CCB Card Risk Modeling Applied AI ML Lead

CCB Card Risk Modeling Applied AI ML Lead

Requirements

• Ph.D. or Master’s degree from an accredited university in a quantitative field such as Computer Science, Mathematics, Statistics, Econometrics, or Engineering
• Demonstrated experience in designing, building, and deploying production quality machine learning models
• Deep understanding of machine learning algorithms (e.g., regressions, XGBoost, CNN, RNN) as well as design and tuning
• At least 5 years of experience and proficiency in coding (e.g., Python, TensorFlow, Spark, or Scala) and big data technologies (e.g., Hadoop, Teradata, AWS cloud, Hive)

Responsibilities

• Design and develop machine learning models to drive impactful decisions for the card business throughout the customer lifecycle (e.g., acquisition, account management, transaction authorization, collection)
• Utilize cutting-edge machine learning approaches, and construct sophisticated machine learning models including deep learning architecture on big data platforms
• Work closely with the senior management team to develop ambitious, innovative modeling solutions and deliver them into production
• Collaborate with various partners in marketing, risk, technology, model governance, etc. throughout the entire modeling lifecycle (development, review, deployment, and use of the models)
• Present Model result and Adhoc research to senior leaders

Preferred Qualifications

• Experience in credit card industry with strong business acumen
• Demonstrated expertise in data wrangling and model building on a distributed Spark computation environment (with stability, scalability and efficiency). GPU experience is desired
• Experience in interpreting machine learning models such as XGBoost, GBM, etc. Experience in interpreting deep learning models is a plus
• Strong ownership and execution; proven experience in implementing models in production