Senior Director

  • 37793
  • Non-Life - Data Science
  • |
  • Massachusetts, United States
  • |
  • Jul 23, 2020
Insurance
RESPONSIBILITIES
  • Lead cross-functional R&D teams, including ODS, academic researchers, Corporate, and business-unit data scientists/analysts, doing hands on ML research in areas of:
  • Privacy preserving data science.
  • Reducing social bias from training data and models.
  • Developing robust and reliable ML.
  • Developing explainable ML applications with algorithmic accountability.
  • Lead a Privacy Preserving ML collaboration with a leading academic institution and the Company Privacy Office.
  • Support development of DS/ML infrastructure that supports Trusted AI including integrating model controls that reduce risk of abnormal model behaviour.
  • Collaborate with Enterprise Risk Management, Corporate Actuarial, and Global Compliance and Ethics on efforts to reduce the risk of data science and machine learning.
  • Educate data scientists on model development and deployment best practices.
  • Educate executives and business users on Trusted AI and the proper use of AI.
  • Keep up to date on current literature, attend conferences, and run seminars to keep the company at the cutting edge of the field.
  • Work with business units across the company to educate executives and business users on ethical considerations around AI, including proper usage of AI, algorithmic bias, and risk management.
QUALIFICATIONS
  • Demonstrated experience leading projects related to the ethical application of data science.
  • Proven ability to lead and drive high profile cross-functional projects and assignments to completion through others.
  • A broad understanding of privacy-related technologies and frameworks such as differential privacy required.
  • Experience building stakeholder trust and confidence in deployed models, especially via application of techniques in the fields of algorithmic bias, interpretable machine learning, and/or robust machine learning.
  • Expert knowledge of machine learning.
  • Expert knowledge of data science and machine learning current best practices.
  • Experience modeling unstructured data including computer vision and/or natural language processing a definite plus.
  • Competencies typically acquired through an advanced degree and 8 years of relevant experience or may be acquired through a Bachelor’s degree and 10 years of relevant experience.