FEATURE ENGINEERING SPECIALIST

Job purpose

The Feature Engineering Specialist is responsible for designing, developing, testing, and continuously improving features and feature pipelines that support accurate and reliable machine learning solutions and deliver measurable business value in banking, while complying with the Bank’s AI governance, model risk management, data governance, information security, privacy, and regulatory requirements. The role ensures features the data inputs used by models are relevant, reusable, traceable, well-documented, and consistent between model development and operational use throughout their lifecycle.
Principal responsibilities
Design, develop, and maintain features and feature pipelines for priority banking use cases such as fraud detection, credit risk assessment, collections, customer analytics, and operational efficiency.
Translate business requirements into feature specifications with business teams and data scientists, define success criteria, and ensure proposed features align with approved business objectives and governance requirements.
Perform data exploration, cleaning, transformation, and statistical analysis; handle missing values and outliers; and create meaningful features such as transaction patterns and customer activity measures.
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Apply feature selection and dimensionality reduction techniques to retain useful information, remove redundant inputs, and improve model efficiency and predictive performance in collaboration with data scientists.
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Build and maintain reliable, scalable feature pipelines with Data Engineering and MLOps teams, ensuring consistent transformation rules and feature values between model training and operational use.
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Test feature quality, accuracy, completeness, and availability; prevent data leakage by excluding information that would not be available at prediction time; and retain reproducible evidence of testing.
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Prepare complete feature documentation, including business rationale, definitions, data sources, transformation rules, assumptions, limitations, and version history, to support reuse, model review, approval, and audit.
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Ensure features and pipelines are developed and maintained in line with the Bank’s AI governance framework, model risk management standards, data governance requirements, responsible AI principles, and applicable regulatory obligations.
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Assess and address feature-related risks involving data quality, bias, privacy, and stability, and escalate material issues through the appropriate governance channels in collaboration with data scientists and control owners.
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Collaborate with Data Engineering, MLOps, IT, and data scientists to support controlled deployment, integration, monitoring, and change management for feature pipelines.
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