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** Machine Learning Research Engineer - Cutting edge AI Tech start-up - Cambridge ** ** PhD from Top University essential - £40,000 - £95,000 Salary **
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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.
Amazon has begun curating summer reading lists. How? Patterns. Facebook shows you ads for items you may have been searching for online. How? It learns from your browsing habits. Ever wondered how Facebook knows you were just looking at that pair of shoes or that particular guitar. The Data you feed it, feeds its brain. In other words, this is how Artificial Intelligence learns. Machine Learning. Whilst it can be disconcerting to know that a machine understands our buying habits, that’s not the only thing it’s used for. It’s also a pivotal tool in such areas as Bionformatics, Biostatistics, Computational Biology, Robotics, and more. What is Machine Learning? Ultimately, it’s a method of Data Analysis which helps to automate model building and is part of Artificial Intelligence. In other words, it helps to solve Computational Biology problems by learning from Data to identify patterns and make decisions with little human intervention. This helps scientific researchers learn about many aspects of biology. However, running a Machine Learning project can be difficult for beginners, who may experience issues when trying to navigate the information without making mistakes or second guessing themselves. This is one of the reasons a Computational Biologist might want to upskill with a course or two in Machine Learning for a more robust understanding of the information being learned and applied. The Good News and the Bad With each shift of industrial revolution, there has been one system which has made an indelible mark on our daily lives and the Fourth Industrial Revolution is no different. Just like we can no longer imagine factories without assembly lines, we can also no longer imagine not having Siri, Google Maps, or online recommendations. But, as exciting and as important as these things are, Machine Learning has become so crucial to our daily lives, so complex, it takes a technology expert to master it leaving it nearly inaccessible to those who could benefit from it. Why is Machine Learning Important? By building models to peel back the layers and discover connections, organisations can more easily and more quickly make improved decisions with little to no human intervention. Computational processing is both more affordable and more powerful. It’s possible to quickly scale and produce models which can analyse bigger and more complex data and there’s also a chance to identify opportunities and to help avoid any unknowns such as risk. Machine Learning is used in every industry from Retail to Financial Services to Healthcare. Here are just a few ways it has already transformed our world. Retail – Retailers are able to learn from their customers buying habits, predictive buying habits, how to personalise a shopping experience, price optimisation, and customer insights.Financial services – Machine Learning helps to prevent fraud and identify Data insights.Healthcare – Wearable devices allow for real-time data to assess a patient’s health. Medical professionals can also more quickly find red flags which can help improve diagnoses and treatment.Oil and gas – It cannot only help find where oil might be, but also predict refinery sensory failure, and streamline distribution.Transportation – Help to make routes more efficient and predict problems that could affect the bottom line. While humans can create at least one or two models a week; Machine Learning can create thousands. Ultimately, the goal of Machine Learning is to understand the structure of Data. As it learns to determine what Data is needed for its structure, it can be easily automated and sift through Data until a pattern is found. This is how machines learn. If you’re looking to take your next step in the field of Machine Learning, we may have a role for you. Take a look at our latest opportunities, or get in touch to see if we can help you take that next step.
04. July 2019
“Don’t judge a book by its cover”. We use this adage to remind ourselves to go deeper and to look beyond the superficial exterior. Except, sometimes, we can’t, or won’t. Sometimes, our perceptions are pre-programmed. Think family, peer pressure, and social influences. But what about computers? What do they see? In a digital landscape that demands privacy but needs information, what are the advantages and disadvantages of Computer Vision? The Good: Digital Superpowers Let’s be clear, Computer Vision is not the same as image recognition, though they are often used interchangeably. Computer Vision is more than looking at pictures, it is closer to a superpower. It can see in the dark, through walls, and over long distances and, in a matter of moments, rifle through massive volumes of information and report back its findings. So, what does this mean? First and foremost, it means Computer Vision can support us in our daily activities and business. It may not seem like it at first glance, but much of what the computer sees is to our advantage. Let’s take a deeper look into the ways we use Computer Vision today. Big Data: From backup cameras on cars to traffic patterns, weather reports to shopping behaviours and everything in between. Everything we do, professional to personal, is being watched, recorded, and used for warning, learning, saving, spending, and social. Geo-Location: Want to know how to get from Point A to Point B? This is where Geo-location comes in. In order to navigate, the satellite must first pinpoint where we are and along the way, it can point out restaurants, shops, and services to ease us on our way.Medical Imaging: X-rays, ultrasounds, catheterisations, MRIs, CAT Scans, even LASIK are already in use. Add telemedicine and the possibilities are endless. The application of these functions will allow faster and more accurate diagnoses and help save lives.Sensors: Motion sensors that only turns a light on when a heat signature is nearby are already saving your home or business money on your electric bill. Now, during a shop visit when you are eyeing an intriguing product, your phone may buzz with a coupon for that very item. Computer Vision sensors are now tracking shopper movements to help optimize your shopping experience.Thermal Imaging: Heat signatures already help humans detect heat or gas and avoid dangerous areas, but soon this function will be integrated into every smart phone. Thermal imaging is no longer used just to catch dangerous environments, it’s used in sport. From determining drug use to statistics and strategy, this is yet another example . The Bad: Privacy Will Forever Change Google is 20 years old this year. Facebook is 15. Between these two media tech giants, technological advances have ratcheted steadily toward the Catch-22 of both helping our daily lives, whilst exposing our data to our employers, governments, and advertisers. Computer Vision will allow them to see you and what you’re doing in photos and may make decisions based on something you did in your school or university days. We’re already pre-wired to make snap judgements and judge books by their cover, but what will these advancements do to our daily lives? Privacy will change forever. We document our lives daily with little regard to the privacy settings on our favourite social media apps. GDPR has been a good start, but it’s deigned to protect businesses and create trust from consumers, rather than truly offer privacy. So far, the impact on our privacy has been limited as it still takes such a long time to sift through the amount of data available. However, the time is coming soon, where we’ll need to perhaps think of a privacy regulation businesses, employers, and governments must follow to protect the general population. Fahrenheit 451, 1984, and Animal Farm were once cautionary tales of a far-off future. But Big Brother is already watching and has been for quite some time. Police monitor YouTube videos. Mayors cite tweets to justify their actions. And we, thumb through our phones tagging friends and family without discretion. Like every new technological advancement there are advantages and disadvantages. As Computer Vision becomes increasingly prevalent, we’ll all need to be aware of the kind of data we supply from to text to image. We can’t go back to the way things were, but we can learn about ourselves through the computer’s lens. And when it comes to computers and their capabilities, don’t judge a book its cover. If you’re interested in Data & Analytics, we may have a role for you. Take a look at our latest opportunities or get in touch with one of our expert consultants for more information.
25. April 2019
From Vinyl to Tidal; we all know that the way we consume music has changed. Technological advances have made Steve Job’s claim that he would put “1,000 songs in our pockets” seem antiquated, whilst Spotify’s algorithms serve us tracks that we’ll love before we’ve discovered them ourselves. But can the technologies that have brought us these advancements change the way we make music? Whether it’s leading to new instruments or creating a song without our input, Artificial Intelligence is a game changer. Make Some Noise Until recently, the best way to imitate a sound was by experimenting with the different settings on a keyboard. However, this is no longer the case, thanks to Google’s research arm Magenta. They’ve created the NSynth Super, an instrument that generates sounds based upon Deep Neural Network techniques. These algorithms allow the NSynth to not only imitate a sound, but consistently learn more and more about the specificities of that pitch, creating something closer to reality. Users can then combine those individual sounds to create something unique and entirely original. This is potentially just the beginning of a new wave of music, and in a decade’s time the NSynth could end up having as big an impact as autotune. Talking About AI Generation Whilst we’re still waiting to see the impact of instruments akin to the NSynth, machine-led compositions are becoming more and more commonplace. Using a Recurrent Neural Network (RNN), one can feed a model existing music and ask it to generate something new. By learning the patterns and rhythms of notes from a variety of compositions, the model should be able to output an original and melodical sequence. Although these may not be the most amazing tracks in the world, they do serve a purpose. Music production platform Jukedeck allows users to input their requirements for a piece of music (genre, temp, mood, length, instruments etc.) that can then be automatically generated using AI. Obviously these aren’t designed to be chart hits, but production music that can be purchased cost-efficiently for YouTubers, Short Films and other backing-tracks. Despite the fact that this remains the most common use of AI in music, some artists are looking to push this even further. Musician Taryn Southern, for example, has created an EP based purely on AI compositions generated using Amper Score. The platform generated a beat, melody and basic structure before Southern then rearranged and added lyrics too. Could this form of collaboration become the future of mainstream music? Rage Against the Machine Learning As with any change, AI’s interruption of the music industry is not without controversy, and there are those who believe that the human contribution is what makes music what it is. Indeed, there are still several limitations to what AI can achieve creatively. Despite a neural network’s success with creating original compositions, another’s ability to write lyrics was somewhat lacklustre. Despite being trained on a combination of lyrics (for structure), and literature (for vocabulary), its output was largely nonsense and included lines such as “I got monk that wear you good”. Perhaps, like Southern’s compositions, AI is best used as an accompanying tool. London-based start-up AI Music offer technology that ‘shape-shifts’ songs to adapt to the context in which they’re played. This could be anything from tempo changes to match a listener’s speed to remastering tracks to appeal to different moods and situations. IBM’s Watson Beat, on the other hand, creates compositions that naturally fit to the visuals of a video. In this context, as within many other industries, AI looks set to support our existing skillsets rather than replace jobs. Whether you’re looking to create collaborative technologies or revolutionise an industry, we may have a role for you. Take a look at our latest opportunities or get in touch with one of our specialist consultants to find out more.
07. February 2019
The Fourth Industrial Age is booming. Data Scientists are rock stars of the tech world and data science is considered the "sexiest job" of the 21st century. But, when you take away all the buzz words and show, what does it all really mean? If you're just getting started in the field or know someone who wants to be, this is the first in a series of bite-sized articles looking at life as a Data Scientist. Is Newton a hero of yours? Me too! Were science and maths your favourite subjects? Me too! As a Data Science specialist recruiter working across both research and commercial roles, I've had the pleasure to meet and learn from thousands of Data Scientists and other professionals within the analytics space, and here's what I’ve learned. What does a Data Scientist do? A Data Scientist offers a holistic view of data with a clear understanding of how data comes together and the relationships between seemingly disconnected features. Below are three distinct areas where these manifest: 1. Experimentation 2. Production 3. ExplanationTesting, Testing, Hypothesise, Prove - ExperimentationAs someone interested in Physics and Chemistry throughout school, the word science conjures up images of frogs being dissected, Newton being hit by an apple, and Bunsen Burners. Much like a chemist tests for chemical properties, playing with their experiments to define different results, a data scientist does the same - only with gigabytes upon gigabytes of data. The phase of experimentation for a Data Scientist is crucial, they test hypotheses, understand the limitations of algorithms and try to establish successful Proofs of Concept (PoC) to both prove and disprove their hypotheses. Once these experiments have proven success on limited data sets, then the process of production begins. It goes without saying that for experimentation to take place, there needs to be a clear structure to the data, an area that my colleague Josh Carter covers in his article Build IT and They Will Come. Putting it All Together - The Production Puzzle When it comes to production, a Data Scientist has to juggle all aspects and implement a ‘clean’ solution that can run as efficiently as possible. An isolated hypothesis is of little use to a business using analytics to shape policy and inform major business decisions. The complete dataset must be rolled out and continue to achieve similar results of the initial PoC to offer commercial impact. It must be able to work in harmony with all the other algorithms that are currently deployed. Once these initial PoC algorithms have been put into production and have produced an interesting output, there is one final stage to the process. Tell Me in Plain Language - Explanation Data Science has infused every industry, including retail. Much like a retail associate explains to prospective buyers the benefits and features of the product, so too must the Data Scientist be able to do the same. However, a Data Scientist must be able to break down a complex concept and be able to translate their findings into non-technical terms. This is an essential skill when you consider that very few commercial Data Scientists work in isolation, and in order for businesses to completely buy into Data Science, they first need to understand it. As someone who's worked with Data Scientists and Data Analysts both in the retail industry and now, as a recruiter, I find this is one of the most fascinating parts of the process.I hope the above brief summary provides insight into a very topline overview of the way that a Data Scientists works within industry. Please do take a look at our current vacancies or reach out to me directly. You can reach at email@example.com or by calling me on 0208 408 6070.
19. June 2018
The days’ of landing your dream job and working your way up within a single organisation over 30-40 years is an experience of past generations. Graduates and most commonly Millennials are routinely chasing new external opportunities to find their dream job throughout their careers, and It’s predicted that Millennials will have nine jobs in a lifetime.As shown in our latest salary guide, analysts want flexibility in their roles and are willing to search for it. The salary guide shows particularly within Data Science that 62% had only been in a role for between 0-2 years, following the trend we saw, that those who change roles receive higher pay over time, with the Harnham Data Science team commenting “As data-driven transactional companies continue to compete for the same talent, we can only predict salaries will continue to rise.”This trend could be evident because of candidate shortage, and those who are open to changing jobs more frequently will, in theory, gain broader experience and deeper skill sets across a wider range of verticals, which makes their acquired knowledge extremely valuable in a growing market.The Global Skills IssueWe not only see this in the UK but our US Data Science consultant Allister Duncan noted in his article detailing the battle that exists for skilled data scientists; that he spotted a trend amongst those from traditional backgrounds falter as they attempt to transition towards modern Data Science roles, because they have to utilise unfamiliar technology and packages - verses those in the market who are comfortable with the idea of changing roles more frequently benefiting from increased control over career growth, direction and education through exposure.The idea of a job for life may no longer exist as global consumer attitudes, demands and technology evolve. To effectively manage this and retain the skilled analysts a business has, and for job seekers to not become perennial job hoppers, the answer may be to strike a balance between up-skilling existing analysts who can grow new teams or face losing talented analysts who seek the opportunity to be developed within or companies.Currently, it is evident that this idea is not implemented deep enough within some companies, as the feedback we received from the data and analytics market shows that 12% of analysts left their roles in 2015 due to a lack of training offered.With that said, the exciting thing about data and analytics is that it is a globally growing discipline, and there is time for the market to settle, and time for those wanting to be a part of it, to gather the required skills needed to be active participants. Universities globally are creating market-centric data and analytics course to plug skill gaps as well as develop analysts for the future in response.Tell us what you look for in a company when you are looking for a new role. What are your thoughts on the market trends that you see? Are your drivers similar to what our salary guide has picked up, or are their other factors at play?
31. October 2016
In the UK, London leads in forging new schemes to draw and train talent both domestically and abroad. From stepped up degrees, early childhood curriculum schemes, migration talent sourcing, and in-house training, businesses are finding creative ways to shrink the data science skills gap. One such suggestion from techUK's Big Data Hero, Alison Lowndes of Nvidia Ltd is to 'teach code as prolifically as reading to open the doors of the digital age for everyone."IBM weighs in on the talent gap to define not only the term "data scientist", but the scope of all data science professionals. Though it may seem as though this career operates in a vacuum, it's imperative to understand that along with the analytical components - maths, statistics, logic, and programming, there is a cultural element as well. Data science is the process of applying scientific method to solve business problems.It requires teamwork and communication skills to not only gather the data, but to be able to present it so that business executives can take that information and implement solutions.Making Better Business Decisions with Predictive AnalyticsWish you could see into the future to grow your business? No crystal ball required. Predictive analytics can optimize your decision-making. Artificial Intelligence and machine learning are core skillsets for a data scientist, but the human element helps divine the good data from the bad.Much like Michaelangelo's David, systematic data gathering is the raw cube of stone, which must then be chiseled into form; analyzed and presented. An art and a science, data science helps businesses make better business decisions by estimating future outcomes via predictive analytics. These predictions are often based on past performance, customer or client influence, and sometimes gut assumptions based on gathered data. As growth trends in data teams expand to include non-technical staff, the siloed idea of simply pushing data from query to conclusion is no longer enough. The data team should have a grasp of how the business works overall to impact strategies and revenue. Businesses know how important data is to their future growth. Effective data teams must interact with managers who can help to frame the company's larger strategy to drive insight and analysis with the right questions.Hard and Soft SkillsSoft skills are not confined to simply being able to communicate findings. Other elements include instinct or gut reactions that the numbers may say one thing, but based on knowledge of the business, may not be the right answer. Data professionals with a healthy skepticism will know when to take a step back, revisit the information, and what they need to do or how best to present suggestions based on their findings. Collected data is not unbiased. Best practices when studying analysis are to have a list of questions ready such as the data source, worst and best case scenarios, and what must be true for a correct recommendation.Data scientists and data teams are expected to not only have an aptitude with number crunching, but also the ability to communicate their findings. To combat this and to grow their data teams, some businesses have begun in-house training programs. Computer and IT professionals who show an aptitude for data science may be offered positions on teams to learn from degreed professionals as well as outside learning opportunities.According to a recent techUK whitepaper, migration sourcing for talent abroad and early education curriculum schemes, as well as domestic apprenticeship and training programs, are just a few of the ways businesses are combatting their struggle to fill key roles.Hindsight doesn't have to be 20/20. As the year 2020 approaches, advancements in big data, data analytics, machine and AI learning are powering business predictions as employers seek to fill key roles. In a look to the future, demand for data science professionals with both hard and soft skills will be at the forefront of the data revolution.We have opportunities for Data Scientists across the Bitcoin, Retail, Fintech and Start-up spaces. Explore new ideas and experiment with big data to produce real-time solutions. Get in on the ground floor and take your career to the next level. For additional opportunities check out our current vacancies. Contact our UK Team at +44 208 408 6070 or email firstname.lastname@example.org to learn more.
05. June 2014