Reshaping the future of the card business by driving long term profitable growth using ML / AI powered predictive models for card acquisition risk management decision process.
Requirements
- Exceptional coding skills with at least one-year professional experience in coding (e.g. Python, SAS, Spark, Scala, or Tensorflow) and big data platform (e.g., Hadoop, HDFS, Teradata, snowflake, AWS cloud, Hive) .
- Solid understanding of advanced statistical methods and machine learning techniques: GLM/Regression, Random Forest, Boosting Trees, Neural Network, Clustering, KNN, Anomaly Detection etc.
- Strong ability to interpret and form a coherent story with complex data and communicate to a wide range of audience with various degree of technical acumen including senior leadership and executives.
- Advanced problem-solving skills and exceptional analytical skills.
- Experience in interpreting / explaining machine learning models such as XGBoost, GBM etc.
- Proven experience in implementing models in production.
- Expertise in data wrangling and model building on a distributed Spark computation environment.
Responsibilities
- Design and develop machine learning models to drive impactful credit decisions for the card business throughout the credit card lifecycle (e.g., acquisition, account management, transaction authorization, collection).
- Leverage cutting-edge machine learning techniques, including deep learning architectures on big data platforms with key emphasis on interpretability and replicability of such techniques.
- Work closely with the senior management team to develop ambitious, innovative modeling solutions and implement them in production to drive significant business impact.
- Collaborate with various business partners in marketing, risk, technology, model governance, compliance etc. throughout the entire modeling lifecycle. (development, review, deployment and ongoing monitoring)
- Present model result and ad-hoc research to senior leaders.
Other
- Ph.D. or Master’s degree from an accredited university in a quantitative field such as Computer Science, Mathematics, Statistics, Econometrics, or Engineering.
- Strong ability to interpret and form a coherent story with complex data and communicate to a wide range of audience with various degree of technical acumen including senior leadership and executives.
- Experience in credit card industry with strong business acumen.
- Strong ownership and execution
