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Mid-level ML/Data role, metro hybrid, and popular title increase applicant density.
Core ML skills transfer across industries, but credit-risk domain expertise increases specificity.
Explicit 5–8 years and mandatory ML/NLP/LLM and risk-modeling skills enforce strict filters.
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Develop and deliver B2B risk solutions, including standard and custom models for major clients such as Fortune 500 companies.
Lead end-to-end modeling processes: design, development, validation, implementation, monitoring, and reporting of credit risk, fraud detection, and compliance models.
Develop and deploy AI agents leveraging ML and NLP, including LLMs and prompt engineering, for real-time business risk monitoring and predictive insights.
Master’s degree or higher in a quantitative discipline (Math/Stat, Economics, Computer Science, Finance, Operations Research, etc.).
5 to 8 years of experience in Data Science, specifically in design and development of risk models and frameworks.
Mandatory experience with ML techniques including Xgboost, Light GBM, Random Forest, Logistic Regression, Decision Tree, Neural Networks.
Experience with Python, Pyspark, strong SQL skills, and mandatory experience with NLP / LLMs.
Strong track record working on financial services risk analytics or related high-stakes risk modeling environments.
Expertise in both structured and unstructured data analysis, including large-scale datasets and NLP/LLM applications.
Able to lead modeling projects end-to-end with strong client collaboration and clear communication to technical and non-technical stakeholders.