As an Artificial Intelligence (AI) / Machine Learning - Research Scientist / Engineer in the AI - Machine Learning team, you will implement and lead improvements in Moody's Analytic's core machine learning and AI-driven products, that have a transformative impact across multiple MA units by enabling new products, 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, NLP (natural language processing) and text analytics, information retrieval, cognitive search, knowledge graphs, relation extraction, recommendation systems, predictive modeling, forecasting, time-series analysis, and risk modeling for the next generation of our product lines, using AI, ML, deep learning. The AI / Machine Learning Research Scientist will be a core member of the AI-Machine Learning team within Moody's Analytics. The AI-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.
- Ph.D. or MS in Computer Science, Statistics, Mathematics, Economics, or other quantitative fields, with focus on Machine Learning , AI , NLP , deep learning, and/or / data-driven statistical analysis & modelling
- 5+ years of applied R&D experience (as a PhD stdent, or post-doc) or professional experience (research staff in a university or industry) developing and deploying data-driven and algorithm-driven models and software products.
- programming skills (5+ years of programming experience) in Python, Java, C/C++, R, or Scala. - In-depth knowledge of AI and machine learning libraries such as TensorFlow, PyTorch, Keras, Numpy, and Scikit-learn, and/or open-source/commercial graph databases
- Experience in some of the following AI, ML areas: o NLP and machine-learning-based algorithms, solutions for natural language understanding, entity recognition, document understanding, co-ref resolution, relation extraction, as well as with the creation and evaluation of annotated training corpora.
- Publications in top-tier venues in the field of Machine Learning, Deep Learning.
- Hands-on working experience with deep learning and Big Data frameworks such as TensorFlow, Keras, PyTorch, Caffe, Fast.ai, MXNet, Spark, or Hadoop, and opensource/commercial graph databases.
- 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 ability to work in a diverse team
- Do research on emerging AI, machine learning, deep learning solutions applied to structured and unstructured data and be conversant with the latest developments in these fields. Areas of interest include:
- Deliver custom, highly scalable AI, deep learning, 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.
Equal Opportunity Employer: Race, Color, Religion, Sex, Sexual Orientation, Gender Identity, National Origin, Age, Genetic Information, Disability, Protected Veteran Status, or any other legally protected group status.