Applied AI/ML Associate

Role Overview: The Data Science and Capability team within CCB Card Modeling is a Center of Excellence dedicated to ensuring superior quality of data used in CCB Card models and strategies. The team utilizes a combination of traditional and advanced techniques, including Artificial Intelligence, to achieve this goal. Your role in the team is crucial in safeguarding the business by maintaining data quality and deriving actionable insights for revenue growth and risk mitigation in the card business. Key Responsibilities: - Design and develop an attribute library to enhance the consistency and quality of data utilized in machine learning models across the customer lifecycle - Collaborate with senior management to implement cutting-edge machine learning approaches for developing innovative attributes and modeling solutions - Conduct attribute quality testing and monitoring using modern techniques to enhance data accuracy and consistency - Evaluate the effectiveness and accuracy of new data sources and gathering techniques - Work closely with partners in various departments such as marketing, risk, technology, and data governance to ensure collaboration throughout the attributing and modeling lifecycle - Take ownership of the quality of attributes and models developed, ensuring adherence to accurate development standards and proper implementation - Contribute to the knowledge base of the group by proposing new approaches to analytic problems and project design Qualifications Required: - Masters / Phd degree in a quantitative field like Computer Science, Mathematics, Statistics, Econometrics, or Engineering from an accredited university - 6 years of experience in working with large data and developing/managing attributes and predictive models in the credit card industry - Proficiency in coding languages like Python, SAS, Spark, Scala, or Tensorflow, and big data platforms such as Hadoop, HDFS, Teradata, AWS cloud, Hive - Solid understanding of advanced statistical methods and machine learning techniques like GLM/Regression, Random Forest, Boosting, neural networks, clustering, etc. - Strong problem-solving and analytical skills, with the ability to interpret data effectively - Excellent communication skills, especially in interactions with senior management - Experience working with US stakeholders is advantageous - Ability to deliver high-quality results under tight deadlines and handle the analysis of large data sets efficiently.,