(GDO14) | Risk- Consumer&Community Banking - Quantitative Modeling - Associate

(GDO14) | Risk- Consumer&Community Banking - Quantitative Modeling - Associate

25 Dec
Bangalore Urban

25 Dec


Bangalore Urban

The Risk Modeling Team is a center of excellence within CCB Risk. We focus on the innovation and use of advanced machine learning modeling including deep learning to develop models that are used in day to day credit risk assessment, and fraud prevention. We are looking for candidates with demonstrated expertise in machine learning to join our talented team. The candidate will be responsible for end-to-end machine learning model design and development. Success in this role requires a strong foundation in machine learning, coupled with experience in big data and distributed computing. In this highly visible role, the successful candidate will be able to think like an analytic leader with business acumen, collaborate in a team environment,

and communicate results effectively to senior management.

Risk Modeling - Machine Learning Data Scientist (Associate)

- Design and develop machine learning models to drive impactful decisions for the consumer checking and 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)

Basic Qualifications

- 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 3 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

- 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 deep learning models is a plus

- Strong ownership and execution; proven experience in taking models to production


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