Come join us in reshaping the future!

As AI/ML Lead you will be working on  machine learning, big data and distributed computing with applications in credit card business.  The successful candidate will drive long-term profitable growth using AI / ML powered predictive models with strong business acumen, collaborate in a team environment, and effectively communicate results to senior management.

Job 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 platformsWork closely with the senior management team to develop ambitious, innovative modeling solutions and deliver them into productionCollaborate 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

Required qualifications, capabilities and skills

Ph.D. or Master’s degree from an accredited university in a quantitative field such as Computer Science, Mathematics, Statistics, Econometrics, or EngineeringDemonstrated experience in designing, building, and deploying production quality machine learning modelsDeep understanding of machine learning algorithms (e.g., regressions, XGBoost, CNN, RNN) as well as design and tuningAt 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) 

Preferred qualifications, capabilities and skills

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