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.

Principle 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• Collaborate with Data Engineering, MLOps, IT, and data scientists to support controlled deployment, integration, monitoring, and change management for feature pipelines.
• Monitor feature pipeline stability, accuracy, and changes in input data; investigate failures and inconsistencies; and support timely remediation to maintain reliable model inputs.
• Maintain a catalogue of reusable feature sets, version control, and traceability for source data and transformation code, and report the number of feature sets developed and available for reuse.
• Measure and report improvements in model performance attributable to feature engineering, together with feature pipeline stability and accuracy, using agreed evaluation methods with data scientists.
• Apply appropriate controls for data confidentiality, customer privacy, access management, and secure handling of sensitive information, and share feature engineering practices that support responsible and evidence-based use of AI.

Qualifications Required

• Bachelors degree in Statistics, Mathematics, Computer Science, Data Science, Artificial Intelligence or a related field.
• Minimum 3 years of relevant experience in data science, machine learning, feature engineering, data engineering, advanced analytics.
• Relevant professional certifications in data science, machine learning, artificial intelligence, cloud computing, data engineering, or MLOps are an added advantage.
• Hands-on experience with machine learning frameworks and libraries such as scikit-learn, TensorFlow, or equivalent tools, including feature selection and dimensionality reduction.
• Practical knowledge of data pipelines, big-data processing, version control, reproducibility, feature monitoring, and MLOps practices.
• Good understanding of data governance, data quality, model risk management, responsible AI, information security, privacy, and applicable regulatory requirements.
• Strong analytical, problem-solving, communication, and stakeholder-management skills, with the ability to translate business requirements into effective feature solutions.
• Strong proficiency in Python and SQL, with practical experience in data exploration, data cleaning, transformation, statistical analysis, and feature engineering.

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• Collaborate with Data Engineering, MLOps, IT, and data scientists to support controlled deployment, integration, monitoring, and change management for feature pipelines.
• Monitor feature pipeline stability, accuracy, and changes in input data; investigate failures and inconsistencies; and support timely remediation to maintain reliable model inputs.
• Maintain a catalogue of reusable feature sets, version control, and traceability for source data and transformation code, and report the number of feature sets developed and available for reuse.
• Measure and report improvements in model performance attributable to feature engineering, together with feature pipeline stability and accuracy, using agreed evaluation methods with data scientists.
• Apply appropriate controls for data confidentiality, customer privacy, access management, and secure handling of sensitive information, and share feature engineering practices that support responsible and evidence-based use of AI.

• Strong proficiency in Python and SQL, with practical experience in data exploration, data cleaning, transformation, statistical analysis, and feature engineering.
• Hands-on experience with machine learning frameworks and libraries such as scikit-learn, TensorFlow, or equivalent tools, including feature selection and dimensionality reduction.
• Practical knowledge of data pipelines, big-data processing, version control, reproducibility, feature monitoring, and MLOps practices.
• Strong analytical, problem-solving, communication, and stakeholder-management skills.

• Bachelors degree in Statistics, Mathematics, Computer Science, Data Science, Artificial Intelligence or a related field.
• Good understanding of data governance, data quality, model risk management, responsible AI, information security, privacy, and applicable regulatory requirements.