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Mid-level ML role, metro locations and known brand create high applicant competition.
Strong ML skills transferable, but credit risk and bureau-data experience bias fit toward financial services.
Explicit degree/years, ML/Deep Learning, cloud and deployment experience make hiring filters strict.
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Lead development and deployment of analytic solutions focused on credit risk, fraud, insurance, and marketing applications.
Manage complex custom analytic engagements end-to-end including opportunity identification, solution design, development, deployment, and monitoring.
Provide oversight and hands-on support for analytics using R, Python, and model lifecycle management, while mentoring junior team members and handling stakeholder interactions.
Master's or PhD in a quantitative field with 5+ years relevant experience OR Bachelor's degree with 8+ years relevant experience.
Experience with Machine Learning and Deep Learning development.
Proficiency with cloud platforms (GCP or AWS) and large datasets in distributed environments; experience with Spark ML and Pyspark.
Work Experience Required: 5+ years (Master's/PhD) or 8+ years (Bachelor's). Notice period: Not explicitly mentioned in the JD.
Experienced in credit risk modeling and familiar with financial services risk management and credit bureau data.
Capable of leading multiple complex projects with strong project management and stakeholder communication skills in a matrixed environment.
Able to innovate and scale analytic prototypes into production-ready solutions while mentoring teams and engaging with clients.