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JPMorgan Risk Modeling - Machine Learning Associate 
India, Karnataka, Bengaluru 
806714318

Yesterday

Job responsibilities

  • Utilize novel approaches to design and develop sophisticated machine learning models to drive impactful decisions for the business.
  • Leverage big data/distributed computing/cloud computing platforms to optimize and accelerate model development processes.
  • Work closely with the senior management team to develop ambitious, innovative modeling solutions to cater business need and deliver them into production.
  • Collaborate with various partners in marketing, risk, technology, model governance, fair lending etc. through the modeling lifecycle (conceptualization, development, review, deployment, model usages and model maintenance)

Required qualifications, capabilities, and skills

  • Advance degree in Mathematics, Statistics, Computer Science, Operational Research, Econometrics, Physics, or other related quantitative fields
  • Minimum 6 years of experience in developing and managing predictive risk models in financial industry.
  • Minimum 4 years of experience and proficiency in coding (Python, Tensorflow or PyTorch, PySpark, SQL), familiarity with cloud services (AWS Sagemaker, Amazon EMR)
  • Fundamental understanding of consumer lending business.
  • Deep understanding of advanced machine learning algorithms (e.g. random forest, XGBoost, Neural Networks etc) as well as design and tuning procedures
  • Effective communication

Preferred qualifications, capabilities, and skills

  • Demonstrated experience in designing, building, and deploying production quality machine learning models. Experience in interpreting deep learning models is a plus.
  • Demonstrated expertise in data wrangling and model building on a distributed Spark computation environment (with stability, scalability and efficiency). GPU experience is a plus.
  • Strong ownership and execution skills, proven experience of implementing models in production.
  • Ability to collaborate with multiple stakeholders on projects of strategic importance.