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Head of Data Science
Up to £130,000
This is your opportunity to work in one of the largest media companies in the world! This role will give you the ability to grow your own team of data scientists from the ground up and shape this capability from scratch.
In this team you will start off managing 2-3 data scientists and then grow this to about 20-30. You will get the unique opportunity to grow a big team and also remain hands on using tools such as Python, Spark and Cloud ML. In this role you will:
Key Skills and Requirements
HOW TO APPLY
Interested? Please register your interest by submitting your CV directly by applying to this advert.
US$160000 - US$200000 per annum
San Francisco, California
A leading San Francisco based Software company looking to hire an experienced data science with the ambition to lead a team.
Up to £100000 per annum + Yes
City of London, London
Machine Learning Engineer, London, United Kingdom.
£45000 - £50000 per annum + Yes
City of London, London
Data Scientist, London United Kingdom.
£100000 - £101000 per annum
City of London, London
Are you interested in contributing to a fast emerging, forward-thinking,data-driven team?Do you have an interest in improving customer experience worldwide?
With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.
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With Data-led roles leading the list in the World Economic Forum’s ‘Jobs of the Future’ report, it is no surprise that Data Science continues to be the main driving force behind a number of technological advancements. From the Natural Language Processing (NLP) that powers your Google Assistant, to Computer Vision identifying scanning pictures for specific objects and the Deep Learning techniques exploring the capability of computers to become “human”, innovation is everywhere. It’s unsurprising, then, that the world of healthcare is fascinated by the possibilities Data Science can offer, possibilities which could not only make your and my life better, but also save several thousands of lives around the world. To just scrape the surface, here are three examples of how Machine Learning (ML) techniques are being used to benefit our healthcare. COMPUTER VISION FOR IMAGING DIAGNOSTICS Have you ever had a broken leg or arm and saw a x-ray scan of your fracture? Can you remember how the doctor described the kind of fracture to you and explained where exactly you can see it in the picture? The same thing that your doctor did a few years ago, can now be done by an algorithm that will identify the type of fracture, and provide insights into how you should treat it. And it’s not just fractures; Google's AI DeepMind can spot breast cancer as well as your radiologist. By feeding a Machine Learning model the mammograms of 76,000 British women, Google’s engineers taught the system to spot breast cancer in a screen scan. The result? A system as accurate as any radiologist. We‘ve already reached the point where Machine Learning and AI can no longer just outsmart us at a board game, but can benefit our everyday lives, including in as sensitive use-cases as the healthcare industry. NLP AS YOUR PERSONAL HEALTH ASSISTANT When we go to our GP, we go to see someone with a medical education and clinical understanding who can evaluate our health problems. We go there because we trust in the education of this person and their ability to give us the best information possible. However, thanks to the rise of the internet, we’ve turned to search engines and WebMD to self-diagnose online, often reading blogs and forums that will convince us we have cancer instead of a common cold. Fortunately, technology has advanced to the point where it can assist with an on-the-spot (much more accurate) evaluation of your medical condition. By conversing with an AI, like the one from Babylon Health, we can gain insights into possible health problem, define the next steps we need to take and know whether or not we need to see a doctor in person. There’s no need to wait for opening times or to sit bored in a waiting room. Easy access from your phone democratises the process and advice can be received by anyone, at any time. DEEP LEARNING DRAWS CONCLUSIONS BETWEEN MEDICAL STUDIES Despite their extensive qualifications, even medical researchers can feel overwhelmed by the sheer amount of Insights and Data that are gathered around the world in hospitals, labs, and across various studies. No wonder it’s not uncommon for important Insights and Data to get forgotten in the mix. Once again, Machine Learning can help us out. Instead of getting lost in a sea of medical data, ML algorithms can dig deep and find the information medical researchers really need. By efficiently sifting a through vast amounts of medical data, combining certain datasets and providing insights, ML sources ways for treatments to be improved, medicines to be altered, and, as a result, can save lives. And this is only the beginning. As Machine Learning continues to improve we can expect huge advances in the following years, from robotic surgery to automated hospitals and beyond. If you’re an expert in Machine Learning, we may have a job for you. Take a look at our latest opportunities of get in touch with one of our expert consultants to find out more.
13. February 2020
During the last half of the past decade the importance of Data reached a level at which it was coined “the new oil”. This was indicative of a shift in the practices of individuals and businesses, highlighting how they now rely on something which isn’t measurable in gallons but in bytes. However, because we can’t physically see the Data we generate, gather and store, its easy to lose our connection to it. This is where NLP is comes into play. With the purpose of helping computers understand our languages, NLP (Natural Language Processing) gained an increased importance over the last couple of years. But, more than teaching a computer how to speak, NLP can make sense of patterns within a text, from finding the stylistic devices of a piece of literature, to understanding the sentiment behind it. So, with NLP set to become even more prevalent over the next decade, here are some of the ways in which it’s already being put to use: EXTRACTION Like an advanced version of using Ctrl + F to search a document, NLP can instantly skim through texts and extract the most important information. Not only that, but NLP algorithms are able to find connections between text passages and can generate statistics related to them. Which leads me to my next example: TEXT CLASSIFICATION This is fairly self-explanatory: NLP algorithms can parameters to categorise texts into certain categories. You’ll find this used frequently in the insurance industry, where businesses use NLP to organise their contracts and categorise them the same way newspapers categorise their articles into different subcategories. And, closer to home, it’s similar algorithms that keep your inbox free from spam, automatically detecting patterns which are heavily used by spammers. But NLP does more than just look for key words, it can understand the meaning behind them: SENTIMENT ANALYSIS Sentiment Analysis takes the above understanding and classification and applies a knowledge of subtext, particularly when it comes to getting an indication of customer satisfaction. For example, Deutsche Bahn are using Sentiment Analysis to find out why people are unhappy with their experience whilst Amazon are using it to keep tabs on the customer service levels of their sellers. Indeed, Facebook have taken this one step further and, rather than just tracking satisfaction levels, they are examining how users are organising hate groups and using the data collected to try and prevent them mobilising. With the advancement of Machine Learning and technological developments like quantum computing, this decade could see NLP’s understanding reach a whole level, becoming omnipresent and even more immersed in our daily lives: PERSONAL AI ASSISTANTS The popularity of using personal AI-based assistants is growing thanks to Alexa and Google Assistant (Siri & Cortana not so much, sorry). People are getting used to talking to their phones and smart devices in order to set alarms, create reminders or even book haircuts. And, as we continue to use these personal assistants more and more, we’ll need them to understand us better and more accurately. After decades of using generic text- or click inputs to make a computer execute our commands, this decade our interactions with computers need to involve into a more “natural” way of communicating. But these advances are not just limited to voice technologies. Talking and texting with machines, the way we would with friends, is increasingly realistic thanks to advances in NLP: CHATBOTS Since companies have realised that they can answer most generic inquiries using an algorithm, the use of chatbots has increased tenfold. Not only do these save on the need to employee customer service staff, but many are now so realistic and conversational that many customers do not realise that they are engaging with an algorithm. Plus, the ability to understand what is meant, even when it is not said in as many words, means that NLP can offer a service that is akin to what any individual can. If you’re interested in using NLP to fuel the next generation of technical advancements, we may have a role for you. Take a look at our latest opportunities or get in touch with one of expert consultants to find out more.
23. January 2020