AVP, Model Ops Engineer

This is a fully remote position, open to applicants in India.

📋 Description

• Design, develop, and maintain robust pipelines for the collection, transformation, and storage of data utilized in model monitoring workflows (e.g., scoring data, performance metrics, outcomes).

• Construct scalable data architectures to facilitate both real-time and batch monitoring, encompassing data ingestion, enrichment, and retention practices.

• Create reusable monitoring components (e.g., performance drift detectors, threshold-based alerts, metric repositories) that cater to various model types and regulatory requirements.

• Integrate data pipelines with model lifecycle platforms, MLOps tools, and observability solutions to guarantee seamless tracking of model performance.

• Collaborate with model risk and compliance teams to ensure the preservation and accessibility of data lineage, audit trails, and documentation for regulatory reviews (e.g., SR 11-7 compliance).

• Work alongside data scientists, model validators, and product managers to ensure that the monitoring data infrastructure aligns with evolving model monitoring needs.

• Partner closely with the model monitoring analytics and strategy monitoring analytics teams within MO&A to ensure that the monitoring data infrastructure evolves with changing analytics and monitoring demands.

• Facilitate visualization and reporting capabilities through dashboards (e.g., Power BI, Tableau) that provide summaries of model health, stability, and alerts for issues.

• Enhance data storage and compute performance for large-scale monitoring scenarios 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 4+ years of experience, or 7+ years of relevant work experience in data engineering in lieu of a degree.

• Minimum of 4 years of experience in MLOps, data engineering, or related roles within the financial services or regulated analytics sector.

• Strong expertise in data engineering tools and frameworks (e.g., Apache Spark, Airflow, Kafka, dbt, PySpark).

• Proficient in programming languages such as SAS, Python, and SQL for constructing monitoring pipelines and validation checks.

• Experience with cloud-based data infrastructure (e.g., AWS, Azure, GCP) and data warehousing solutions (e.g., Snowflake, Redshift, BigQuery).

• Familiarity with MLOps practices, model metadata tracking (e.g., MLflow), and monitoring toolkits (e.g., Evidently AI, WhyLabs, Prometheus).

• Understanding of model risk governance requirements and the significance of data engineering in ensuring compliant model monitoring.

• Capability to thrive in an agile environment and deliver high-quality, production-grade code in collaboration with DevOps and platform engineering teams.

🏝️ Benefits

• Health insurance

• Career advancement and upskilling opportunities

• Flexible work arrangements