Harnham
SEARCH
Shortlist

Machine Learning Engineer

City of London, London
Up to £100000 per annum + Yes
Apply Save job

Machine Learning Engineer

London, United Kingdom

Salary up to £65,000+ Benefits

Want to work as a Machine Learning Engineer, solving cutting edge solutions and powering change through Data Science, sign up here!

This is a chance to work on projects that will impact the real world and work towards transformations through Machine Learning.

THE COMPANY

This innovative company works with clients and organisations worldwide, join a collaborative team and work building ML and AI models to provide future solutions for clients.

THE ROLE

Train Machine learning algorithms using pre trained models, using ML to provide platform success:

  • Build transformer networks using pre-trained models
  • Work with NLP
  • Develop a platform that will operate worldwide
  • Problem solve on a daily basis
  • Analyse large data sets
  • Lead projects within the ML
  • Deploy fully functional models using ML models

YOUR SKILLS AND EXPERIENCE

  • Advanced degree in Machine Learning, Mathematics, Statistics
  • Several work experience in ML
  • PhD preferred
  • Ability to lead projects
  • Come up with innovative ideas
  • Experience leading projects
  • Experience leading a group of Machine Learning engineers

SALARY AND BENEFITS

  • Earning potential up to £65,000
  • + A number of benefits

HOW TO APPLY

  • Please register your interest by sending your CV to Charlotte York via the apply link on this page
Apply Save job
Send similar jobs by email
32807/CW
City of London, London
Up to £100000 per annum + Yes
  1. Permanent
  2. Data science

Similar Jobs

Senior Data Scientist
Salary

US$140000 - US$160000 per annum

Location

San Diego, California

Description

Looking for a high level Data Science opportunity in a cutting edge fintech company?

ADD TO SHORTLIST
Director of Data Science
Salary

US$200000 - US$250000 per annum + Competitive benefits and stock

Location

Salt Lake City, Utah

Description

A leading pharmaceutical company is looking to make a Director level hire within their Data Science and Computational Chemistry Team.

ADD TO SHORTLIST
Data Scientist
Salary

£55000 - £75000 per annum

Location

London

Description

This is an exciting new opportunity for an experienced Data Scientist to work for a leading consultancy working on client projects around London.

ADD TO SHORTLIST
Fraud Data Scientist
Salary

£70000 - £90000 per annum

Location

London

Description

This is a really exciting opportunity for a Data Scientist to work in Payments Fraud team for one of the worlds leading media services provider.

ADD TO SHORTLIST

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.

Why Businesses Need To Put Fraud Prevention Front And Centre

If Fraudsters are anything, they are opportunists. Once the first new stories about COVID-19 started running, it wasn’t long until they were joined by tales of fraudsters selling face masks and hand sanitiser, asking panicked customers to transfer money and then disappearing without a trace.  And it’s not the first time we’ve seen this. Fraudsters are notoriously wise to periods of heightened sensitivity and uncertainty, often preying on the vulnerable. The 2008 financial crisis saw an increase in email-based phishing scams and a decade’s worth of technological advancements means that Fraud remains a many-headed beast.  Add into the mix a change in working styles and environments, and many businesses are more exposed to potential security breaches than they have been in years. Now, more than ever, companies need to make sure their Data is well protected and secure. THE FIRST LINE OF DEFENCE If you’re part of, or leading, a Fraud Prevention team, there are a number of ways you can support your business and keep on top of the situation. Here are just a few: Increase and update your investigation capacity. This team are the front line of your business’ Fraud defence team, interacting with customers daily and spotting new scams. During an uncertain period, retention and team stability is key. These are the people that understand the day-to-day Fraud challenges you face and will be essential in fighting any future challenges.  Sharing Fraud Prevention knowledge is key. Throughout this crisis, trends will be evolving quickly and working collaboratively across teams, and even other businesses, is the best way to combat this. We consistently hear from Fraud Managers that the key to beating Fraud is to share information and knowledge. Despite this, there is always a hesitation amongst companies to admit that they have been a victim to an attack. Perhaps now is the time to change this. Invest in Machine Learning and real time updates for your Fraud defences. Fraud technology has moved on from script writing in SQL and rule changes. Businesses need a real time reactive response and now is an important time to be embracing new technologies. There are a number AI-driven off the shelf packages available or, for a more bespoke solution, a Fraud Data Scientist can create something internally. Educate your team. It may seem simple, but the Fraud team can play a crucial role in minimising any potential risk from human-error. Educating employees on the risks they may face when working remotely, or what scams they need to look out for, is one of the most effective ways of fighting Fraud.  PREPARING YOUR BUSINESS Success in the fight against Fraud isn’t purely down to the group of individuals that make up the Fraud team. As a business, now is the time to be making decisions that can help you stay ahead of the Fraudsters. Here are some considerations: Consider investing in tech as an your immediate response. Not just to bolster your Fraud defences (although there are plenty of vendors offering AI-based solutions), but also technology for your employees to keep work as normal as possible such a sharing platforms, DevOps technology and video calling networks. One of the best ways to block some of the vulnerability loopholes fraudsters are trying to exploit is to keep working habits as close to normal as possible as you move to a remote solution. Be transparent with your customers. Consumers are being incredibly savvy and noting how businesses respond to the pandemic in a way that could have a big impact when normality returns. But they’re also being more empathetic and are willing to understand difficulties. For example, shopping delivery service Ocado were open and transparent when their system could not initially deal with demand. Having communicated the difficulties, worked through their issues and gone the extra mile to let customers know how they can be supported in this time, the received minimal backlash. There is an understanding that we’re all in this together. Finally, if you have the budget, continue to staff up - particularly in competitive fields such as Data Science. A lot of top Data professionals are currently at home and much more accessible than they have been in a long time. With a number of ways to remotely interview and onboard both permanent and contract staff, if you are able to get begin conversations with them now, you’ll have an edge in what will be a very competitive market come later in the year.  If you’re looking to take your next step in the world of Fraud, we may have a role for you, including a number of remote opportunities.  Or, if you’re looking to expand and build out your Fraud team, get in touch with one of expert consultants who will be able to advise on the best remote and long-term processes. 

Rosalind Madge

Rosalind Madge

01. April 2020

News

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

News

Recently Viewed jobs