01Key Responsibilities
Engage regularly with model developers, validators, and risk stakeholders to understand their evolving data needs for model development, monitoring, and governance. Partner with credit analytics, risk, fraud, marketing, and operations functions to identify, define, and prioritize use cases requiring model-ready data. Build scalable data architectures to support real-time and batch monitoring, including data ingestion, enrichment, and retention practices.Support pipeline development by designing and maintaining automated end-to-end ML pipelines for data collection, preprocessing, feature engineering, and model training.Conduct data transformation by converting raw observations into variables (features) that machine learning models can understand, such as turning timestamps into cyclical time features. Transforming theoretical data science prototypes into robust, high-performance software systems that can handle large volumes of real-time dataCI/CD Pipeline Development: Build and maintain automated pipelines that handle not just code, but also data validation, model training, and artifact managementDesign, develop, and maintain robust pipelines to collect, transform, and store data used in model monitoring workflows (e.g., scoring data, performance metrics, outcomes).Provide thought and technical leadership in generating new signals from raw data by applying techniques such as normalization, scaling and categorical encodingIntegrate 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).Liaise with cloud, data lake, data warehouse, and model governance engineering teams on delivery execution and backlog prioritization. Collaborate with data scientists, model validators, and product managers to align monitoring data infrastructure with evolving model monitoring requirements.Optimize data storage and compute performance for large-scale monitoring use cases involving high-frequency scoring or model ensembles.
Required Skills & Knowledge:
Bachelors degree in a quantitative, technical, or data-focused field (e.g., Statistics, Mathematics, Computer Science, Data Science, Engineering) with 6+ years experience OR in lieu of a degree 8+years of relevant work experience in monitoring, validation, or credit risk strategyMinimum 6+ years of professional experience in model operations, data engineering, or analytics infrastructure 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 .
