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

Apply Now

Engineer: Data Science

Mayer Brown
City of London
2 days ago
Create job alert

Join to apply for the Engineer: Data Science role at Mayer Brown.

Mayer Brown is an international law firm positioned to represent the world’s major corporations, funds, and financial institutions in their most important and complex transactions and disputes. We are recognized by our clients as strategic partners with deep commercial instincts and a commitment to creatively anticipating their needs and delivering excellence in everything we do.

We are a collegial and collaborative firm where highly motivated individuals with an unwavering commitment to excellence receive the opportunity, support, and development they need to grow, thrive, and realise their greatest potential—all while supporting the firm’s client service principles of excellence, strategic partnership, commercial instinct, integrated strengths, innovation, and collaboration across our international firm.

If you enjoy working with team members whose defining characteristics are exceptional client service, initiative, professionalism, responsiveness, and adaptability, you may be the person we are seeking to join our Information Technology department in our London office as an Engineer: Data Science.


Role

The Engineer: Data Science is responsible for the design, development, and delivery of advanced analytics and AI solutions in support of the firm’s Data and AI strategy. This role works closely with the data science team, IT engineers, and business teams to implement reliable, scalable solutions that deliver measurable business value. The Engineer applies experience in data science, AI methods, and modern engineering practices to build and deploy solutions in production environments. The role emphasizes delivery excellence – ensuring that solutions are practical, efficient, and compliant with the firm’s standards for security, confidentiality, and governance. Working closely with data science, IT, and data teams, the Engineer translates complex concepts into practical solutions that support critical business outcomes.


Standard hours are 9:30 am to 5:30 pm with flexibility in accordance with the needs of the business. Our current working-from-home policy allows for two days working from home, subject to business need. Given the global nature of this role, there is often the need for off‑hours (e.g., late evening and/or early morning) conference calls or video conferences.


Responsibilities

  • Design, build, and deploy data science and AI solutions end‑to‑end, from design and development through testing, release, monitoring, and support.
  • Operationalise models with CI/CD pipelines, automated testing, and monitoring, applying MLOps practices such as versioning, retraining, and drift detection (tools: MLflow, Azure ML, Databricks).
  • Leverage both open‑source frameworks (LangChain, Hugging Face, etc.) and enterprise platforms (Azure OpenAI, Databricks, etc.) to deliver production‑ready, scalable AI solutions.
  • Implement generative AI and advanced analytics features, including embeddings, retrieval‑augmented generation, and building AI agents and chat‑based solutions.
  • Write clean, testable, and well‑documented code using modern engineering practices (unit testing, code reviews, API development, Azure DevOps preferred).
  • Ensure solutions align with enterprise architecture, data governance, and security standards.
  • Collaborate with enterprise architects, IT, and business stakeholders to validate approaches.
  • Contribute to lifecycle management practices including model versioning, monitoring, and continuous improvement of delivery processes.
  • Evaluate and pilot emerging technologies to improve scalability and solution quality.

Qualifications
Education & Training

  • Bachelor’s degree in Computer Science, Data Science, or a related field required.
  • Master’s degree in Computer Science, Data Science, or a related field preferred.

Certifications

  • Microsoft Certified: Azure AI Engineer Associate, Azure Data Scientist Associate (preferred).
  • Databricks Certified or equivalent cloud ML platform certification (preferred).

Professional Experience

  • Minimum of 2 years of hands‑on experience delivering data science, machine learning, or AI solutions in production environments.
  • Law firm or professional services industry experience a plus.

Technical Skills

  • Experience developing, testing, and deploying complex, high‑impact AI/ML solutions into production, ensuring reliability and scalability.
  • Hands‑on with Azure (preferred), AWS, or GCP; familiarity with Microsoft Fabric/Synapse, data lakehouse architectures, and containerization (Docker/Kubernetes).
  • Proficiency with modern AI and ML frameworks such as PyTorch, TensorFlow, LangChain, and enterprise AI platforms such as Azure OpenAI Service.
  • Strong understanding of CI/CD, model versioning, monitoring, retraining, and lifecycle management using cloud‑based tools.
  • Applied experience with large language models (LLMs), embeddings, retrieval‑augmented generation (RAG), and building AI agents or chat‑based solutions.
  • Familiarity with data integration, ETL, and governance standards to ensure AI/ML solutions align with enterprise architecture.
  • Strong written and verbal communication skills, with the ability to communicate effectively and professionally at all levels of the firm.
  • Ability to work in a diverse, cross‑functional team environment, supporting the demanding needs of a global law firm.
  • Self‑starter with high initiative in problem‑solving, process improvement, and driving data‑management best practices.
  • Strong customer‑service orientation, anticipating stakeholder needs and exercising independent judgment.
  • Exceptional attention to detail and organisational skills, ensuring accuracy in documentation, data integrity, and process adherence.
  • Strong analytical and problem‑solving skills focused on delivering business value through data‑driven solutions.
  • Ability to present complex data concepts to non‑technical stakeholders, translating technical information into clear, business‑friendly insights.

Commitment to Inclusion

At Mayer Brown, we are committed to creating an inclusive work environment that offers our people the opportunity and support they need to succeed. We actively support employee resource groups covering LGBTQI+, race & ethnicity, multi‑faith, women, disability, social inclusion, and family. We welcome reasonable adjustments throughout the recruitment process and beyond.


#J-18808-Ljbffr

Related Jobs

View all jobs

Engineer: Data Science

Engineer: Data Science

Principal Engineer – Data Science

Data Engineer Data Science/Java/Python/Unix

Lead R Engineer / Data Scientist - Integrated Pest Management & Soil Science

Lead R Engineer / Data Scientist - Integrated Pest Management & Soil Science

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 to Write an AI CV that Beats ATS (UK examples)

Writing an AI CV for the UK market is about clarity, credibility, and alignment. Recruiters spend seconds scanning the top third of your CV, while Applicant Tracking Systems (ATS) check for relevant skills & recent impact. Your goal is to make both happy without gimmicks: plain structure, sharp evidence, and links that prove you can ship to production. This guide shows you exactly how to do that. You’ll get a clean CV anatomy, a phrase bank for measurable bullets, GitHub & portfolio tips, and three copy-ready UK examples (junior, mid, research). Paste the structure, replace the details, and tailor to each job ad.

AI Recruitment Trends 2025 (UK): What Job Seekers Must Know About Today’s Hiring Process

Summary: UK AI hiring has shifted from titles & puzzle rounds to skills, portfolios, evals, safety, governance & measurable business impact. This guide explains what’s changed, what to expect in interviews, and how to prepare—especially for LLM application, MLOps/platform, data science, AI product & safety roles. Who this is for: AI/ML engineers, LLM engineers, data scientists, MLOps/platform engineers, AI product managers, applied researchers & safety/governance specialists targeting roles in the UK.

Why AI Careers in the UK Are Becoming More Multidisciplinary

Artificial intelligence is no longer a single-discipline pursuit. In the UK, employers increasingly want talent that can code and communicate, model and manage risk, experiment and empathise. That shift is reshaping job descriptions, training pathways & career progression. AI is touching regulated sectors, sensitive user journeys & public services — so the work now sits at the crossroads of computer science, law, ethics, psychology, linguistics & design. This isn’t a buzzword-driven change. It’s happening because real systems are deployed in the wild where people have rights, needs, habits & constraints. As models move from lab demos to products that diagnose, advise, detect fraud, personalise education or generate media, teams must align performance with accountability, safety & usability. The UK’s maturing AI ecosystem — from startups to FTSE 100s, consultancies, the public sector & universities — is responding by hiring multidisciplinary teams who can anticipate social impact as confidently as they ship features. Below, we unpack the forces behind this change, spotlight five disciplines now fused with AI roles, show what it means for UK job-seekers & employers, and map practical steps to future-proof your CV.