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Medium — mid-level metro ML lead with banking specialization reduces applicant density.
High — banking, on-prem deployment, and transaction-data expertise restrict cross-industry transferability.
High — explicit 6–10 years, mandatory production ML skills, and banking/on-prem deployment constraints.
Lead development, evaluation, and deployment of ML models focused on banking transaction and behavioral data to drive customer engagement growth, initially targeting credit cards.
Establish and maintain robust production ML pipelines and governance, including secure on-premises/private cloud deployment suited for bank environments.
Collaborate with banking co-development partners and internal teams for model reproducibility, knowledge transfer, and enabling multi-bank reusable ML architectures.
6+ years of experience in Applied Data Science / Machine Learning.
Strong hands-on skills in Python, SQL, and ML frameworks like scikit-learn, XGBoost, LightGBM, CatBoost, or PyTorch.
Experience delivering and maintaining production ML models with associated monitoring, retraining, and improvements.
Work Experience Required: 6+ years in relevant Applied Data Science/ML roles.
Experienced in building ML solutions for large structured and transactional datasets with expertise in feature engineering, experimental design, and business impact measurement.
Proven ability to operate within secure, privacy-sensitive environments including on-prem and private cloud deployments, ensuring governance and auditability.
Demonstrated ability to collaborate cross-functionally with product, engineering, and external partners for knowledge transfer and scalable multi-client solution deployment.