Forward-Deployed Lead - Credit Analytics & ML Platforms

About the role

We are looking for a hands-on Forward Deployed Engineering (FDE) Lead 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, and with other engineers. You will own the engineering architecture, build and maintain models, 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.

What you will do

• 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.

• Build, calibrate, validate and maintain the probability of default, prepayment and lifetime risk models with the account Principal. 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.

What we are looking for

• A track record of designing and delivering production data or ML systems, with ownership extending beyond prototypes and architecture diagrams.

• Hands-on experience building and deploying ML models - from features through calibration to monitoring.

• 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.

• 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 documentation.

• Strong client-facing communication in English. You can explain trade-offs to non-technical stakeholders, constructively challenge a requirement and take responsibility for the resulting decision.

• Experience leading a small technical team while remaining actively involved in implementation.

Useful additional experience

• Credit-risk, lending or servicing analytics, including PD, LGD, ECL, prepayment or portfolio stress testing (strongly preferred), or other financial-services models such as fraud, pricing or churn.

• Regulated financial-services delivery and model governance.

• Streamlit, Azure ML, semantic data layers or analytics applications built on Snowflake.

• Configurable ML products serving multiple portfolios or clients.

• Evaluated natural-language analytics, text-to-SQL or LLM-assisted workflows.

Current technical environment

Snowflake, SQL, Python, Streamlit and an Azure compute environment. The team also uses AI-assisted development tools. You will help evolve the architecture based on the product’s needs rather than introduce a predetermined stack.

What we would like to discuss

• A production ML or analytics system you personally helped build, including the architecture decisions and code you owned.

• How you would adapt a model and its calibration for a new portfolio while preserving reproducibility, access boundaries and validation.

• A time you challenged a business or technical assumption, proposed an alternative and delivered a better outcome.

Skills

• Production Python and SQL (Snowflake) at scale

• Hands-on model development: gradient boosting and logistic regression, calibration, out-of-time validation (scorecards a plus)

• Data engineering: pipelines, data-quality checks, lineage

• MLOps: versioning, CI/CD, monitoring, reproducible releases, rollback

• Model evaluation in production: leakage, drift / PSI, population change, model quality vs system reliability

• Clear communication with non-technical stakeholders, and the ability to challenge

• Hands-on lead of a small team

Knowledge

• Credit-risk concepts: PD, LGD, ECL / IFRS 9, prepayment, roll rates - strong plus

• Data protection: GDPR, anonymisation, access control. UK model governance (FCA / PRA model-risk expectations) is a plus

• Snowflake security, cost and deployment model (roles, Streamlit in Snowflake)

• Where LLM / text-to-SQL analytics is useful, and where it is not