CCB Card Risk Modeling Applied AI ML Lead
CCB Card Risk Modeling Applied AI ML Lead
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Salary Not Disclosed
1 Vacancy
Job Description
Come join us in reshaping the future!
As AI/ML Lead you will be working on machine learningbig data and distributed computing with applications in credit card business. The successful candidate will drive longterm profitable growth using AI / ML powered predictive models with strong business acumencollaborate 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 cuttingedge 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
Required qualifications capabilities and skills
- Ph.D. or Masters 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)
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
Employment Type
Full-Time
