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Strong employer brand, mid-senior ML role, metro location, and generalist ML/AI skills create high applicant competition.
Role requires credit risk and credit-bureau familiarity, causing high industry-specific background sensitivity despite transferable ML skills.
Explicit degree and years requirements plus mandatory ML, cloud, Spark, and model-deployment experience make screening highly strict.
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Lead development and deployment of analytic and machine learning solutions across credit, fraud, insurance, and marketing business use cases.
Own end-to-end project delivery, from opportunity management and solution design to deployment and ongoing monitoring, ensuring high-quality outputs and stakeholder satisfaction.
Manage and mentor small analytic research teams or interns, lead innovation labs, and drive process improvements using R, Python, and cloud-based tools.
Master's or PhD in statistics, applied mathematics, financial mathematics, computer science, engineering, operations research, or related quantitative field with minimum 5 years relevant experience; OR Bachelor's with minimum 8 years relevant experience.
Experience with machine learning and deep learning development and end-to-end model deployment lifecycle.
Hands-on experience with cloud platforms (GCP/AWS) and big data tools such as Spark ML and PySpark in distributed environments.
Work Experience Required: Minimum 5 years with advanced degree OR 8 years with Bachelor's as specified.
Strong expertise in credit risk modeling and broad financial services risk management practices, with familiarity of credit bureau data and solutions.
Proven ability to lead multiple complex analytic projects simultaneously in collaborative, fast-paced, matrixed environments.
Skilled at stakeholder management and translating technical concepts into actionable business insights for diverse internal and external partners.