Data Scientist [multiple Positions Available]

JPMorgan Chase JPMorgan Chase · Banking · Jersey City, NJ +1 · Asset & Wealth Management

Data Scientist at JPMorgan Chase focused on building and training production-grade ML models for financial use cases, including forecasting, anomaly detection, NLP, and LLMs. Requires experience with deep learning, transformer models, and ML deployment in a financial domain.

What you'd actually do

  1. Build and train production grade machine learning models on large-scale datasets to solve business use cases.
  2. Use large-scale data processing frameworks to manipulate and extract value from both structured and un-structured data.
  3. Solve various business use cases involving forecasting and anomaly detection using Deep Learning models like Natural language Processing and Large Language Models.
  4. Perform data modeling experiments, evaluating against strong baselines, and extracting key statistical insights and/or cause and effect relations.
  5. Create data models using best practices to ensure high data quality and reduced redundancy.

Skills

Required

  • developing and deploying machine learning techniques in financial domains
  • regression
  • classification
  • clustering
  • time series analysis
  • designing and implementing financial engineering models
  • developing and refining Natural Language Processing models
  • designing and tuning language models using deep neural networks
  • sequence prediction
  • language modeling
  • implementing transformer-based models
  • BERT
  • GPT
  • Focal loss function
  • conducting Monte Carlo simulations
  • performing matrix computations
  • applying linear algebra techniques
  • Spark
  • Hadoop
  • Shell Scripting
  • Pentaho
  • Qliksense Server Side Extensions
  • Kubernetes
  • Spark on Kubernetes
  • machine learning (ML) model deployment
  • Jenkins pipeline

What the JD emphasized

  • production grade machine learning models
  • large-scale datasets
  • Deep Learning models
  • Natural language Processing
  • Large Language Models
  • transformer-based models
  • ML model deployment

Other signals

  • production grade machine learning models
  • large-scale datasets
  • Deep Learning models
  • Natural language Processing
  • Large Language Models
  • data modeling experiments
  • transformer-based models
  • ML model deployment