Register a new account with Harnham
£90000 - £100000 per annum + Huge Bonus + Very Strong Pension
London
** Lead Data Scientist needed to join Investment/ Asset Mgt. giant ** ** Leading a new team / London ** - £100k Salary + Huge Bonus and Pension Apply Below!
59640
£57000 - £67000 per annum + Bonus + Strong Benefits
** IN-HOUSE Data Science Consultants needed for Major UK Airline ** ** £57k - £67k // London // min. 1 years commercial exp. needed ** Apply Below!
59802
£50000 - £75000 per annum + Huge Bonus and Pension, flex working
* Data Scientist needed to join Investment/ Asset Mgt giant! * * London // £50,000 - £75,000 / min. 1 year exp needed * * Customer analytics / top academics *
59642
£40000 - £90000 per annum + pension, healthcare etc
** Data Scientist wanted to join a fin-tech start-up - Scorecard/ Decision Science exp needed ** Good Academics Essential! £40 - £80k // London ** Apply Below
48321
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.
by Luke Frost
04. July 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 markproud@harnham.com or by calling me on 0208 408 6070.
19. June 2018
Read about Harnham's corporate social responsibility initiatives
Find our more about the causes we support.
