[Hiring] Forward-Deployed Architect, Credit Analytics & ML Platforms @Neurons Lab

Role Description

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
• 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
• Explain technical trade-offs clearly

Qualifications

• 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. Comfortable investigating datasets, reviewing modelling code and implementing critical parts of the solution.
• 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; 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 documentation.
• Strong client-facing communication in English. 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.

Requirements

• Credit-risk, lending or servicing analytics, including PD, LGD, ECL, prepayment or portfolio stress testing.
• 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
• An Azure ML environment
• AI-assisted development tools

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.