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Principal Data Scientist

London
£110000 - £120000 per annum
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Principal Data Scientist
Product / Tech Company
£120,000 + benefits

The Company

Harnham are working with a tech company in London with a fantastic product loved by many! Here's the highlights:

  • Company has achieved Unicorn status and grown to 500 + employees
  • Data and analytics drives decisions within all aspects of their organisation
  • Machine Learning and Data Science is key for their business operations
  • The number of subscribers to their service has rocketed in recent years
  • Huge growth plans for the data science and machine learning team
  • Very fun environment with people passionate about building something BIG!

The Role - Principal Data Scientist

You will be:

  • Working on problems in relation to supply chain / logistics / optimisation
  • Working as part of a team of 8 (x4 data scientists / x4 machine learning engineers)
  • Delivering high-impact algorithms for the business
  • Acting as Technical Lead and communicator for the company, liaising with product, engineering and commercial teams

Your Skills and Experience - Principal Data Scientist

You must have:

  • An MSc or Ph.D. in Computer Science
  • Experience working on end-to-end machine learning solutions
  • A strong knowledge of machine learning
  • Domain knowledge of supply chain / logistics / optimisation problems
  • Experience in using cloud-based technologies is desirable

The Benefits - Principal Data Scientist

£120,000 + bonus + healthcare + pension + many more!

How to Apply?

Please register your interest by sending your CV via the Apply link on this page. For more information about similar roles, please get in touch with Nick Mandella at Harnham.

Keywords

Python, AWS, Spark, Machine Learning, Software Engineering, Statistics, Artificial Intelligence, Data Scientist, Data Science, big data, deep learning, CI/CD, Kubernetics, Docker, supply chain, optimisation, logistics.

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London
£110000 - £120000 per annum
  1. Permanent
  2. Data science

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Harnham blog & news

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.

How Are Data & Analytics Professionals Mapping COVID Trends With Data?

The coronavirus pandemic has impacted industries across the globe. There’s no ignoring that simple fact. This disruption (most notably) caused devastating effects in two strands: to our health and to business operations. As the virus spread, the health and wellbeing of people in society worsened, and businesses felt the strain of projects being placed on hold, and work slowing or completely grinding to a halt. As of the 24th February 2021, the disease has infected more than 112,237,188 people, with 2,487,349 reported deaths. For Data & Analytics professionals, it soon became evident that they could use their skills to help. Using the mass of data available, professionals and researchers turned to big data analytics tools to track and monitor the virus’s spread, along with a variety of trends. Here’s how: Genomics and sequencing Life science is a significant application within Data & Analytics and explores the study of all living things on earth. One particular section of this study looks at the concept of genomic sequencing.  Genomic sequencing is significant as it allows us looks at the entire genetic code of a virus – in this case, COVID-19. Most importantly, the technique means that researchers and analysts can identify dangerous mutations and track movements of specific variants. We know that the UK has the most advanced system for tracing covid variants too. Last year, Britain launched one of the world’s largest coronavirus sequencing projects, by investing £20 million in the Covid-19 Genomics UK consortium. In a group that included NHS researchers, public health agencies, academic partners and the Wellcome Sanger Institute, they set out to map the genetic code of as many strains of the coronavirus as possible. And the buy-in paid off. It took the US approximately 72 days to process and share each genetic sequence, compared with 23 days for UK researchers, according to figures compiled by the Broad Institute with data from Gisaid. Tech giants stepping in Ultimately, your organisation is more agile than you think it is. Regardless of the size of the business, or the industry in which it operates, the sector’s response in applying analysis and data to track the coronavirus was nothing short of miraculous. Google introduced a series of features such as popular times and live busyness, COVID-19 alerts in transit, and COVID checkpoints in driving navigation in order to keep their one billion (and growing) app users safe. They also introduced the COVID layer in Maps, a tool that shows critical information about COVID-19 cases in a given area, allowing their customers  to make informed decisions about where to go and what to do. Apple also released a mobility data trends tool from Apple Maps. This data was shared in order to provide insights to local governments and health authorities so that they could support mapping specific covid trends. These first-hand examples indicate the influence and power of using data to better our understanding of the virus. Before the coronavirus pandemic, professionals, businesses and industries alike worked in siloes. What we have witnessed since has been very much the opposite, as experts quickly came together to begin mapping out data requirements and supporting the world’s focus to improve the public’s health and get businesses back on their feet. Without Data & Analytics, none of this would be possible. If you're looking to take the next step in your career or build out a diverse Data & Analytics team, we 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. 

Amy Topcakan

Amy Topcakan

25. February 2021

News

Data Science For Business Decision Making

All strong and successful businesses are built and run upon well-informed decision-making, which derive from a mix of leader experience, industry knowledge and, more recently, the regular implementation and use of advanced Data Science teams.  While the use of data has been around for many years, it’s hard to believe that it is only in the last five years or so that we have seen the adoption of such technology and skills really take off. Five years ago, the importance and demand for Data Scientists sat at a very meagre 17 per cent, whereas in 2019, we saw exponential growth of over 40 per cent – a number that is expected to continue growing as we move forward.  Within Data & Analytics, Data Science is a crucial arm within many businesses of all shapes and sizes. Through the collection and analysis of certain datasets, Data Science teams can delve into an organisation’s pain points, any potential obstacles and future predictions; crucial elements which, if looked at and planned for in advance, can be the making of a business.  So, how else can Data Science influence the decision-making process and make a positive impact on a business and its bottom line? The removal of bias and the increase of accuracy As humans we are innately susceptible to bias, conscious and unconscious, and this can be a hindrance on our ability to make informed yet impartial decisions. By relying solely on facts and figures instead of our own opinions, we are not only removing bias, but we are in turn making the decision-making process more accurate.  Accuracy within decision-making will remove the potential risk of mistakes and the need to re-do tasks, therefore saving precious time, resource and money, unequivocally a benefit for any business’s bottom line.  Efficiency There are elements of all businesses that require trial and error for example, hiring practices. People who look great on paper and perform exceptionally well in first interview may turn out to be utterly the wrong fit six months down the line. However,  collecting and recording data of those employees who do fit well into the business, compared to those who don’t, can help to reduce the chance of choosing the wrong candidate. This in turn improves staff retention rates, helps create a positive work culture and, of course, positively impacts profitability.  Considering the cost for hiring one person for a company is around £3,000, Data Science is of huge benefit to any company, large or small, in reducing the risk of high staff turnover.  Mitigating risk All businesses at some point in their lifetime will come up against potential obstacles and risks that, if not managed properly, can be potentially lethal. The implementation of Data Science will allow senior leaders to learn from past mistakes and create evidence-based plans to better tackle, or completely avoid, similar problems in the future.  This could be for either organisational risk or strategic risk, both of which can be extremely damaging if not prepared for. Organisational risk entails problems occurring within daily business tasks such as fraud, data loss, equipment and IT issues and staff resignations. Strategic risk relates to events that cannot be planned for in advance; those sudden and unforeseeable changes - a great example being the current COVID-19 pandemic.  However, with both risk groups, Data Scientists can help to mitigate these risks through learnings and observations made from reams of previous data, as well as real-time intelligence. This allows senior leaders to act fast where needed, and plan where possible.  Data & Analytics, and especially Data Science, has been, and will continue to be, a key driver in the evolution of many industries worldwide. As we move forward, we will undoubtedly see an even larger uptake of the available technologies as business leaders everywhere begin to see the influential value of data-driven decision-making. If you’re a Data Scientist looking to take a step up or are looking for the next member of your team, we 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.

Rosie O'Callaghan

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