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Data Scientist - Machine Learning

City of London, London
£500 - £550 per day
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DATA SCIENTIST - MACHINE LEARNING
£500- £550 per day
REMOTE BASED
6 MONTHS

Are you looking for an opportunity to join a team of Data Scientists in building different price prediction models and ML models to solve business problems across the supply chain.

THE COMPANY:

As a Data Scientist you will have the chance to work for a leading retail company in helping building different models within products. You will be working alongside both Data Scientists, Product Owners and analysts in 2 week agile sprints.

THE ROLE:

As a Data Scientist be looking into how different products are built and the intelligence you can help build into them. As a Data Scientist you will be working closely with the Product Analyst who sits in the wider product organisation to help build different pricing models. As a Data Scientist you will be using Python extensively and differnent ML packages. As a Data Scientist you will be helping to build models using Spark in EMR and then looking how you can drive value in these models through different data science techniques. As a Data Scientist you will be expected to present your ideas to senior stakeholders and other members of the wider product team so you must be able to demonstrate you are an effective communicator. Previous experience working as a Data Scientist in e-commerce is highly valuable as you will be dealing with data sources such as customer data, product data and tracking data.

YOUR SKILLS AND EXPERIENCE:

The successful Data Scientist will have the following skills and experience:

  • Extensive experience with Python and a good understanding of its libraries; Pandas, NumPy, SciPy
  • Commercial experience working on different customisation and recommendation models
  • Excellent knowledge of AWS
  • Used Pyspark for ETL development and model building
  • Previously dealt with senior stakeholders
  • Masters in relevant field

The ideal candidate will have

  • Previous experience within the e-commerce industry

THE BENEFITS:

  • The chance to work on different price modellers within products
  • A competitive day rate of up to £550

HOW TO APPLY:
Please register your interest by sending your CV to Anna Greenhill via the Apply link on this page.

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49490/AG
City of London, London
£500 - £550 per day
  1. Contract
  2. Data science

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Getting Ahead As A Specialist In Data Science

For professionals working in Data Science, the discipline is all about discovery, insights and innovation. Rapid advancements in the adoption of data and technologies, coupled with organisations feeling the strain of the mass of data they have, means that Data Scientists are in high demand. To stay ahead of the competition, companies must continuously look for unique ways to extract insights from the large volumes of data they acquire. This is where professionals from Data Science come in. Their skills lie in correlating data points, mapping out trends and identifying insights that support organisations to action change and/or enhance their growth. Now more than ever, due to the global pandemic, opportunities to move into a career in this space are vast. From Data Scientists, to Data Engineers and Heads of Analytics and Machine Learning, the possibilities for professionals in this discipline are limitless. Here are just a handful of the things you should know for a career in Data Science. Data Science is supporting the future According to the 2020 Emerging Jobs Report published by LinkedIn, the role of the Data Scientist continues to be an incredibly important one within data, analytics and technology. It also shows that, in our core markets, this position continues to be one of the top emerging roles: USA, it is thirdUK, it is seventhGermany, it is eighthFrance, it is tenth Data Science is a discipline that is growing. In the past year, we have seen these professionals demonstrate their ability to adapt into industries such as retail, banking and medicine, where we have seen such sharp change in consumer habits and a step up in global demand. They are all poised and ready to make use of Data Science functions and analytics. Take healthcare and medicine, for example. New collaborations, funding routes and systems for sharing data will shape research from now on. Data supports the way in which we interpret information and provides a means for us to make predictions, spot new trends and developments as well as better managing supply chains and organisational planning. Taking on a role as a Data Scientist or engineer will ultimately be a purpose-driven career, driving future innovations and making a range of business processes simpler and more effective. Professionals require an array of skills The way in which we have become connected today is ubiquitous. It is this level of connectivity that is having a direct impact on the way in which organisations operate (and how their consumers make use of services), due to the growing levels of data that are being collected and are then required to be interpreted and managed. Professionals working within the realms of Data Science need to keep this link in mind as their career and new project opportunities arise. Whilst it is crucial for these specialists to have unique skills such as Apache Spark, Data Science, Machine Learning and Python, they also need to have a clear understanding of statistics, hold business development skills and be clear communicators. Particularly of Data Scientists and engineers, it is imperative to really understand what the customer is looking for from the software, be able to handle multiple projects side-by-side and have excellent end-to-end experience across relevant frameworks. Professionals should take the time to bolster these skills, particularly for technical needs, by completing ongoing online courses, speaking to industry experts and staying updated with the latest iterations of programming and data languages. There are a range of career opportunities for skilled, savvy Data Science professionals with an interest in data and analytics on the table. The discipline is determined by technology and trends, making for a dynamic, rapidly developing industry that is growing at an unprecedented rate. Critically, as the industry continues to advance and demand for skilled professionals grows, there will be plenty of opportunity for you to make your mark. If you’re a Data Scientist looking to take a step up or are looking for the next member of your team, we can help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.

Luke Frost

Luke Frost

23. December 2020

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From Broken Data Pipelines to Broken Data Headlines

This week's guest post is written by Moray Barclay. Two things have caused the UK’s Test & Trace application to lose 16,000 Covid-19 test results, both of which are close to my heart. The first is the application’s data pipeline, which is broken. The second is a lack of curiosity. The former does not necessarily mean that a data application will fail. But when compounded by the latter it is certain. Data Pipelines All data applications have several parts, including an interesting part (algorithms, recently in the news), a boring part (data wrangling, never in the news), a creative part (visualisation, often a backdrop to the news), and an enabling part (engineering, usually misunderstood by the news).  Data engineering, in addition to the design and implementation of the IT infrastructure common to all software applications, includes the design and implementation of the data pipeline. As its name suggests, a data pipeline is the mechanism by which data is entered at one end of a data application and flows through the application via various algorithms to emerge in a very different form at the other end. A well architected data application has a single pipeline from start to finish. This does not mean that there should be no human interaction with the data as it travels down the pipeline but it should be limited to actions which can do no harm. Human actions which do no harm include: pressing buttons to start running algorithms or other blocks of code, reading and querying data, and exporting data to do manual exploratory or forensic analysis within a data governance framework. The data pipeline for Test & Trace will look something like this:    a patient manually fills out a web-form, which automatically updates a patient listfor each test, the laboratory adds the test result for that patientthe lab sends an Excel file to Public Health England with the ID’s of positive patientsPHE manually transpose the data in the Excel file to the NHS Test & Trace systemthe NHS T&T system pushes each positive patient contact details to NHS T&T agentsfor each positive patient, an NHS T&T contact centre agent phones them. This is a not a single pipeline because in the middle a human being needs to open up an editable file and transpose it into another file. The pipeline is therefore broken, splitting at the point at which the second Excel file is manually created. If you put yourself in the shoes of the person receiving one of these Excel files, you can probably identify several ways in which this manual manipulation of data could lead to harm. And it is not just the data which needs to be moved manually from one side of the broken pipeline to the other side, it is the associated data types, and CSV files can easily lose data type information. This matters. You may have experienced importing or exporting data with an application which changes 06/10/20 to 10/06/20. Patient identifiers should be of data type text, even if they consist only of numbers, for future-proofing. Real numbers represented in exponential format should, obviously, be of a numeric data type. And so on. One final point: the different versions of Excel (between the Pillar 2 laboratories and PHE) are a side-show, because otherwise this implies that had the versions been the same, then everything would be fine. This is wrong. The BBC have today reported that “To handle the problem, PHE is now breaking down the test result data into smaller batches to create a larger number of Excel templates. That should ensure none hit their cap.” This solves the specific Excel incompatibility problem (assuming the process of creating small batches is error-free) but has no bearing on the more fundamental problem of the broken data pipeline, which will stay until the manual Excel manipulation is replaced by a normal and not particularly complex automated process. Curiosity So where does curiosity fit in? The first thing that any Data Analyst does when they receive data is to look at it. This is partly a technical activity, but it is also a question of judgement and it requires an element of curiosity. Does this data look right? What is the range between the earliest and the latest dates? If I graph one measurement over time (in this case positive tests over time), does the line look right? If I graph two variables (such as Day Of Week versus positive tests) what does the scatter chart look like? Better still, if I apply regression analysis to the scatter chart what is the relationship between the two variables and within what bounds of confidence? How does that relate to the forecast? Why? This is not about skills. If I receive raw data in csv format I would open it in a python environment or an SQL database. But anyone given the freedom to use their curiosity can open a csv file in Notepad and see there are actually one million rows of data and not 65,000. Anyone given the freedom to use their curiosity can graph data in Excel to see whether it has strange blips. Anyone given the freedom to use their curiosity can drill down into anomalies. Had those receiving the data from the Pillar 2 laboratories been allowed to focus some of their curiosity at what they were receiving they would have spotted pretty quickly that the 16,000 patient results were missing. As it was, I suspect they were not given that freedom: I suspect they were told to transpose as much data as they could as quickly as possible, for what could possibly go wrong? Single Data Pipeline, Singular Curiosity: Pick At Least One To reiterate, the current problems with T&T would never have arisen with a single data pipeline which excluded any manual manipulation in Excel. But knowing that the data pipeline was broken and manual manipulation was by design part of the solution, the only way to minimise the risk was to encourage people engaged in that manual process to engage their curiosity about the efficacy of the data they were manipulating. In their prototype phases – for that is the status of the T&T application - data projects will sometimes go wrong. But they are much more likely to go wrong if the people involved, at all levels, do not have enough time or freedom to think, to engage their curiosity, and to ask themselves “is this definitely right?” You can view Moray's original article here.  Moray Barclay is an Experienced Data Analyst working in hands-on coding, Big Data analytics, cloud computing and consulting.

Guest Blog

Guest Blog

15. October 2020

News

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