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Global finance brand, mid-level ML role, metro location, and generalist ML requirements increase competition.
Core ML skills transfer well, but finance/actuarial and audit experience are preferred for strong fit.
Explicit 7–10 years plus mandatory ML, MLOps, Databricks, PyTorch, SQL and audit-ready documentation requirements.
Own end-to-end predictive modeling and machine learning projects including development, validation, tuning, and interpretability to deliver measurable business outcomes.
Handle large, complex datasets including feature engineering and data preparation for robust model deployment and lifecycle management.
Lead and mentor junior team members while ensuring clear communication of model outputs, assumptions, and limitations to stakeholders.
7 to 10 years of relevant work experience in data science or related fields.
Proficiency in Python for data science, SQL, and distributed data environments like PySpark/Databricks.
Strong expertise in predictive modeling, machine learning techniques, and MLOps concepts including CI/CD and GitHub for code management.
Numerical based degree or Post Graduate qualification.
Experienced in applying machine learning and predictive modeling in financial services, insurance, or actuarial analytics preferred.
Operates with strong business understanding to translate problems into actionable data science solutions with audit-ready documentation.
Able to provide technical guidance and coaching to junior data scientists, and influence cross-functional stakeholders effectively.