AVP, Model Ops Engineer

Role Overview

• Design, develop, and maintain robust pipelines to collect, transform, and store data used in model monitoring workflows (e.g., scoring data, performance metrics, outcomes)
• Build scalable data architectures to support real-time and batch monitoring, including data ingestion, enrichment, and retention practices
• Develop reusable monitoring components (e.g., performance drift detectors, threshold-based alerts, metric repositories) that support various model types and regulatory needs
• Integrate data pipelines with model lifecycle platforms, MLOps tools, and observability solutions to ensure seamless model performance tracking
• Partner with model risk and compliance teams to ensure data lineage, audit trails, and documentation are preserved and accessible for regulatory reviews (e.g., SR 11-7 compliance)
• Collaborate with data scientists, model validators, and product managers to align monitoring data infrastructure with evolving model monitoring requirements
• Work closely with the model monitoring analytics and strategy monitoring analytics teams within MO&A to ensure the monitoring data infrastructure adapts to changing analytics and monitoring needs
• Enable visualization and reporting capabilities through dashboards (e.g., Power BI, Tableau) that summarize model health, stability, and issue alerts
• Designing and maintaining high-performance data pipelines that ingest, transform, and version datasets for Model and Strategy Monitoring
• Optimize data storage and compute performance for large-scale monitoring use cases involving high-frequency scoring or model ensembles

Requirements

• Bachelor’s degree in a quantitative, technical, or data-focused field (e.g., Statistics, Mathematics, Computer Science, Data Science, Engineering) with 5+ years’ experience or in lieu of degree, and 7+ years of relevant work experience in, data engineering or related roles in the financial services or regulated analytics domain
• Strong proficiency with data engineering tools and frameworks (e.g., Apache Spark, Airflow, Kafka, dbt, PySpark)
• Proficient in programming languages such as SAS, Python, and SQL for building monitoring pipelines and validation checks
• Experience with cloud-based data infrastructure (e.g., AWS, Azure, GCP) and data warehousing (e.g., Snowflake, Redshift, BigQuery)
• Familiarity with MLOps practices, model metadata tracking (e.g., MLflow), and monitoring toolkits (e.g., Evidently AI, Why Labs, Prometheus)
• Understanding of model risk governance requirements and the role of data engineering in ensuring compliant model monitoring
• Ability to work in an agile environment and deliver high-quality, production-grade code in collaboration with DevOps and platform engineering teams

Tech Stack

• Airflow
• Amazon Redshift
• Apache
• AWS
• Azure
• BigQuery
• Cloud
• Google Cloud Platform
• Kafka
• Prometheus
• PySpark
• Python
• Spark
• SQL
• Tableau

Benefits

• best-in-class employee benefits and programs that cater to work-life integration and overall well-being
• career advancement and upskilling opportunities