Data Scientist - Banking
The Role: Data Scientist - Banking
Location: Sheffield, UK
Position Type: Contract Inside IR35
Remote work option Available: Hybrid – 2 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.