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Metro location, popular data science title, and mid-level experience make this role highly competitive.
Strong ML skills are transferable, but financial risk domain preference increases industry-specific background sensitivity to medium.
Required Master's, explicit 2–5 years, and mandatory ML/PySpark/risk-modeling skills make shortlisting highly strict.
Develop and implement B2B risk solutions including standard and custom models for Fortune 500 clients focused on credit risk, fraud detection, and compliance.
Apply LLMs and prompt engineering to analyze large-scale structured and unstructured data (e.g., Company News, Annual Reports) to derive risk insights.
Design, develop, and deploy AI agents using Machine Learning and NLP to detect real-time risk triggers and enable predictive risk management.
Master’s degree or higher in quantitative disciplines like Math/Stat, Economics, Computer Science, Finance, or Operations Research.
2-5 years of work experience in Data Science, with experience in risk model development desirable.
Strong programming skills in Python and Pyspark; strong SQL skills and experience with large datasets.
Work Experience Required: 2-5 years in Data Science
Experienced in applying modern machine learning techniques such as Xgboost, Light GBM, Random Forest, Neural Networks and familiar with ML explainability methods.
Ability to work independently and collaboratively with global teams and clients, managing multiple assignments under challenging timelines.
Demonstrates strong business acumen particularly in Financial Services and can communicate complex ideas effectively to technical and non-technical stakeholders.