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Tier-1 bank, metro location and visible ML director role increase competition but seniority and niche reduce density.
Role requires banking fraud/financial-crime domain expertise, limiting cross-industry transferability.
Explicit 12+ years, mandatory production MLOps expertise and financial-crime domain increases strictness.
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Lead deployment, automation, maintenance, and monitoring of machine learning models in production to ensure effective performance.
Develop frameworks for robust model monitoring and oversee machine learning pipeline design, testing, and fault-finding.
Manage and motivate teams and engage stakeholders to align machine learning solutions with business strategy and goals.
At least 12 years of experience operating and managing production ML systems including monitoring and lifecycle management.
Strong knowledge of fraud and financial crime systems (transaction monitoring, onboarding/KYC, real-time risk decisioning).
Experience building or maturing MLOps capabilities and frameworks, not just working within established functions.
STEM degree in Mathematics, Physics, Engineering, or Computer Science; Financial Services domain experience mandatory.
Experienced leader in productionising machine learning models with hands-on MLOps and pipeline automation expertise.
Deep domain understanding of financial services fraud and risk systems to drive ML analytical solutions.
Skilled in stakeholder management, team leadership in agile environments, and translating complex data insights to business impact.