Senior ML Engineer at JP Morgan Chase (Herzliya, Israel)

Global Payment Guardian is Wholesale Payment’s world class fraud screening application.  We are is currently in need of a Data Scientist/Machine Learning engineer to join our fast-growing team. The ideal candidate will be intricately involved in running analytical experiments in a methodical manner, and will regularly evaluate alternate models via theoretical approaches. This is a great opportunity for the successful candidate to become a part of an innovative team that analyzes data to develop tools to help fight payment fraud for our clients.


  • Collaborate with business, operations and other technology colleagues to understand company needs and devise possible solutions
  • Research and analyze data sets using a variety of statistical and machine learning techniques
  • Communicate results and ideas to key decision makers
  • Document approach and techniques used.  
  • Work on longer term projects, building tooling that can be used to scale certain types of analyses across multiple datasets and business use cases.
  • Collaborate with other J.P. Morgan machine learning teams.
  • Keep up-to-date with latest technology trends

Required Technical Qualifications and experience

  • MS or PhD in a Data Science or related discipline, e.g. Computer Science, Applied Mathematics, Statistics, Physics, Artificial Intelligence
  •  5+ Years of Advanced data mining and EDA (Exploratory Data Analysis) skills
  • Strong ability to develop and debug in Python (must) and Java (would be a plus).
  •  3+ years of experience with machine learning APIs and computational packages (examples: TensorFlow, LightGBM, PyTorch, Keras, Scikit-Learn, NumPy, SciPy, Pandas, H2O, SHAP, Catboost)
  • Good experience with model explainability
  • 5 + years of experience with big-data technologies such as Hadoop, Spark, SparkML, etc
  • Able to understand various data structures and common methods in data transformation
  • Excellent pattern recognition and predictive modeling skills
  • Experience with large imbalanced datasets

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