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Data Science​/ML Engineer

Job in Glasgow, Glasgow City Area, G1, Scotland, UK
Listing for: TXP
Full Time position
Listed on 2026-09-16
Job specializations:
  • IT/Tech
    Machine Learning/ ML Engineer, AI Engineer (Applied/Software), Data Analyst, Data Scientist
Salary/Wage Range or Industry Benchmark: 92000 - 148000 GBP Yearly GBP 92000.00 148000.00 YEAR
Job Description & How to Apply Below

Data Science /ML Engineers

Location - Glasgow, (Edinburgh is also fine)

Duration - 6 months

Onsite Requirements - 2-3 days onsite

Skills

Analytics Centre of Excellence is looking for experienced ML Engineer to support a new initiative focused on Transaction Monitoring (TM) for Business Banking. A similar machine learning-driven transaction monitoring capability is already underway within Retail Banking, and the team is now expanding these capabilities into the Business Banking space.

Required Skills
  • 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 Py Spark
  • 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
Nice to Have
  • 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
What We're Looking For

Candidates with a strong data science and machine learning background who are keen to apply their skills to real-world financial crime and transaction monitoring challenges. While prior domain experience is beneficial, strong technical capability and a willingness to learn the business context are most important

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