Data Scientist – predominantly remote working

Security Cleared Jobs
Central London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist - New

Data Scientist/AI Engineer

Data Scientist (Globally Renowned Retail Group)

We are seeking a skilled Data Scientist for a 12-month contract role, working remotely with occasional visits to the London office. The ideal candidate will have extensive experience in writing high-quality, production-grade Python code using a variety of libraries and frameworks, including Pandas, NumPy, SciPy, scikit-learn, TensorFlow, PyTorch, OpenCV, and Pydantic. Proficiency in designing and implementing data workflows and models is essential, alongside the ability to contribute to scalable and efficient solutions.

The role requires strong expertise in database systems, particularly Oracle and PostgreSQL, with the ability to write and optimize SQL queries for extracting and manipulating data from relational data stores. Additionally, experience with AWS cloud technologies and its ecosystem is vital for this position, as the candidate will work on cloud-based data solutions and infrastructure. 

Key accountabilities:

  • Extract, analyse and interpret large amounts of data from a range of sources, using algorithmic, data mining, artificial intelligence, machine learning and statistical tools. Interpret the data and present the results using clear and engaging language. 
  • Work closely with the business to identify issues and use data to propose solutions for effective decision making. 
  • Build algorithms and design experiments to merge, manage, interrogate and extract data to supply tailored reports. 


Experience:

  • Experience writing high quality production-grade Python code, using a range of libraries and frameworks: Pandas, numpy, scipy, scikit-learn, Tensorflow, PyTorch, OpenCV, Pydantic etc.
  • Experience with Oracle database and/or PostgreSQL. Being able to understand and write SQL queries to extract and manipulate data from existing and new relational (SQL) data stores is a key requirement.
  • Experience using AWS cloud technologies and ecosystem including: S3, lambda functions, AWSTextract, CodeCommit, Cloud9, SageMaker etc.
  • Proven experience in a range of AI and data science fields: data wrangling, data visualisation, natural language processing, computer vision, deep learning, and statistical inference.
     

SC highly desirable; candidates with active SC clearance will be fast-tracked.
Should you require reasonable adjustments at any point during the recruitment process or if there is a more accessible way for us to communicate, please do let us know.
If you consider yourself to have a disability or if you are a veteran, and you meet the essential criteria for the role, you will be put forward for the ‘Guaranteed Interview’ scheme whereby you will have the opportunity to discuss this role and your suitability with a member of the Sourcing team.
As an Equal Opportunities Employer, we provide the best talent and encourage all applications regardless of background, in line with our commitment to diversity, equality and inclusion.
 

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Careers Jobs

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

Jobs

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Education Jobs

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.