VP Senior Financial Engineer - Paris - 14948BR

Moody's is an essential component of the global capital markets, providing credit ratings, research, tools and analysis that contribute to transparent and integrated financial markets. Moody's Corporation (NYSE: MCO) is the parent company of Moody's Investors Service, which provides credit ratings and research covering debt instruments and securities, and Moody's Analytics, which offers leading-edge software, advisory services and research for credit and economic analysis and financial risk management. The Corporation, which reported revenue of $4.2 billion in 2017, employs approximately 11,900 people worldwide and maintains a presence in 41 countries. Further information is available at www.moodys.com.
The VP Sr Financial Engineer working in Data Science is a key member of the Analytic and Technology Solutions (ATS) group in MIS. ATS is responsible for developing the quantitative models and analytical tools used in the rating process and across the rating agency, as well as MIS technology innovation activities, including advanced capabilities in machine learning and artificial intelligence.
The candidate will be responsible for helping manage application of the latest techniques in Machine Learning and Distributed Computing to drive business value. A successful candidate will not only be technically competent, but will be able to work collaboratively with business stakeholders to increase analytical efficiency, drive new business insights, and develop new products. The role also includes advocating for operational and process changes to move towards a more data driven organizational paradigm.
The duties of the role include:
  • Manage development and deployment of Machine Learning solutions for predictive analytics and NLP based projects
  • Provider leadership in staying up to date with the developments in relevant technologies and market trends (including statistical and machine learning methods) to identify enhancements
  • Evaluate external vendors and partners as needed from time to time
  • Work collaboratively with relevant stakeholders and partners to develop and release tools that address business needs
  • Evaluate and apply sound software and architectural development practices in development and deployment of models as software products.
  • Leverage cloud and distributed computing platforms for model development and deployment
  • Communicate results to business stakeholders and decision makers
  • Speak internally and/ or at external events as required

Qualifications

Required

  • Master’s Degree in Computer Science, Statistics, Applied Math, specialized Machine Learning program, or related field
  • Extensive practical experience in Machine Learning or statistics and/or distributed computing
  • Proven track record of successfully building machine learning models and other applications
  • Knowledge of Machine Learning techniques include Neural Networks, Tree-based Models, Linear model
  • Experience with one or more of the following programming languages is highly preferred: Python, R, Scala, and SQL
  • Experience with one or more of the following Machine Learning frameworks is preferred: TensorFlow, Scikit-Learn, or AWS Sage Maker
  • Experience with NLP knowledge and relevant machine learning libraries such as SpaCy, Stanford NLP is also preferred
  • Experience with Machine Learning on the Cloud is a plus
  • Experience with the following distributed compute and stream analytics platforms is preferred Apache Spark, Apache Kafka

Preferred

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Moody’s is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, sexual orientation, gender expression, gender identity or any other characteristic protected by law.
Candidates for Moody's Corporation may be asked to disclose securities holdings pursuant to Moody’s Policy for Securities Trading and the requirements of the position. Employment is contingent upon compliance with the Policy, including remediation of positions in those holdings as necessary.