Sr. ML Scientist

• As a Senior ML Scientist you will help design, enhance, and build next generation fraud detection solutions in an agile development environment.
• Formulate business problems as technical data problems while ensuring key business drivers are captured in collaboration with product stakeholders.
• Work with software engineers to ensure feasibility of solutions. Deliver prototypes and production code based on need.
• Experiment with in-house and third-party data sets to test hypotheses on relevance and value of data to business problems.
• Build needed data transformations on structured and un-structured data.
• Build and experiment with modeling and scoring algorithms. This includes development of custom algorithms as well as use of packaged tools based on machine learning, data mining and statistical techniques.
• Devise and implement methods for adaptive learning with controls on effectiveness, methods for explaining model decisions where necessary, model validation, A/B testing of models.
• Devise and implement methods for efficiently monitoring model effectiveness and performance in production.
• Devise and implement methods for automation of all parts of the predictive pipeline to minimize labor in development and production.
• Contribute to development and adoption of shared predictive analytics infrastructure.
• Mentor and train other ML scientists on the team on key solutions

Basic Qualifications:
2+ years of relevant work experience and a Bachelors degree, OR 5+ years of relevant work experience

Preferred Qualifications
3 or more years of work experience with a Bachelor s Degree or more than 2 years of work experience with an Advanced Degree (e.g. Masters, MBA, JD, MD)

Training and/or deep experience in mathematical concepts underlying statistics, machine-learning, algorithms, and data structures.
Intermediate to Expert programming skills in at least one of Python, C, Scala.
Experience working with large datasets using tools like PySpark.
Experience in Java is a plus.
Experience in building and deploying models as micro-services.
Publications or presentation in recognized Machine Learning and Data Mining journals/conferences is a plus.