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Lead Data Scientist
Up to £100,000
In this role you will grow with both the team and the company to change the way data science is used in one of the largest media companies of the time. In this role you will see the data science capability grow and be a vital part of this movement.
This is an exciting opportunity to help build out a whole new data science function in one of the most influential companies. You will be utilising your skills in Python and R to track how this company interact with their large consumer base. You will:
Key Skills and Requirements
HOW TO APPLY
Interested? Please register your interest by submitting your CV directly by applying to this advert.
£45000 - £55000 per annum + benefits + bonus
This is a client-facing role where you will be working with huge clients from a variety of industries on large, messy data sets using Python.
£80000 - £90000 per annum + Benefits
A leading credit company who are in the process of building a brand new machine learning team focused on automation.
£80000 - £100000 per annum + Yes
City of London, London
Senior Data Scientist, London United kingdom.
US$150000 - US$170000 per annum
San Francisco, California
Looking for a new challenge? Lead a team of accomplished Data Scientists as they tackle exciting, ever-evolving challenges in the entertainment space.
£80000 - £85000 per annum + Other Benefits
** Senior Data Scientist - SAS company - Reading - £85,000 ** ** Python, big data exp needed ** ** Min. 4 years exp required ** Apply Below for more info!
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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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
It’s that time of year again. Decorations are going up, the temperature is dropping daily, and the year’s biggest shopping weekend is upon us. Black Friday and Cyber Monday may have started stateside, but they’re now a global phenomenon. This year, in the UK alone, shoppers are expended to spend £8.57 billion over the four-day weekend. But, for retailers, this mega-event means more than a cash injection. In the world of Data, insights gained from shopping and spending habits during this period can dictate their product and pricing strategies for the next twelve months. So what is it, exactly, that we can stand to learn from the Black Friday weekend? THE GHOST OF BLACK FRIDAY PAST There are a few interesting takeaways from 2018’s Black Friday weekend that will likely impact what we see this year. Firstly, and perhaps unsurprisingly given that it’s a few years since the event has become omnipresent, spending only increased about half as much as initially predicted. There are a number of reasons for this, but cynicism plays a central role. More and more, consumers are viewing Black Friday deals with an element of suspicion and questioning whether the discounts are as good as they’re promoted to be. This, combined with other major annual retail events, such as Amazon’s Prime Day, means that this weekend no longer has the clout it once did. However, 2018 also saw marketers doing more to stand out against the competition. Many businesses have moved away from traditional in-your-face sales messaging and some are even limiting their Black Friday deals to subscribers and members. By taking this approach, their sales stand out from the mass market and can help maintain a level of exclusivity that could be jeopardised by excessive discounts. In addition to branding, marketers making the most of retargeting saw an even greater uplift in sale. Particularly when it came to the use of apps, those in the UK using retargeting saw a 50% larger revenue uplift than those who didn’t. So, having reviewed last year’s Data; what should businesses be doing this year in order to stand out? GETTING BLACK FRIDAY-READY WITH DATA Businesses preparing for Black Friday need to take into account a number of considerations involving both Marketing and Pricing. For the latter, Data and Predictive Analytics play a huge role in determining what items should go on sale, and what their price should be. Far from just being based on gut instinct or word-of-mouth, algorithms derived from Advanced Analytics inform Machine Learning models that determine what should be on sale, and for how much. These take into account not only how many of each discounted product need to be sold to produce the right ROI, but also what prices and sales should be for the rest of the year in order to make the sale financially viable. In terms of Marketing, Deep Learning techniques can be used to accurately predict Customer Behaviour and purchases. These predictions can then reveal which customers are likely to spend the most over the weekend, and which are likely to make minimal purchases. Marketers can then, in the lead up to Black Friday, target relevant messaging to each audience whether it be “get all you Christmas shopping in our sale” or “treat yourself to a one-off item”. By carefully analysing the Data they have available and reviewing the successes and failures of their Black Friday events, businesses can generate greater customer loyalty and improve their sales year-round. If you’re looking to build out your Marketing Analytics team or take the next step in your career, we can help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.
28. November 2019