Specialist recruitment for data science & AI roles in regulated industries

What we do

Data science in financial services sits at the intersection of statistical rigour, engineering discipline and regulatory awareness. A fraud model deployed at a consumer lender operates under very different constraints to a propensity model at a fintech, different data, different scrutiny, different explainability requirements. We recruit across this range, which means we can have an honest conversation with both clients and candidates about what a role actually involves day to day.

We work on permanent and contract placements at all levels, from junior data scientists building their first production models to principal scientists and heads of data science leading teams of ten or more.

What we place

Fraud & financial crime modelling

Fraud detection is one of the highest-stakes applications of machine learning in financial services. We place data scientists working on transaction fraud, identity fraud, application fraud and first-party fraud at banks, payment businesses and specialist lenders. These roles typically demand strong Python skills, experience with gradient boosting and neural network architectures, and a clear grasp of the performance vs. false-positive trade-off in a real-time decisioning context.

Propensity & customer modelling

Propensity models underpin acquisition, retention and cross-sell strategies at lenders, insurers and fintechs. We place scientists building next-best-action models, churn predictors and lifetime value frameworks, typically in Python with scikit-learn, XGBoost or LightGBM, often integrated with broader data platforms in Snowflake, BigQuery or Databricks.

Forecasting & time series

Forecasting demand, loss rates, prepayment and market behaviour is a distinct discipline within data science, one that requires as much domain understanding as statistical sophistication. We place forecasting specialists at banks, insurers, energy firms and fintechs, working with time series methods from classical ARIMA approaches through to deep learning frameworks.

NLP & unstructured data

Natural language processing is growing in financial services, for complaints analysis, document extraction, credit assessment commentary and customer communications. We place NLP engineers and scientists working with transformer-based models, large language model fine-tuning and retrieval-augmented generation, primarily at fintechs and data-forward banks.

Model risk & governance

Model risk management sits alongside data science in regulated firms, reviewing, validating and governing the models the science teams produce. We place model risk analysts, model validators and heads of model risk who understand both the quantitative and the governance side of the discipline. These roles are scarce, particularly at senior level, and we work as a search partner to find people who are not actively looking.

Tools and technologies

The roles we fill most regularly require Python (pandas, scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow), SQL and increasingly cloud-native data platforms, Snowflake, Databricks and BigQuery. We also see demand for MLflow and other MLOps tooling as firms move toward more structured model deployment and monitoring.

Who we work with

Our clients include retail and challenger banks, specialist lenders, payment businesses, insurers, fintechs and the consultancies that serve them. On the candidate side, we work with data scientists and machine learning engineers at all levels, from those two or three years into their first roles to experienced practitioners who have led teams and want their next genuinely interesting problem.

Permanent and contract

We place both. Permanent roles are the majority of what we do at mid-to-senior level; contract and fixed-term placements are common for project work, interim cover and capacity during periods of rapid model development. The data science skills market in financial services is tight, particularly for people with both strong technical skills and regulated-industry experience, and we work as a search partner rather than posting CVs on job boards.