Salary: £? - ? per year

Requirements:

• Strong Python programming skills, including data analysis using libraries such as Pandas
• Strong understanding of machine learning, including both supervised and unsupervised techniques
• Experience with tree-based and boosting algorithms such as XGBoost, LightGBM, and related approaches
• Experience working with large-scale datasets, ideally using PySpark
• Strong analytical and SQL skills for handling, querying, and interpreting complex datasets
• Ability to communicate insights effectively through visualisations using tools such as Matplotlib, Plotly, or similar
• Exposure to model interpretability techniques such as SHAP and LIME
• Previous experience within the Banking / Financial Services sector
• Familiarity with Financial Crime, AML, Compliance, or Transaction Monitoring domains
• Strong data science and machine learning background
• Willingness to learn the business context

Responsibilities:

• Support a new initiative focused on Transaction Monitoring (TM) for Business Banking
• Apply machine learning skills to real-world financial crime and transaction monitoring challenges
• Help expand an existing machine learning-driven transaction monitoring capability from Retail Banking into Business Banking

Technologies:

• Support
• Machine Learning
• Python
• PySpark
• SQL
• pandas

More:

We are the Analytics Centre of Excellence and we are looking for an experienced ML Engineer to support a new initiative focused on Transaction Monitoring for Business Banking. This is a 6-month onsite role based in Glasgow, with Edinburgh also considered, requiring 2-3 days onsite. We are expanding an existing machine learning-driven transaction monitoring capability already underway within Retail Banking into the Business Banking space, and we are seeking someone who can bring strong technical capability and a willingness to learn the business context.

last updated 37 week of 2026