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

Apply Now

Data Scientist - Tax & Legal

Square One Resources
City of London
5 days ago
Create job alert
Overview

Job Title: Data Scientist
Location: London
Salary/Rate: Up to 620 per day inside IR35
Start Date: 10/11/2025
Job Type: Contract

Company Introduction

We are looking for a Data Scientist to join our Data and AI Team supporting internal service delivery transformation projects across Tax and Legal. You will be a Python and Azure expert in designing and building cutting-edge generative AI solutions for complex challenges. You will report to the Technical Lead and collaborate with data scientists, software engineers and market specialists across the team to build and deliver high-impact solutions

Responsibilities
  1. Build and deploy Python based AI applications
  2. Research and design advanced experiments and prototypes using cutting edge techniques, specifically in GenAI
  3. Develop various LLM assisted frameworks
  4. Design and write clean, maintainable, auditable and well documented codebases
  5. Implement testing pipelines and evaluation frameworks
  6. Good documentation practices to ensure seamless operations
  7. Exceptional teamwork and communication skills working in a cross functional environment with other data scientists, software engineers and T&L domain experts
Required Skills/Experience
  1. Be an expert Python programmer proficient in frameworks/libraries such as: Numpy, Pandas, Scikit-Learn, Langchain, Llamaindex, Azure AI Foundry amongst others.
  2. Must have Azure and be an expert with R&D on generative AI techniques
  3. Practical experience with GenAI techniques such as Finetuning, Prompt engineering, prompt orchestration, retrieval methods (RAG and Knowledge graph techniques), Agentic Systems etc.
  4. Knowledge of Agentic frameworks such as LangGraph, Azure AI Foundry Agents, Semantic Kernel Agents etc.
  5. Knowledge of prompt orchestration and optimisation techniques such as Azure Semantic Kernel, Prompt flow etc.
  6. Skilled at working with AI engineers to write production ready python code and implementing robust quality control methods in solutions
  7. Have knowledge of basic software engineering concepts and best practices for team-based programming, including versioning, testing, and deployment
Desirable Skills/Experience
  1. Adept at creating highly optimised workflows and solutions
  2. Deep knowledge of various Microsoft Azure AI services
  3. Strong data analytics and visualisation skills specifically using tools like Excel and Alteryx
  4. Knowledge of Responsible AI practices
How to apply

If you are interested in this opportunity, please apply now with your updated CV in Microsoft Word/PDF format.

Disclaimer

Notwithstanding any guidelines given to level of experience sought, we will consider candidates from outside this range if they can demonstrate the necessary competencies.

Square One is acting as both an employment agency and an employment business, and is an equal opportunities recruitment business. Square One embraces diversity and will treat everyone equally. Please see our website for our full diversity statement.


#J-18808-Ljbffr

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist - Outside IR35

Data Scientist - AI Agents - Remote - Outside IR35

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.

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.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.