Harnham
SEARCH
Shortlist
** DEFAULT signpostwidget.closetext - en-GB **

visit our American site

visit harnham.com/us
It looks like you are visiting Harnham from the US.

To get the most out of our site and experience tailored jobs and content for your region, we'd recommend visiting our US website. However, if you're looking to visit our global website please just close this pop-up and continue to harnham.com.

close

The global leader in
data & analytics recruitment

Trust the worlds biggest data and analytics recruitment company to support your hiring or job seeking needs

Search 1000s of jobs

SEARCH

Browse jobs via our Core Specialisms

DATA & TECHNOLOGY
RISK ANALYTICS
MARKETING & INSIGHT
DATA SCIENCE
DIGITAL ANALYTICS
VIEW ALL JOBS

Trust the worlds biggest data and analytics recruitment company to support your hiring or job seeking needs

Harnham About

We are Harnham

Harnham is one of the world’s leading  providers of recruitment services and advice  to the Data and Analytics marketplace 

We support global corporations through to ambitious local start-ups, so whether you need a Credit Risk Manager in London, a Data Scientist in New York, or a Head of Analytics in Frankfurt we can help you achieve your business goals.

More about Harnham

JOIN
Harnham

Take your career to the next level with Harnham in an environment that encourages growth and success. 

If you are a recruitment professional or a recent graduate interested in joining Harnham visit our careers page to find out more.

Careers at Harnham

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.

There’s Women At The Forefront Of Every Industry: A Q&A With Rachel Stuve

We recently had the opportunity to talk to Rachel Stuve, one of LinkedIn’s Top Voices in Data Science & Analytics, and a leading Director of Data teams. An expert in her field, Stuve has a wide breadth of experience. Having attended college in automotive-heavy Michigan, her first role was analysing the auto-industry at Chrysler. Shortly after she moved into local government, digitising and integrating their law enforcement processes before working on a state-wide Data-sharing initiative.  Most recently, however, Stuve has been focusing her efforts in Healthcare. While it might seem to many as a highly-specialised, inaccessible industry, Stuve disagrees. “It’s all about transferable skills,” she says. “You may be looking at different sets of Data with a healthcare provider but, essentially, the analysis follows the same principles”. Despite this, Stuve does admit that there are some hurdles to overcome, particularly when it comes to terminology.  “Admittedly the jargon does take some getting used to, and there is a lot of it.”  But the main differences are less scientific and more to do with infrastructure. Unlike like many Data-led industries, Health Insurers do not deliver directly to consumers. In fact, their main relationship is with Healthcare Providers.  “It’s not the same as getting a mortgage, you don’t approach your insurer to be provided with care. Your direct service is with the Healthcare provider, the hospital, or whoever, and it’s the insurer’s job to cover the payments. Part of the challenge is working out which providers offer the best value for money and, also, which ones offer quality care”. This means managing a team comprised of both Data Scientists and Epidemiologist, specialists who can better identify which treatments provide the most success, at the lowest cost. So, how can you get a team with different backgrounds and approaches to work in harmony with one another? “So much of a project’s success relies on agreeing to the right goals at the start. If you can get everyone to agree on what success looks like, be it a 10, 20% profit increase or whatever, you know you’re all working towards the same thing. Sure, you may have some debate around statistical conversations, but ultimately you’re all pulling in the same direction. "Stuve also stresses the importance of including the right people at the right stage of each project. Too often end-users are not included in the early stages of Data projects, leading to huge gaps in knowledge. Stuve notes: “If those who have true knowledge of what they need from a project are left out of the initial scoping, things will almost certainly be missed” In addition to her work in Healthcare, Stuve also invests in female-led start-ups with her work at Golden Seeds, something that is close to her heart.  “I love Golden Seeds. There have been numerous studies that show that female-run businesses produce higher returns, and yet they only receive a fraction of the investment that male-led businesses do.” She points to a recent article in the Harvard Business Review as to why this may be. According to the article, there is an inherent gender bias in the investment process where male entrepreneurs are asked about the potential of their businesses. Female entrepreneurs, on the other hand, were more likely to be asked purely risk-mitigating questions.  “People invest in optimism, so if you aren’t allowing an entrepreneur to sell you the dream, you’re far less likely to invest in them”.  Stuve also believes that there’s a perception that female-led businesses are less likely to be innovative: “I want to change the idea that these businesses are, for want of a better word, ‘girly’ and purely focused on clothes, food and retail. This is not the case from what I’ve seen, and women are at the forefront of all sorts of industries from biotech, to energy, to any number of specialisms”.  So, what does she look for when investing? “Sure I’m looking for an innovative idea that fulfils a business need, but I’m also looking to invest in the person. Are they realistic? Are they are strong leader? Do they know their own weaknesses and have they built up a team around them who can pick up where they’re not as strong?” “There’s also, unfortunately, a double-standard when it comes to the perception of male and female leaders. This means how they carry themselves makes a big difference, particularly if they’re looking for further investment in the future.” Stuve is well aware of the difficulties women face in male-dominated industries, having found herself as the sole female in many of her teams, increasingly so as she progressed into management. Fortunately, she sees light at the end of the tunnel: “Companies are beginning to see the value in broadening the diversity of their teams and there’s definitely been a shift in the corporate conversation around this.”  “Also, if you look for it, there is a fantastic network of women in Data out there. Reaching out tends to have this snowballing effect as well. You connect with one person, who introduces you to another, who introduces you to another, and soon you discover this amazing community of exceptional women”.  If you’d like to hear more from Rachel, you can follow her LinkedIn for regular updates and ideas.  For more information on the current states of Diversity in Data & Analytics, you can download our report on the subject here.  If you’re looking to build out your team or for a new opportunity, you can get in touch with one of our expert consultants or view our latest opportunities here. 

Talitha Boitel-Gill

Talitha Boitel-Gill

25. March 2020

Why You Should Always Be Learning In Data Science: Tips From Kevin Tran

Last month we sat down with Kevin Tran, a Senior Data Scientist at Stanford University, to chat about Data Science trends, improvements in the industry, and his top tips for success in the market.  As one of LinkedIn’s Top Voices of 2019 within Data & Analytics. his thoughts on the industry regularly garner hundreds of responses, with debates and discussions bubbling up in the comments from colleagues eager to offer their input.  This online reputation has allowed him to make a name for himself, building out his own little corner of the internet with his expertise. But for Tran, it’s never been about popularity. “It’s not about the numbers,” he says without hesitation. “I don’t care about posting things just to see the number of likes go up.” His goal is always connection, to speak with others and learn from them while teaching from his own background. He’s got plenty of stories from his own experiences. For him, sharing is a powerful way to lead others down a path he himself is still discovering.  When asked about the most important lesson he’s learned in the industry, he says it all boils down to staying open to new ideas.  “You have to continue to learn, and you have to learn how to learn. If you stop learning, you’ll become obsolete pretty soon, particularly in Data Science. These technologies are evolving every day. Syntax changes, model frameworks change, and you have to constantly keep yourself updated.”  He believes that one of the best ways to do that is through open discussion. His process is to share in order to help others. When he has a realisation, he wants to set it in front of others to pass along what he’s learned; he wants to see how others react to the same problem, if they agree or see a different angle. It’s vital to consider what you needed to know at that stage. Additionally, this exchange of ideas allows Tran to learn from how others tackle the same problems, as well as get a glimpse into other challenges he may have not yet encountered.  “When I mentor people, I’m still learning, myself,” Tran confesses. “There’s so much out there to learn, you can’t know it all. Data Science is so broad." At the end of the day, it all comes down to helping each other and bringing humanity back to the forefront. In fact, this was his biggest advice for both how to improve the industry and how to succeed in it. It’s a point he comes back to with some regularity in his writing. “It doesn’t matter how smart you are, stay humble and respect everyone,” one post reads. “Everyone can teach you something you don’t know.” Treating people well, understanding their needs, and consciously working to see them as people instead of numbers or titles—this, Tran argues, is how you succeed in the business. To learn and grow, you must work with people, especially people with different skills and mindsets. Navigating your career is not all technical, even in the world of Data. “The thing that cannot be automated is having a heart,” he tells me sagely. Beyond this, Tran stresses the need for a solid foundation. The one thing you can’t afford to do is take shortcuts. You have to learn the practicalities and how to apply them, but to be strong in theory as well.  Understanding what is happening underneath the code will keep you moving forward. He compares knowing the tools to learning math with a calculator. “If you take the calculator away, you still need to be able to do the work. You need the underlying skills too, so that when you’re in a situation without the calculator, you can still provide solutions.” By constantly striving to collaborate and improve, Tran believes the Data industry has the best chance of innovating successfully.  If you’re looking for a new challenge in an innovative and collaborative environment, we may have a role for you. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more. 

William Reichert

William Reichert

19. March 2020

Coronavirus Update: What to expect from Harnham

As we learn more about COVID-19, we want to inform you of the proactive measures Harnham have taken to ensure the health and safety of our employees, while continuing to provide the best possible service to you.  The majority of our service offering will be unaffected by the current situation. All staff are continuing to work remotely and are on hand to support you, although you may experience slight delays in communication or find our phone lines busy. In these instances, we'd ask that you contact the member of the Harnham team that you were last in contact with directly. If you need to find their details, you can contact them via their online profile. Alternatively, you can also contact us via our social media channels and directly via email to our main inbox (UK/EU and USA).  Our Operations and Technology team have been working around the clock over the past weeks to ensure that we are able to continue running processes virtually. This has ensured that we are able to provide our clients with virtual meeting spaces, alongside the opportunity to conduct video interviews and calls without the need for face to face interaction.  We are working with a number of businesses who are continuing to hire, supporting them as they begin putting in place alternative processes. We will be in contact with all candidates who are currently in any process to update on the current situation or any change to process.  If you are currently looking for a new role, all our open vacancies have been updated on our website which you can view here.  In the coming weeks our Marketing Team will be running a number of events such as webinars and online Q&A sessions. I would advise that if you are not already following us on Social Media (Twitter and LinkedIn), that you do so to ensure you don’t miss these. We are also working to provide a range of comprehensive guides covering the challenges that you may face in the current climate.  I’d also like to add, if you have yet to take part in the Harnham 2020 Salary Survey please take a moment to do so, we will be extending this for a further two weeks due to unprecedented demand. All those that take part will be the first to receive a copy of the report.  In the meantime, we're running as close to business as normal as we possibly can, and are still here to support you with any hiring or job-seeking needs. We hope that you are able to look after yourself through this trying time and we look forward to working closely together again when normality returns.  

David Farmer

David Farmer

19. March 2020

How Computer Vision Is Streamlining Manufacturing

Since the Ford Motor Company first introduced the assembly line for car production, automation has been part of the manufacturing industry. Over 100 years later, Computer Vision adds another layer to streamlined processes. Industrial robots. Drones. Automation. With the adoption of AI technologies and its connective capabilities, we’re in the next age of Smart Manufacturing. Demand is led by supply and, as consumers demand more, manufacturers are constantly evolving to ensure their processes are efficient and safe. The implementation of machines allows them to make sure quality control measures are in place and catch issues before breakdowns occur. This verification of output far outpaces the human eye and opens up opportunities for more creative thinking.  Working Hand In (Robotic) Hand While there may still be some element of fear regarding machines taking over jobs, this isn’t the intent. Ultimately, the idea is for humans and machines to partner for more streamlined and efficient processes within the industry. The role of machines is to continue the automation of processes using image recognition, gathering insights from AI-driven Analytics solutions, and optimising operations across facilities. We continue to retain oversight of these processes, but are now also free to focus on higher-value tasks at the same time, allowing strategic and creative thinking to take the lead.  Computer Vision is playing a crucial role in the implementation of AI in manufacturing and its use is estimated to grow more than 45% by 2025. Why? Here are a few reasons: Quality inspectionPredictive maintenanceDefect reductionProductivity improvement Human-machine partnerships through the adoption of AI, cloud-based technologies, and Computer Vision are helping to prepare facilities to become networked factories. Not unlike the un-siloed Data teams working throughout a variety of industries, the factory will also link their teams. From design to supply chain, the production line to quality control; the coming years will see continued growth in the output and efficiency of today’s manufacturer. Looking Out For Bias However, there is one area in which Computer Vision remains lacking. Navigating visual images still contains within it a bias which can be detrimental to some production output use cases. Think cars, wearable devices, or uniforms. The biases and stereotypes found most often in Computer Vision algorithms are three attributes protected by anti-discrimination law; gender, skin colour, and age. To help combat these biases and make imageable visuals more easily identifiable, two computer scientists embarked upon a research project.  What they found was that not only were there biases in these areas but some visual clues still posed problems.  However, the images used to train Computer Vision technologies can determine the differences. Not just in people, but in landscape and objects as well. By crowdsourcing correct categorisations, automating image collection, and more aptly defining words to negate stereotypical phrasings, researchers are striving toward a bias-free image capture. Seeking Out Business Goals In the last few years, Computer Vision has made great strides in uniting technologies to streamline the manufacturing process. As researchers work to reduce bias in computer vision and AI, machines become ever more essential for meeting business goals. Factories with smart manufacturing systems can more quickly process inefficiencies with improved accuracy. In 2017, sales of Computer Vision and automation systems grew 14.6% over the previous year to $2.633 billion. All industries are noticing the benefits of Computer Vision as an essential system but, like the Ford Motor Company in the early 20th century, manufacturing looks once again set to lead the world in innovation.  Ready to take the next step in your career? Whether you’re interested in AI, Big Data and Analytics, Computer Vision or more, we may have a role for you. Check out our current vacancies or get in touch with one of our expert consultants to find out more.  

Charlotte York

Charlotte York

12. March 2020

recently viewed jobs