• Drive technical roadmap to extend risk monitoring across identified threat surfaces.

• Develop/experiment/ship state-of-the-art prediction models

• Use excellent data science practices to iteratively produce high performing models

• ​​​​​​Create immediate impact through sound and practical deliveries of risk monitors

• Work with engineering colleagues to convey findings through data visualizations

• Measure, tune and refine existing algorithms to incrementally improve performance

• Analyze new and existing data after extracting. transforming and combining it in novel ways

• Convey needs to engineering and operations teams to ensure healthy feedback loops

• Attract and onboard new talent while preserving and enhancing existing culture

• Build strong partnerships and collaborate with other teams across the enterprise

• Demonstrate sense ownership and personal accountability for your team's work

Basic Qualifications:

• 5+ years applied data science experience, including 3 years of advanced analytics experience focused on enterprise-specific problem solving

• 2+ years management, mentoring, or other closely related team or people leadership experience

• Experience in machine learning (supervised, semi-supervised or unsupervised learning)

• Strong communication, delivery management, and leadership skills
  

Preferred Qualifications

• A bachelor’s degree, MSc or Ph.D. in Statistics, Data Science, Artificial Intelligence, or equivalent alternative education or experience

• Applied experience with SQL, also Python or R or Scala, and modern data science tools/packages e.g. PyTorch, Transformers, TensorFlow, scikit-learn

• Applied experience with Databricks and/or Azure ML

• Strong coding abilities in one or more scripting languages like Python or SQL

• Understanding of compliance, security, and risk domains along with associated patterns and data elements

• Understanding of product and services activation, use, and transaction models and data

• Understanding of statistical analysis and machine learning tools and practices

• Understanding of Cloud-centric data processing and visualization approaches including SQL and NoSQL databases with exposure to Azure SQL, Azure Cosmos DB, Data Factory, Synapse, Azure Data Lake, etc

• Familiarity with Agile software delivery including application lifecycle mgmt (SAFe, Azure DevOps/VSTS, Git)

All your information will be kept confidential according to EEO guidelines.