Moody’s Analytics: Machine Learning / NLP – Research Scientist / Engineer [New York, NY]

Moody's Analytics is seeking a Machine Learning / NLP - Research Scientist / Engineer in New York, NY, to drive algorithmic improvements by enabling new automation capabilities, and improving efficiency and performance across multiple business lines.

At: Moody's AnalyticsMoody's Analytics
Location: New York, NY
Web: Moody's Analytics (LinkedIn)
Position: Machine Learning / NLP - Research Scientist / Engineer


As a Machine Learning / NLP - Research Scientist / Engineer in the Machine Learning team, you will drive algorithmic improvements in Moodys Analytic’s core machine learning and AI-driven products, that have a transformative impact across multiple MA units by enabling new automation capabilities, and improving efficiency and performance across multiple business lines. You’ll leverage your expertise and experience to propose and lead initiatives in problems like supervised and unsupervised learning, classification, predictive modeling, risk modeling , sentiment analysis, relation extraction, text summarization, entity recognition, information retrieval, and natural language generation as well as designs for the next iterations of our product lines, using ML, deep learning, and NLP.

The Machine Learning / NLP-  Research Scientist will be a core member of the Machine Learning team within the Moody's Analytics Accelerator (formerly known as the Emerging Business Unit ). The ML team is a highly visible team working across multiple business lines that is key to the Moody’s Analytics’ (MA) long-term growth strategy using AI and ML.


  • Do research on emerging machine learning, deep learning and NLP solutions applied to natural language (text) and unstructured data and be conversant with the latest developments in these fields.
  • Enable new capabilities in document understanding and knowledge extraction from text using state-of-art deep learning techniques and frameworks.
  • Deliver custom, highly scalable deep learning, and NLP solutions through prototyping, POC, and quantitative metrics.
  • Propose and develop new systems for evaluating model accuracy and building better-annotated training corpora by developing data collection and annotation processes.
  • Discuss, suggest, and brainstorm new advanced technology solutions with team members.
  • Explain complex models to non-experts, in layperson terminology to clients, stakeholders, and managers, while also being able to discuss intricacies of complex algorithms with experts in the field.
  • Prepare reports, presentations, for internal and external stakeholders, and as applicable, publish in conferences and peer-reviewed journals.


  • Ph.D. or MS in computer science, statistics, machine learning, or other quantitative fields.
  • 5+ years of applied R&D experience (as a PhD student, or post-doc) or professional experience (research staff in a university or industry) developing and deploying data and algorithm-driven software products.
  • Strong programming skills (8+ years of programming experience) in Python, Java, R, or Scala.
  • In-depth knowledge of NLP and machine learning libraries such as SpaCy, NLTK, Stanford NLP, Numpy, and Scikit-learn.
  • Experience with specialized NLP tasks such as sentiment analysis, relation extraction, summarization, entity recognition, document classification, and knowledge base generation.
  • Publications in top-tier venues in the field of Machine Learning, Deep Learning, NLP or Computational Linguistics.
  • Hands-on working experience with deep learning and Big Data frameworks such as TensorFlow, Keras, PyTorch, Caffe,,  MXNet, Spark, or Hadoop.
  • Experience beyond using open source tools as-is, and writing custom code on top of, or in addition to, existing open source frameworks.
  • Excellent communication skills (oral and written) to explain complex algorithms, solutions to stakeholders across multiple disciplines, and the ability to work in a diverse team

Machine Learning – AI Team / Moody’s Analytics Accelerator / Emerging Business Unit

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Engineering & Technology

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