FEATURE ENGINEERING SPECIALIST

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.

Requirements

• one position

Qualifications

• 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.

Skills