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Metro location and mid-level MLOps role at a known financial firm create moderate candidate competition.
Model governance and credit-risk expertise required increases industry specificity and reduces transferability.
Explicit 6+ years, mandated MLOps/data engineering stack and governance experience make shortlisting strict.
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Design and build data infrastructure and pipelines for automated post-deployment model monitoring.
Develop and maintain scalable end-to-end ML pipelines and CI/CD automation integrating data validation, model training, and artifact management.
Collaborate with model developers, risk, compliance, and product teams to operationalize monitoring aligned with governance and regulatory compliance.
Bachelor’s degree in a quantitative or data-focused field plus 6+ years experience OR 8+ years relevant work experience without degree.
Minimum 6+ years professional experience in model operations, data engineering, or analytics infrastructure.
Proficiency in data engineering tools (Apache Spark, Airflow, Kafka, dbt), programming (SAS, Python, SQL), and cloud platforms (AWS, Azure, GCP).
Work timing: Must be available between 06:00 AM – 11:30 AM Eastern Time for coordination with US/India teams.
Experienced in operationalizing ML models with strong skills in scalable data pipeline architecture and MLOps tools.
Comfortable navigating model risk governance and regulatory compliance requirements within model monitoring workflows.
Capable of collaborating cross-functionally with model risk, compliance, data science, and product teams in an agile environment to deliver production-grade solutions.