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Mid-level Data Scientist title, metro location, broad ML skills and a strong employer increase competition.
Highly domain-specific credit risk and regulatory modeling experience reduces cross-industry transferability.
Explicit 2–5 years, required Master's, mandatory Python/SQL and domain/regulatory experience create strict filters.
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Validate models across multiple financial domains including credit risk, marketing, fraud, and account management to ensure accuracy and compliance.
Use Google Cloud Platform (GCP) to automate workflows and improve process integration.
Lead and execute AI validation projects aligning with organizational goals and contribute to AI policy and model risk management development.
2 - 5 years of experience in model development and/or validation, especially in credit risk, marketing, or fraud.
Master’s degree or higher in Statistics, Mathematics, Engineering, Economics, Data Science, or related field.
Advanced programming skills in Python and SQL; experience with ML libraries like Scikit-learn, StatsModels, TensorFlow is recommended.
Work Experience Required: 2 - 5 years in relevant field.
Experienced in applying AI techniques such as machine learning, NLP, and deep learning in financial model validation.
Familiar with cloud platforms, especially GCP, and capable of automating and integrating processes using these platforms.
Capable of collaborating across teams and geographies with strong interpersonal and communication skills, and understanding regulatory guidelines related to model risk management.