About The Role

About us

Analytium is a boutique UK data and analytics consultancy - 14 people, 150 projects delivered for 50 clients. We are embedded in a UK tier-one bank, modernising one of the largest SAS estates in the country, because we build our own accelerators and we work AI-first.

Why this role exists

Banks are increasingly asking us a harder question than "can you migrate this" - they are asking "can you make our modelling environment governed, repeatable and defensible". This role focuses primarily on the ungoverned script estate and bringing governance to it. That means walking into a credit risk estate nobody fully understands and producing conclusions that survive a model validation review. The person will do estate discovery, own lineage and evidence capture, bring model lifecycle discipline, industrialise data pipelines, evidence rationalisation decisions so they survive audit, and work directly with credit risk, modelling and model risk stakeholders.

Required Qualifications

Strong practical SAS capability across Base SAS, macro language, PROC SQL, and Enterprise Guide, including the ability to reverse-engineer undocumented code.
Working-strength Python experience, with the software engineering discipline to take notebooks or scripts to production-grade.
Hands-on Databricks experience, including Delta Lake, notebooks, and cluster behaviour.
Credit risk modelling knowledge including IFRS 9, IRB, PD, LGD, EAD, capital, stress testing, or model monitoring.
Track record of shipping work where an AI coding tool did most of the typing, with the ability to explain what you needed to fix.
Experience automating part of your own work using AI-assisted ways of working.
Client-facing experience, including running workshops and writing outputs clients will act on, together with strong written English for client-facing assessment output.
Able to travel occasionally to the Hampshire base and for possible European client travel.

Preferred Qualifications

Experience conducting formal estate assessment, discovery, or technical due diligence work.
Experience with MLOps or model lifecycle tooling such as MLflow, model registries, or feature stores.
Experience with workflow orchestration tools such as Airflow, dbt, or Databricks Workflows.
Experience with data product or data mesh operating models.
Knowledge of European banking or EU regulatory frameworks.
Experience with Teradata and/or PySpark.
Discretionary annual bonus scheme