Data Scientist

Lorien
City of London
1 month ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Overview

Data Scientist. Hybrid working – Local site with 1-2 days on site. Financial Services.

Responsibilities
  • Collaborate with cross-functional teams to develop and enhance our GenAI-Powered smartdigital assistant.
  • Leverage expertise in NLP and transformer architectures to create intelligent conversational agents.
  • Dive into traditional NLP techniques and stay ahead of the curve.
  • Apply understanding of fundamental concepts—statistics, linear algebra, calculus, regression, classification, and time series analysis—to extract valuable insights from data.
  • Drive data visualisation efforts — whether it’s Tableau, Power BI, or Cognos — to create compelling visualisations that bring data to life.
  • Contribute to the development of a visualisation layer for analytics, making complex insights accessible and actionable.
Key Skills and ExperienceNLP Mastery
  • Proficiency in LLMs and transformer architecture.
  • Deep understanding of traditional NLP techniques.
  • Solid grasp of data visualisation tools (Tableau, Power BI, Cognos, etc.).
  • Proficiency in Python visualisation libraries (Matplotlib, Seaborn).
  • SQL for data extraction and manipulation.
  • Experience working with large datasets.
Technical Skills
  • Proficiency in cloud computing and Python programming.
  • Familiarity with Python libraries like Pandas, NumPy, scikit-learn.
  • Experience with cloud services for model training and deployment.
Machine Learning Fundamentals
  • Statistical concepts for robust data analysis.
  • Linear algebra principles for modelling and optimisation.
  • Calculus for optimising algorithms and models.
  • Predictive modelling techniques for regression and classification.
  • Time series analysis for handling time-dependant data.
  • Deep learning and neural networks.
LLM Operations
  • Expertise in managing and operationalising large language models.
  • Experience in deploying models on cloud platforms (e.g. AWS, SageMaker, Google AI Platform, IBM Watson).

IND_PC3

Carbon60, Lorien & SRG - The Impellam Group STEM Portfolio are acting as an Employment Business in relation to this vacancy.


#J-18808-Ljbffr

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.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.