Data Science / ML Engineer

TXP; Glasgow; On-site; GBP 92,000 - 148,000; Full time

Job summary

TXP is seeking a Data Science / ML Engineer to help build and deploy machine learning solutions for transaction monitoring in Business Banking. You will work with large-scale data, implement models, and collaborate with analytics colleagues to turn insights into actionable production workflows.

Candidates should be strong in Python, ML techniques, and data visualization, with experience in PySpark and SQL. Location is Glasgow (Edinburgh considered) and the role is onsite 2-3 days per week for a

Qualifications

• Strong Python and Pandas for data analysis.
• Solid ML knowledge with supervised and unsupervised techniques.
• Experience with tree-based/boosting models (XGBoost, LightGBM).
• Experience handling large datasets, ideally with PySpark.
• Strong SQL and data visualization skills (Matplotlib, Plotly).

Responsibilities

• Develop ML models for transaction monitoring in Business Banking.
• Collaborate with analytics teams to translate insights into production pipelines.
• Work on end-to-end data analytics from data wrangling to model interpretation.

Skills

Python

Pandas

ML/ML algorithms

PySpark

SQL

Data visualization

Tools

XGBoost

LightGBM

Matplotlib

Plotly

Job description

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 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

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