London / £40000 - £60000
£40000 - £60000
UP TO £60,000 + BENEFITS (incl. 10% bonus)
This is an excellent opportunity to work in a leading customer & loyalty analytics company as a Data Scientist! You will be working to break down and analyse retail customer data using a tech stack that is based on Python/PySpark and SQL.
As a Data Scientist in this company, you will get to work with some massive clients within the retail sector using advanced modelling to understand their customer base. This is an opportunity to work amongst talented individuals across a wide range of projects in a fast-paced environment.
As a Data Scientist, you will be working with leading retailers in the retail sector as they aim to improve their knowledge and understanding of their customer base. You will be using advanced modelling techniques such as regression, propensity, churn and chaid analysis using a tech stack based around Python/PySpark and SQL.
You will be working on a consultancy basis, using statistical modelling to solve client's problems using your excellent problem-solving skills. As well as this, you will be using advanced technology to increase clients understanding of their customers.
YOUR SKILLS AND EXPERIENCE
The successful Data Scientist will have the following skills and experience:
- Experience working with Customer Data and statistical modelling
- Advanced commercial experience using SQLand Python
- Commercial experience within the Retail industry is desirable
- Excellent communicational skills
- Experience working in a consultancy environment is desirable
- Educated to degree level in a STEM subject or similar
The successful Data Scientist will receive a salary between £40,000 and £60,000. In addition to this, they shall receive an extensive benefits package, including your birthday off, early Friday finishes, bonus and options to negotiate on flexi-working including a 10% bonus.
The interview process will consist of 2 stages with a takehome task.
HOW TO APPLY:
Please register your interest by sending your CV to Dylan Butcher via the Apply link on this page.
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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’SCreate A Clear Career PathIn 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 needData 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’TSHire The Wrong SkillsetAs 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.
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These skills are critical as Software Data Engineers are constantly testing and deploying new services to make systems more efficient. This is important to understand, especially when incorporating Data Science and Machine Learning teams. If Data Scientists or Machine Learning Engineers do not have a strong Software Engineers in place to build their platforms, the models they build won’t be fully maximized. They also have to be able to scale out systems as their platform grows in order to handle more Data, while finding ways to make improvements. Software Data Engineers will also be looking to work with Data Scientists and Machine Learning Engineers in order to understand the prerequisites of what is needed to support a Machine Learning model. Which is right for your business? 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Although they might be able to do more than a Data Engineer, Software Data Engineers may not be as strong when it comes to the nitty gritty parts of Data Engineering, in particular querying Data and working within a Data Warehouse. It is always a challenge knowing which type of job to recruit for. It is not uncommon to see job posts where companies advertise that they are looking for a Data Engineer, but in reality are looking for a Software Data Engineer or Machine Learning Platform Engineer. In order to bring the right candidates to your door, it is crucial to have an understanding of what responsibilities you are looking to be fulfilled.That’s not to say a Data Engineer can’t work with Docker or Kubernetes. Engineers are working in a time where they need to become proficient with multiple tools and be constantly honing their skills to keep up with the competition. However, it is this demand to keep up with the latest tech trends and choices that makes finding the right candidate difficult. Hiring Managers need to identify which skills are essential for the role from the start, and which can be easily picked up on the job. Hiring teams should focus on an individual’s past experience and the projects they have worked on, rather than looking at their previous job titles. If you’re looking to hire a Data Engineer or a Software Data Engineer, or to find a new role in this area, we may be able to help. Take a look at our latest opportunities or get in touch if you have any questions.
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