Data Scientist - Banking
Posted 3 days ago by Vallum Associates
£600 Per day
Inside
Hybrid
South Yorkshire
Summary: The Data Scientist role in Banking involves designing predictive models and analyzing financial data to develop machine learning and natural language processing solutions for risk management and fraud detection. The position requires a strong background in banking, with responsibilities including building credit risk scorecards and automating data pipelines. Candidates should possess 3-5+ years of relevant experience and technical proficiency in Python and SQL. The role offers a hybrid working arrangement with three days on-site in Sheffield, UK.
Key Responsibilities:
- Develop behavioural segments, credit risk scorecards, and predictive models for customer onboarding, cross-selling, and churn retention.
- Utilize advanced analytics to identify anomalies and fraudulent activities in transaction data. Implement risk models (probability of default) and maintain regulatory compliance.
- Extract, clean, and analyze structured and unstructured data from internal/external sources.
- Write advanced SQL queries and Python/R scripts for data manipulation and build machine learning algorithms (e.g., Scikit-learn, TensorFlow).
- Translate complex analytical findings into actionable business insights for management.
Key Skills:
- 3+ years of experience in banking or financial services, specifically in credit risk, fraud strategy, or compliance.
- Proficiency in Python, R, SQL, and big data technologies (Spark/Hadoop).
- Hands-on experience with Machine Learning (ML), Natural Language Processing (NLP), or Large Language Models (LLMs).
- Master’s degree in Statistics, Econometrics, Mathematics, Finance, or Data Science.
- Strong analytical mindset, detail-oriented approach, and the ability to work under pressure.
Salary (Rate): £600/day
City: Sheffield
Country: UK
Working Arrangements: hybrid
IR35 Status: inside IR35
Seniority Level: Mid-Level
Industry: IT
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.