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
* 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 analyse structured and unstructured data from internal and 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.
Required Experience & Skills
* Domain expertise: 3+ years 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 ML, NLP, or large language models.
* 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.
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