Come and join us to reshaping the future!

Job Summary-

As a AI ML Sr associate in Card Risk Modeling team, you will be tasked to leverage cutting-edge technology to reimage how we should develop the next generation of fraud detection and generative models that would help our business partners identify emerging fraud patterns and enhance our capabilities against them. You will work with a team of talented people on projects that will have impact across various lines of business.

 

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). Research, develop, document, implement, maintain, and support tools and frameworks for AI/ML model explain ability and fairness. Utilize cutting-edge machine learning approaches and construct sophisticated machine learning models including deep learning architectures 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, research etc. throughout the entire modeling lifecycle (development, review, deployment, and use of the models)

 

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 Engineering Demonstrated experience in designing, building, and deploying production quality machine learning models. Deep understanding of advanced machine learning algorithms (e.g., regressions, XGBoost, Deep Neural Network – CNN and RNN, Clustering, Recommendation) as well as design and tuning. At least one year 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 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.