Data Scientist – Banking

Job Description

The Role: Data Scientist – Banking

Location: Sheffield, UK

Position Type: Contract Inside IR35

Remote work option Available: Hybrid – 3 Days Onsite

Job Description:

A Data Scientist with banking experience designs predictive models, analyzes financial data, and develops ML/NLP solutions for risk management, fraud detection, and customer analytics. Key responsibilities include building credit risk scorecards, automating data pipelines, and ensuring regulatory compliance, typically requiring 3–5+ years of experience with Python, SQL, and statistical modeling in financial institutions. 

Key Responsibilities

• Predictive Modeling & Analytics: Develop behavioural segments, credit risk scorecards, and predictive models for customer onboarding, cross-selling, and churn retention.
• Fraud & Risk Management: Utilize advanced analytics to identify anomalies and fraudulent activities in transaction data. Implement risk models (probability of default) and maintain regulatory compliance.
• Data Handling: Extract, clean, and analyze structured and unstructured data from internal/external sources.
• Technology & Tools: Write advanced SQL queries and Python/R scripts for data manipulation and build machine learning algorithms (e.g., Scikit-learn, TensorFlow).
• Stakeholder Communication: Translate complex analytical findings into actionable business insights for management. 

Required Experience & Skills

• Domain Expertise: 3+ years of experience in banking or financial services, specifically in credit risk, fraud strategy, or compliance.
• Technical Skills: Proficiency in Python, R, SQL, and big data technologies (Spark/Hadoop).
• Modeling Capabilities: Hands-on experience with Machine Learning (ML), Natural Language Processing (NLP), or Large Language Models (LLMs).
• Education: Master’s degree in Statistics, Econometrics, Mathematics, Finance, or Data Science.
• Soft Skills: Strong analytical mindset, detail-oriented approach, and the ability to work under pressure.