Data Science Senior Analyst – Machine Learning & NLP

Campion Pickworth Ltd
UK
1 year ago
Applications closed

Related Jobs

View all jobs

Junior / Graduate Data Scientist

Junior Data Analyst

Junior Data Analyst

Junior Data Analyst

Junior Data Analyst

Junior Data Analyst

Our client, a leading international consultancy, is looking to recruit a Data Science Senior Analyst within their London office. The ideal candidate will have a strong background in Natural Language Processing and Machine Learning research and a strong track record of taking research ideas to real-world applications. Role Responsibilities: Using machine learning techniques such as NLP (natural language processing) and advanced predictive modelling in order to derive valuable insights from large disparate sources of data and deliver insightful and meaningful understanding to the risks and key drivers of clients Working closely with the business stakeholders and experts in order to develop new concepts to develop new and innovative tools to support the evolving audit and assurance environment Helping the team to support clients in building production quality applications related to natural language processing and machine learning Staying up to date with developments in the field of NLP and Machine Learning, architectures and languages Leading diverse teams within an inclusive team culture where people are recognised for their contribution Qualifications/Experience Technical Experience in a Machine Learning/AI environment, ideally within an in-house dedicated team or consultancy A deep understanding and at least 4 years of experience of developing NLP based ML algorithms, modern text analytics methodologies, such as Word/sentence embeddings, Topic Modelling, Named Entity Recognition, Relation Extraction, Entity Linking and other natural language processing and machine learning techniques Advanced programming skills in Python/R and related NLP/ML libraries like NLTK, scikit-learn, numpy, scipy, spaCy etc. Real world experience of working with Deep Learning architectures (CNN, RNN) Practical experience in preparing data for Machine Learning (e.g., using SQL and/or NoSQL technologies) Working experience of deep learning frameworks such as Keras, TensorFlow etc General Ability to communicate complex data problems to non-technical stakeholders A degree (preferably Masters or PhD) in Computer Science, Software Engineering, Mathematics or other related topics Understanding of cloud solutions (AWS, Azure, Databricks) Self-starter with project management skills Experience leading teams using Agile methodologies Strong communication, presentation and client management skills

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 Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

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

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.