Forward-Deployed Architect, Credit Analytics & ML Platforms

We are looking for a hands-on Forward Deployed Engineering (FDE) Architect to help develop a credit analytics platform for a regulated financial-services business operating in the UK and Ireland. The platform brings together lending and servicing data to support portfolio analysis, credit-risk modelling and better business decisions. Current work covers probability of default, redemption and prepayment, expected credit loss, and interactive analytics.

The next challenge is to turn this work into a reliable, maintainable platform that can support different portfolios and client needs. You will work directly with the client’s business and technology teams, alongside a credit-domain and modelling lead and other engineers. You will own the engineering architecture, contribute production code, and help the team decide what to build and how to deliver it.

We need someone who can question assumptions, propose simpler alternatives and explain technical trade-offs clearly.

Translate business problems into an architecture, practical delivery plan and clear acceptance criteria. Identify gaps in requirements, data and dependencies before they become delivery problems. Design the path from working models and dashboards to a production-ready ML platform. Make deliberate choices about shared components, portfolio-specific configuration, model calibration and future client onboarding. Build and improve reproducible workflows for data preparation, feature engineering, training, validation, scoring and deployment.

Establish clear versioning for code, data, models and calibration logic. Work with the modelling lead on how probability of default, prepayment, lifetime risk and loss estimates are calculated, tested and exposed to applications. Keep business definitions consistent across models, tables and dashboards. Strengthen the data foundation: trace results to source, investigate data-quality issues, document assumptions and build automated checks around important transformations.

Improve the reliability of the Snowflake-backed analytics application, including deployment, testing, permissions and integration with model outputs. Put appropriate MLOps practices in place, including repeatable releases, monitoring, failure handling, rollback and operational documentation. Design for privacy, access control, auditability and model governance. Work with domain experts to make validation, stability and fairness checks practical and reviewable. Lead technical discussions, review code, unblock engineers and keep the client informed about progress, risks and architectural decisions.

Assess where natural-language or LLM-assisted analytics would add value, without losing focus on trustworthy data, robust models and useful products.

A track record of designing and delivering production data or ML systems, with ownership extending beyond prototypes and architecture diagrams. Strong hands-on Python and SQL. You should be comfortable investigating a dataset, reviewing modelling code and implementing critical parts of the solution yourself. Practical MLOps experience: reproducible pipelines, model and data versioning, testing, deployment, monitoring and the operational consequences of model updates.

Experience with cloud data platforms and modern warehouses. Snowflake experience is particularly relevant; you should be able to reason about data access, performance, cost and deployment constraints. An understanding of model evaluation, calibration, data leakage, changing populations and the difference between model quality and system reliability. The ability to work through incomplete documentation and inconsistent business definitions, and turn findings into maintainable code and clear…