Data Scientist

• Manage multiple software development teams throughout the software development life cycle
• Lead projects and ensure successful technical implementation of business software applications
• Assemble project plans, monitor work efforts, perform quality reviews, and escalate issues
• Develop or guide development of project governance documents covering objectives, scope, approach, and timeline
• Coordinate internal IT processes and resources to deploy new solutions
• Coordinate communication and prepare project status presentations for project teams, sponsors, and IT executives
• Track project milestones and adjust plans to meet objectives
• Oversee adherence to project and business governance standards and methodologies
• Manage one or more cross-functional projects
• Ensure compliance with software development guidelines, user interface standards, policies, procedures, and change control processes
• Complete performance reviews for direct reports
• Ensure projects are delivered on time and according to specifications
• Define, measure, and report project metrics
• Keep user representatives and team members updated on project and problem resolution status
• Oversee and participate in solution development and unit testing
• Obtain code reviews and ensure technical standards and policies are followed
• Propose design changes and improvements to processes and applications
• Perform other duties as assigned

Manager, Data Products

• Research, model, implement, and validate predictive and prescriptive algorithms to analyze diverse data sources
• Mentor and guide junior risk data scientists
• Plan and manage multiple concurrent projects
• Apply advanced quantitative solutions, models, tools, and analysis to solve complex business problems and manage risks
• Work with large, structured, and unstructured datasets
• Use quantitative and analytical techniques to support profitable growth and monitor and mitigate risk
• Research the ongoing appropriateness of existing or proposed quantitative solutions
• Use Big Data tools to build data analytics solutions
• Build machine learning and AI models from development through testing and validation
• Design rich data visualizations for business leaders and executives
• Communicate outcomes and proposed business solutions to senior leaders, external auditors, and prudential regulators
• Draw insights from data to support informed decisions
• Contribute across all model lifecycle phases, including research, data collection, development, variable selection, implementation, testing, documentation, monitoring, and validation

Risk Data Scientist

• Research, model, implement, and validate predictive and prescriptive algorithms to analyze diverse data sources and achieve targeted outcomes
• Work with multiple teams of data scientists, analysts, and visualization experts to solve complex business problems and enable effective risk management
• Work with large, structured, and unstructured datasets
• Use quantitative and analytical techniques to accelerate profitable growth and monitor and mitigate risk
• Use Big Data tools such as Hadoop, Spark, H2O, CDSW, and Domino Labs to build data analytics solutions
• Build machine learning and artificial intelligence models through development, testing, and validation
• Design rich data visualizations to communicate complex ideas to business leaders and executives
• Communicate outcomes and proposed business solutions to senior Risk Data Scientists
• Draw insights from data to support quick, well-informed decisions
• Continuously learn and provide value in a dynamic environment
• Manage all phases of the model lifecycle and ensure model documentation complies with model validation expectations