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This tech company is leading the crowd in smart products for children and infants! By creating innovative smart products, this company is helping parents solve parenting challenges so they can focus more on the things that matter - quality time with your family. With the latest technology and flexible working schedules, this startup has an amazing work environment and laid back team structure.
As a Senior Machine Learning Engineeer, you will…
YOUR SKILLS & EXPERIENCE
As a Senior Machine Learning Engineer, you can expect a base salary between $140,000 to $160,000 (based on experience) plus competitive benefits.
HOW TO APPLY
Please register your interest by sending your CV to Kristianna Chung via the Apply link on this page.
£55000 - £65000 per annum + benefits + bonus
You will be responsible for working directly with clients from proofs of concept all the way through to implementing and developing into client systems.
£55000 - £65000 per annum + benefits + bonus
You will be working with large, messy data sets in Python to build tailored machine learning models to target clusters of customers more effectively.
£100000 - £110000 per annum + benefits + bonus
You will get the opportunity to play around with huge amounts of world data - from location to financial data - to pioneer machine learning solutions in Python
£60000 - £70000 per annum + benefits + bonus
Analysing large amounts of customer data in Python or R using machine learning techniques to produce valuable insights for the business around their consumers.
£75000 - £85000 per annum + benefits + bonus
This global e-commerce company are investing heavily into finding a Senior Data Scientist to join a team that focuses on caring for every customer every day.
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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Whilst the role of Data Scientist is still considered one of the most desirable around, many businesses are finding that a shortage of strong, experienced talent is preventing them from growing their teams sufficiently. With a huge demand for such a small talent base, enterprises have begun to ask what they can do to ensure that they can secure the skillsets they need. If you’re looking at hiring a Data Scientist, there are a few key Do’s and Don’ts that you need to bear in mind: THE DO’S Create A Clear Career Path In most companies, a career path is defined. Usually you grow from junior to senior to manager etc. However, Data Scientists often like to become experts rather than moving up the traditional career ladder into people management roles. And, once a Data Scientists becomes an expert, they want to remain an expert. To do this, they need to keep up with the latest tools and data systems and continually improve. That’s why it’s important that you put in place a clear career path that suits the Data Scientists. In addition to the possibility of leading teams on projects, businesses should provide opportunities for financial progression that reflect growing skillsets in addition to increased responsibilities. Let Them Be Inventive One of the biggest turn-offs for Data Scientists is lack of opportunities to try new techniques and technologies. Data Scientists can get bored easily if their tasks are not challenging enough. They want to work on a company’s most important and challenging functions and feel as though they are making an impact. If they are asked to spend their time on performing the same tasks all the time, they often feel under-utilised. Providing forward-looking projects, with innovative technologies, gives Data Scientists the opportunity to reinvent the way the company benefits from their Data. Provide Opportunities To Discover As part of their attitude of constant improvement, Data Scientists often feel that attending conferences or meet-ups helps them become better at their role. Not only are these a chance for them to meet with their peers and exchange their Data Science knowledge, they can also discover new algorithms and methodologies that could be of benefit to your business. Businesses that allow the time and budget for their team to attend these are seen as much more attractive prospects for potential employees in a competitive market. Give them the freedom they need Data Scientists are efficient workers who can both collaborate and work independently. Because of this, they expect their employers to trust that they will get the job done without feeling micro-managed. By offering flexible working, be it flexi-hours or working from home options, enterprises can make themselves a much more appealing place to work. THE DON’TS Hire The Wrong Skillset As many companies begin to introduce Data teams into their business, they can often attempt to hire for the wrong job. Generally, this will be because they automatically jump to wanting to hire a Data Scientist, but actually need a different role placed first. For example; a company may be looking to hire a Machine Learning specialist, but their data pipeline hasn't even been built yet. There are many talented candidates out there who want to work with the latest technology and solve problems in complex ways. But the reality is that a lot of businesses aren’t ready for their capabilities yet. Before hiring, asses what skillsets you really need and be specific in your search. Undervalue Their Capabilities There are still a large number of organisations that do not value Data within their culture and Data professionals pick up on this incredibly quickly. If they feel that their work is under appreciated, and they know that there is high demand for what they do, they will not waste their time sticking around. Ask yourself how you see your Data team contributing to the company as a whole and make this clear within your organisation. Advanced Data Scientists don't want to work on dashboarding so make sure that their work will have an impact and explain how you see this happening during the interview process. Additionally, be aware of other financial implications that their hire may have. It’s likely that they’ll need a supporting Data Engineer to work with and, if they don’t have access to one, they have another reason to look elsewhere. The Data Scientist market is a candidate-driven one and, as a result of this, businesses need to go the extra mile to ensure they get the best talent around. By offering a strong set of benefits, the opportunity to grow and progress, and an environment that values Data, enterprises can stand out amongst the crowd and attract the best Data Scientists on the market. If you’re looking for support with your Data Science hiring process, get in touch with one of our expert consultants who will be able to advise you on the best way forward.
11. April 2019
The Ski season may be drawing to a close, but it’s never too early to start planning for next year. Born and raised in the mountains of Austria, I have been skiing all of my life. For me, it’s about freedom, enjoying the views and forgetting about everything else. But, since I’ve stepped into the world of Data & Analytics, I started to asked myself “what can I learn from my work that I can apply to my skiing”? After having a look around, I began to discover ways in which Data could support my passion. I’ve pulled together some of the most interesting things I’ve discovered and created this handy guide to help you prepare for your next trip. Here’s how you can use data to create the perfect ski trip. Follow the snow Anyone who has skied before knows about the uncertainty before a trip. Will there be enough snow? Will the weather be good? Which resort is the most suited to my ability? Fortunately, somebody has already pulled this information together for you. Two "web spiders" were built via Scrapy, a Python framework used for data extraction, the first of which extracted ski resort data. The second spider, on the other hand, extracted daily snowfall data for each resort (2009 - present). After collecting Data from more than 600 ski resorts and spitting it into 7 main regions, the spiders were able to form a conclusion. The framework then pulled out key metrics, including the difficulty of runs, meaning that skiers are now able to decide which resort is most suitable for their ability. As for the weather, onthesnow.com has recorded snowfall data from all major resorts, every year since 2009. We all know that good snow makes any trip better, so the collected data here will help skiers ensure they are prepared for the right weather, or even plan their trip around where the snow will be best. Optimise your skis Ski manufacturing is a refined and complicated process, with each ski requiring many different materials to be built. Unfortunately, this often results in the best skis running out quickly as supply outspeeds demand. To help speed up and improve the process, companies are implementing technologies like IBM Cognos* that monitor entire supply chains so that no matter how much demand increases, they have the materials to meet it. Additionally, since the majority of companies have become more data-driven, production time has been reduced by weeks. Predictions for future demand has also become 50% more accurate, resulting in a drop of 30% idle time on production lines. Skip the Queue Tired of queuing for the ski lift? There’s good news. As they begin to make the most of data, ski resorts are introducing RFID* (Radio Frequency Identification) systems. These involve visitors purchasing cards with RFID chips included, allowing them to skip queues at the lifts as there is no need to check for fake passes. The data can then be utilised for gamification platforms to turn a skier’s time on the slopes into an interactive experience. The shift towards Big Data not only has advantages for the visitors, but the management are also benefiting. In the past, it has been difficult to analyse skier’s data. Now, with automated and proper data management, the numbers can be crunched seamlessly and marketing campaigns can be directed at how people actually choose to ski. Carve a Better Technique Skiing isn’t always easy, especially if you haven’t grown up with it. Usually, ski instructors are the solution but, in the age of Data & Analytics, there are other solutions. Jamie Grant and co-founder Pruthvikar Reddy have created an app called Carv 2.0, which allows you to be your own teacher. It works by using a robust insert that fits between the shell of your ski boots and the liner. It then gathers data from 48 pressure sensitive pads, and nine motion sensors. This data is fed to a connected match-box size tracker unit, sitting on the back of your boots, before being relayed via Bluetooth to the Carv App on your phone. Carv can then measure your speed, acceleration and ski orientation a staggering 300 times a second. Thanks to a complex set of algorithms this data is then converted into an easy to follow graphic display on your phone’s screen as well as verbal feedback from Carvella. The accuracy of this real-time data could make it a better instructor than any individual person. Data & Analytics are helping streamline every part of our lives. Whilst the above can’t guarantee a perfect ski trip, they can help us minimise risks and optimize our performance and experience. If you’re able to use data to improve day-to-day living, we may have a role for you. Take a look at our latest opportunities or get in touch with our expert consultants.
04. April 2019