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SENIOR MACHINE LEARNING ENGINEER
As a Senior Machine Learning Engineer you will:
YOUR SKILLS AND EXPERIENCE
HOW TO APPLY
Please register your interest by sending your CV to Elise Myhren via the Apply link on this page
Up to 750000kr per annum
Harnham is now recruiting for a skilled Data Scientist to perform analysis on a large amount of unstructured data.
550000kr - 650000kr per annum
This is your chance to have a huge impact on the growth of a young company based in Sweden
£80000 - £90000 per annum + benefits + bonus
You will be responsible for researching, developing and deploying deep learning solutions using Python...
£60000 - £95000 per annum + Additional Benefits
Join a FTSE 100 company operating on a huge global scale.
£550 - £650 per day
As a Data Scientist, you will be working as part of the AI/ML team, building Machine Learning models to improve their supply-chain management.
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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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
As Big Data can reveal patterns, trends and associations relating to human behaviour and interactions, it’s no surprise that Data & Analytics are changing the way that the supply chain sector operates today. From informing and predicting buying trends to streamlining order processing and logistics, technological innovations are impacting the industry, boosting efficiency and improving supply chain management. Analysing behavioural patterns Using pattern recognition systems, Artificial Intelligence is able to analyse Big Data. During this process, Artificial Intelligence defines and identifies external influences which may affect the process of operations (such as customer purchasing choices) using Machine Learning algorithms. From the Data collected, Artificial Intelligence is able to determine information or characteristics which can inform us of repetitive behaviour or predict statistically probable actions. Consequently, organisation and planning can be undertaken with ease to improve the efficiency of the supply chain. For example, ordering a calculated amount of stock in preparation for a busy season can be made using much more accurate predictions - contributing to less over-stocking and potentially more profit. As a result, analysing behavioural patterns facilitates better management and administration, with a knock-on effect for improving processes. Streamlining operations Using image recognition technology, Artificial Intelligence enables quicker processes that are ideally suited for warehouses and stock control applications. Additionally, transcribing voice to text applications mean stock can be identified and processed quickly to reach its destination, reducing the human resource time required and minimising human error. Artificial intelligence has also changed the way we use our inventory systems. Using natural language interaction, enterprises have the capability to generate reports on sales, meaning businesses can quickly identify stock concerns and replenish accordingly. Intelligence can even communicate these reports, so Data reliably reaches the next person in the supply chain, expanding capabilities for efficient operations to a level that humans physically cannot attain. It’s no surprise that when it comes to warehousing and packaging operations Artificial Intelligence can revolutionise the efficiency of current systems. With image recognition now capable of detecting which brands and logos are visible on cardboard boxes of all sizes, monitoring shelf space is now possible on a real-time basis. In turn, Artificial Intelligence is able to offer short term insights that would have previously been restricted to broad annual time frames for consumers and management alike. Forecasting Many companies manually undertake forecasting predictions using excel spreadsheets that are then subject to communication and data from other departments. Using this method, there’s ample room for human error as forecasting cannot be uniform across all regions in national or global companies. This can create impactful mistakes which have the potential to make predictions increasingly inaccurate. Using intelligent stock management systems, Machine Learning algorithms can predict when stock replenishment will be required in warehouse environments. When combined with trend prediction technology, warehouses will effectively be capable enough to almost run themselves negating the risk of human error and wasted time. Automating the forecasting process decreases cycle time, while providing early warning signals for unexpected issues, leaving businesses better prepared for most eventualities that may not have been spotted by the human eye. Big Data is continuing to transform the world of logistics, and utilising it in the best way possible is essential to meeting customer demands and exercising agile supply chain management. If you’re interested in utilising Artificial Intelligence and Machine Learning to help improve processes, Harnham may be able to help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more. Author Bio: Alex Jones is a content creator for Kendon Packaging. Now one of Britain's leading packaging companies, Kendon Packaging has been supporting businesses nationwide since the 1930s.
29. August 2019