Risk Data Scientist

Quick Facts

• Builds, implements, and validates predictive and prescriptive risk models using structured and unstructured data
• Mentors junior risk data scientists and manages multiple concurrent projects
• Communicates results to business leaders, executives, and regulators

Description

The Risk Data Scientist researches, models, implements, and validates predictive and prescriptive algorithms to analyze diverse data sources and achieve targeted business outcomes. The role works across teams of data scientists, analysts, and visualization experts, mentoring junior team members while contributing independently to complex business and risk management problems. Responsibilities cover the full model lifecycle, from research and data collection through development, testing, documentation, and ongoing monitoring.

Responsibilities

• Mentor and guide junior risk data scientists
• Plan and manage multiple concurrent projects
• Leverage advanced quantitative solutions, models, tools, and analysis to evaluate and solve complex business problems and manage a wide spectrum of risks
• Work with large structured and unstructured datasets
• Use quantitative and analytical techniques to support profitable growth while monitoring and mitigating risk
• Research and assess the ongoing appropriateness of existing or proposed quantitative solutions
• Use Big Data tools (e.g., Hadoop, Spark, H2O, CDSW, Domino Labs) to build analytics solutions
• Build machine learning and AI models from development through testing and validation
• Design rich visualizations to communicate complex ideas to business leaders and executives
• Communicate outcomes and proposed solutions to senior leaders, external auditors, and prudential regulators
• Draw insights from data to make quick, well-informed decisions
• Continuously learn and deliver value in a dynamic environment
• Shepherd models through the model validation process and ensure ongoing monitoring

Requirements

• Bachelor’s degree with 6 years of related experience, or Master’s degree with 4 years, or Ph.D. with 4 years in a quantitative/analytical/STEM field
• 2 years hands-on experience with Big Data technologies such as Hadoop, Hive, Impala, Spark, or Kafka
• 3 years working experience with statistical and predictive modeling concepts and approaches (machine learning, clustering and classification, AI)
• 3 years working programming experience analyzing large, complex, multi-dimensional datasets using tools such as SAS, Python, Ruby, R, Matlab, Scala, or Java

Benefits

• Paid vacation and sick time
• 401(k) with company match
• Medical, dental, and vision benefits
• Disability benefits
• Health savings account
• Flexible spending account
• Life insurance
• Parental leave
• Employee assistance program
• Associate volunteer program