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Metro role at known employer, broad ML/data scope and generalist senior title drive high applicant competition.
Role favors ML/AI expertise but finance-specific experience preferred, making cross-industry transfer moderately sensitive.
Explicit 7–10 years plus mandatory ML, Databricks, PySpark and MLOps requirements create high shortlisting strictness.
Lead development and deployment of predictive machine learning models to solve business problems and deliver measurable outcomes.
Own end-to-end model lifecycle including development, validation, tuning, interpretability, and MLOps deployment in distributed data environments.
Guide and review work of junior team members while communicating model insights and assumptions clearly to stakeholders.
7 to 10 years of relevant work experience in predictive modeling and machine learning.
Strong proficiency in Python for data science, SQL, and PySpark/Databricks or similar distributed data platforms.
Experience with model development, evaluation, validation, interpretability, and MLOps concepts (deployment, monitoring, lifecycle management).
Numerical based degree or postgraduate qualification.
Experienced in applying machine learning and statistical methods in complex, large-scale data environments, preferably in financial services or insurance domains.
Operates independently on end-to-end modeling projects, including data engineering, modeling, and stakeholder communication.
Experienced in code/source control with GitHub and DevOps pipelines for CI/CD, and comfortable working in audit-aware and process-reengineered environments.