive patient contact details to NHS T&T agentsfor each positive patient, an NHS T&T contact centre agent phones them. This is a not a single pipeline because in the middle a human being needs to open up an editable file and transpose it into another file. The pipeline is therefore broken, splitting at the point at which the second Excel file is manually created. If you put yourself in the shoes of the person receiving one of these Excel files, you can probably identify several ways in which this manual manipulation of data could lead to harm. And it is not just the data which needs to be moved manually from one side of the broken pipeline to the other side, it is the associated data types, and CSV files can easily lose data type information. This matters. You may have experienced importing or exporting data with an application which changes 06/10/20 to 10/06/20. Patient identifiers should be of data type text, even if they consist only of numbers, for future-proofing. Real numbers represented in exponential format should, obviously, be of a numeric data type. And so on. One final point: the different versions of Excel (between the Pillar 2 laboratories and PHE) are a side-show, because otherwise this implies that had the versions been the same, then everything would be fine. This is wrong. The BBC have today reported that “To handle the problem, PHE is now breaking down the test result data into smaller batches to create a larger number of Excel templates. That should ensure none hit their cap.” This solves the specific Excel incompatibility problem (assuming the process of creating small batches is error-free) but has no bearing on the more fundamental problem of the broken data pipeline, which will stay until the manual Excel manipulation is replaced by a normal and not particularly complex automated process. Curiosity So where does curiosity fit in? The first thing that any Data Analyst does when they receive data is to look at it. This is partly a technical activity, but it is also a question of judgement and it requires an element of curiosity. Does this data look right? What is the range between the earliest and the latest dates? If I graph one measurement over time (in this case positive tests over time), does the line look right? If I graph two variables (such as Day Of Week versus positive tests) what does the scatter chart look like? Better still, if I apply regression analysis to the scatter chart what is the relationship between the two variables and within what bounds of confidence? How does that relate to the forecast? Why? This is not about skills. If I receive raw data in csv format I would open it in a python environment or an SQL database. But anyone given the freedom to use their curiosity can open a csv file in Notepad and see there are actually one million rows of data and not 65,000. Anyone given the freedom to use their curiosity can graph data in Excel to see whether it has strange blips. Anyone given the freedom to use their curiosity can drill down into anomalies. Had those receiving the data from the Pillar 2 laboratories been allowed to focus some of their curiosity at what they were receiving they would have spotted pretty quickly that the 16,000 patient results were missing. As it was, I suspect they were not given that freedom: I suspect they were told to transpose as much data as they could as quickly as possible, for what could possibly go wrong? Single Data Pipeline, Singular Curiosity: Pick At Least One To reiterate, the current problems with T&T would never have arisen with a single data pipeline which excluded any manual manipulation in Excel. But knowing that the data pipeline was broken and manual manipulation was by design part of the solution, the only way to minimise the risk was to encourage people engaged in that manual process to engage their curiosity about the efficacy of the data they were manipulating. In their prototype phases – for that is the status of the T&T application - data projects will sometimes go wrong. But they are much more likely to go wrong if the people involved, at all levels, do not have enough time or freedom to think, to engage their curiosity, and to ask themselves “is this definitely right?” You can view Moray's original article here.  Moray Barclay is an Experienced Data Analyst working in hands-on coding, Big Data analytics, cloud computing and consulting.

Guest Blog

Guest Blog

15. October 2020

Is Computer Vision at the Core of the New Normal?

Computer Vision is one of the fastest growing markets in Data & Analytics. While it was on a trajectory prior to the pandemic, the needs we have now have amped up the role Computer Vision plays in our day-to-day lives and businesses who want to keep up or get ahead are paying attention.  Unexpected Businesses Using Computer Vision Some unusual players leaning on these technologies are grocery stores. While some have pivoted to pickup and delivery, others have remained stagnant with yesterday’s shopping habits changed only to individuals in store wearing masks. For those who made the leap to the "new normal", they’re using things like shelf sensors and Machine Learning to automate ordering and determine best placement of a product. Though retail stores are no stranger to video analytics, the rise of Deep Learning and AI offer a more rapid analysis of video for real-time threat assessment. Teaching the machine to watch for crowding, erratic movement, or potential conflict allows for quick reaction or proactive measures to stop a conflict in play. Yet, behind all this Machine Learning and Computer Vision elements are people. Real live humans. And it’s their new normal which is a strong part of the world’s new normal as most everyone shifts and remains online, working remotely. Behaviours are changing and many businesses have differentiated themselves from others by staying ahead of the game.        Five Ways Businesses Are Moving Forward in the New Normal Remote work is here to stay. A jump of 18% of remote working after the pandemic is expected to remain key to many businesses. And nearly three quarters of executives, plan to increase their remote workers. Key components of this new change will be to bring onboard those with strong digital collaboration skills, ability to manage virtually, and reassess how goals and objectives are to be decided. How will businesses keep remote employees engaged, enthused, and feel part of the team when they could be miles or countries apart?Gig Workers as Cost-Saving Measure. As employees move out of office and online, gig workers are a go-to for businesses hoping to move forward and keep costs low. Performance management systems will need to be re-evaluated. After all, if the idea is to keep costs low (read: overhead), then how does the debate about whether or not to offer benefits fit in to the mix?Definitions are Changing. Whether the definition includes ‘critical skills,’ ‘critical role,’ or something similar. What these meant once are changing. Now, the focus is on how to encourage, mentor, or coach employees in professional development skills which can open up a variety of opportunities versus one set path to one set role.Keeping Track Virtually. Though most businesses tend to follow the model of ‘productivity and performance’ over simply hours worked, some organisations passively track their remote workforce. This keeping track can include timeclock software virtually managed to computer usage to monitoring communications. Several benefits of data tracking in this manner could be a boon to HR Managers as it could help to understand employee engagement. But it’s a fine line to traverse.Organisational Redesign Done with Efficiency in Mind. As everything from products to people move online, it’s more important than ever to ensure things like logistics, supply chains, and workflows are designed with efficiency in mind. Computer Vision AI models can help take these systems to the next level as things like grocery shopping, retail, and legacy businesses find their business must go online or pivot in the new normal to survive. In our recently released 2020 Salary Guide we discuss each specialism. What’s working. What isn’t. And how businesses can hire and retain top talent to keep their projects on track and their businesses running smoothly.If you’re interested in Data & Technology, Risk or Digital Analytics, Life Sciences Analytics, Marketing & Insight, or Data Science, check out our current opportunities. Alternatively, you can contact one of our expert consultants if you’d like to learn more. 

Nick Mandella

Nick Mandella

03. September 2020

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