Essential:

• A data science foundation, applied for outcomes: framing the business question, setting baselines, choosing the right metric and delivering measurable results rather than just models. This comes with statistical rigour: experimental design, sound validation, and an instinct for leakage, bias and overfitting in messy real-world data.
• Strong ML fundamentals across classical ML and deep learning (pandas, scikit-learn, XGBoost or LightGBM, PyTorch), and the judgement to tell when a language model is the wrong tool.
• A deep understanding of how modern models are built, not only how they're used: transformer architectures, pre-training and post-training methods (SFT, RLHF/DPO) and evaluation methodology. You will have fine-tuned models yourself, for example with the Hugging Face ecosystem (Transformers, PEFT/LoRA, TRL), and can explain from first principles why a system fails.
• NLP depth on long-form professional text: classification, information extraction, entity resolution, summarisation, embeddings and retrieval.
• Real-world delivery of modern AI approaches (RAG, fine-tuning, agentic AI and multimodal models) in production workflows. You'll have built on model APIs (e.g. Azure OpenAI, Anthropic, open-weight models) and evaluation tooling, and know when the framework, or the LLM, is unnecessary.
• Hands-on engineering skills in Python and SQL, taking work from notebook to production to a high standard (testing, CI/CD, containers, infrastructure-as-code), with production experience on Azure, ideally Azure ML and Databricks. You'll work as a peer of the platform engineering teams.
• Rigour in running AI in production: experiment tracking and model registry (MLflow), evaluation frameworks and regression testing before release, monitoring and data-drift management, LLM observability and model governance.
• A track record of owning technical decisions across model selection, data and retrieval strategy, evaluation and production deployment, and of contributing AI expertise to solution architecture.
• Proven leadership of AI, ML or data science teams: hiring, mentoring and growing a team that ships, and moving it from prototype habits to production discipline. You'll run delivery through Scrum or Kanban, adapted for AI work with time-boxed spikes, hypothesis-driven experiments and definitions of done that include evaluation thresholds.
• The credibility to advise senior stakeholders, translating complex technical trade-offs and statistical findings into clear, commercially grounded decisions, and being plain about what the data does and doesn't support.
• A pragmatic, delivery-first mindset: you influence what gets built, then make sure it ships.
• Genuine curiosity about where AI is heading, and the judgement to know which developments actually matter for the business.

Desirable:

• Experience across the wider Azure AI and data estate, such as AKS, Azure AI Search, Azure OpenAI and Azure Document Intelligence.
• Data engineering alongside data science, using tools such as Spark, dbt, Azure Data Factory and SQL.
• Exposure to model serving and optimisation, such as vLLM, ONNX Runtime and quantisation.
• Familiarity with annotation and human-in-the-loop tooling.
• Experience with ranking, scoring or recommendation systems, or with survey and research data.
• Responsible-AI practice, such as fairness assessments, model documentation and input to data protection impact assessments.
• A postgraduate degree in a quantitative discipline, or equivalent applied experience.

We welcome applications from people of all backgrounds and experiences. If you’re not sure you meet every requirement, we’d still like to hear from you — we value potential as much as experience.

Depending on the role, we may be able to offer flexible working options such as hybrid working, part‑time or job‑share arrangements, or flexible hours.

About us:

Chambers is the leading legal data and intelligence partner for lawyer, firms, and in-house teams.

We conduct over 350,000 research interviews and surveys with in-house counsel every year, and receive 62,000 submissions from 9,000 firms worldwide, giving us unrivalled insight into the legal sector.

This research powers Chambers Rankings - the definitive guide to the best legal talent - and our Intelligence, which delivers the insights helping firms and in-house teams to succeed.

Our independent, rigorous research identifies the exceptional and charts the path to success, enabling legal professionals to see with clarity, decide with confidence, and plan with ambition.

Equal Opportunity Statement:

We are committed to fostering and promoting an inclusive professional environment for all of our employees, and we are proud to be an equal opportunity employer. Diversity and inclusion are integral values of Chambers and Partners and are key in our culture. We are committed to providing equal employment opportunities for all qualified individuals regardless of age, disability, race, sex, sexual orientation, gender reassignment, religion or belief, marital status, or pregnancy and maternity. This commitment applies across all of our employment policies and practices, from recruiting and hiring to training and career development. We support our employees through our internal INSPIRE committee with Executive Sponsors, Chairs and Ambassadors throughout the business promoting knowledge and effecting change.

Applicants who identify as Disabled and/or Neurodiverse will be entitled to an interview if they meet the minimum criteria as specified in the Job Description, additionally we will offer reasonable adjustments to those who require them. Some examples of reasonable adjustments are extra time in assessments, video interviews to combat travel-based issues and advice on expected interview topics/questions.