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Head of Data Science
Salary

£110000 - £111000 per annum + Yes

Location

City of London, London

Description

Head of Data Science, London United Kingdom - Remote working.

Reference:

25436/CW

Expires on
Charlotte York
Charlotte York
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Senior Data Scientist
Salary

£60000 - £70000 per annum + bonus, benefits

Location

City of London, London

Description

Do you want to work for a tech start-up that is using machine learning and data science to have a positive impact on society?

Reference:

10YUL

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Francesca Curtis
Francesca Curtis
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Machine Learning Engineer
Salary

Up to £100000 per annum + Yes

Location

City of London, London

Description

Machine Learning Engineer, London, United Kingdom.

Reference:

32807/CW

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Charlotte York
Charlotte York
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Financial Crime Analyst
Salary

£30000 - £40000 per annum + benefits + bonus

Location

City of London, London

Description

NEW! An exciting opportunity to join a fast growing B2B payments firm with global reach as a Financial Crime Analyst.

Reference:

30440/RM

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Rosalind Madge
Rosalind Madge
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DevOps contract (Remote)
Salary

£600 - £650 per day

Location

City of London, London

Description

DevOps contract AWS Automation Python Kubernetes

Reference:

JAC29030220

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Jonathan Cons
Jonathan Cons
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JavaScript (React) contractor - Remote
Salary

£550 - £600 per day

Location

London

Description

JavaScript contract 6 months Fintech Start UP React/Redux

Reference:

JAC28022020

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Jonathan Cons
Jonathan Cons
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Senior Software Engineer (Python)
Salary

£60000 - £70000 per annum + 10% Bonus

Location

London

Description

Work closely with other Software Engineering team members across multiple platforms and technologies

Reference:

23425/JB

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Jessica Boss
Jessica Boss
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Python Developer
Salary

£55000 - £70000 per annum + Benefits

Location

London

Description

Re-build of their lending infrastructure in scalable, robust Python microservices.

Reference:

78600/JB

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Jessica Boss
Jessica Boss
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Software Engineering Manager - Java
Salary

£80000 - £85000 per annum + 5% Bonus

Location

London

Description

come and support the CTO and manage their development team remotely from the UK.

Reference:

73089/JB

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Jessica Boss
Jessica Boss
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Software Engineer - Python
Salary

£30000 - £40000 per annum + Equity

Location

London

Description

Come and help build out and enhance software for a machine learning product

Reference:

34356/JB

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Jessica Boss
Jessica Boss
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Senior Backend Engineer - Python
Salary

£60000 - £80000 per annum + Equity

Location

London

Description

A great software engineering/data engineering/machine learning hybrid role!

Reference:

43652/JB

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Jessica Boss
Jessica Boss
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Full Stack Developer - Python & React
Salary

£60000 - £80000 per annum + Equity

Location

London

Description

Come and work across the stack for a talented and academic team!

Reference:

23424/JB

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Jessica Boss
Jessica Boss
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1972 result(s) found
Results per page 12 24 60

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

News

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

News

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