Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Scientist

Lorien
City of London
6 days 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.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.