Manager, Risk Analytics and Modeling, PricewaterhouseCoopers Advisory Services LLC, Dallas, TX. Help organizations navigate risk and drive business results using smarter data analytics. Perform analysis related to risk and business continuity management in areas such as risk assessment, business impacts, recovery strategies, and crisis management plans. Help develop or validate statistical, financial engineering, and AI/machine learning models in the areas of credit risk, market risk, assets and liabilities management, fraud detection, anti money laundering and other functional modeling and analytics areas. Employ knowledge of organizational technology capabilities, including business intelligence, data management, and data assurance that help our clients drive innovation, growth, and change within their organizations. Work with clients to develop and strengthen risk and compliance functions through governance, policy, control design, and internal controls. Supervise and coach teams.
Must have a Bachelor's degree or foreign equivalent in Mathematics, Statistics, Engineering, Data Science, Economics or a related field, plus 5 years of post-bachelor’s, progressive related work experience.
In the alternative, the employer will accept a Master's degree or foreign equivalent in Mathematics, Statistics, Engineering, Data Science, Economics or a related field, plus 3 years of related work experience.
Must have at least one year of experience with each of the following:
- Performing conceptual and technical validation of client's complex statistical models within the risk frameworks;
- Using one or more of the following programming or data analytics tools: SQL, SAS, R, Python, Ruby, QlikView, Tibco Spotfire, or Tableau;
- Leveraging SQL Server Management Studio (SSMS) to design, develop, and maintain SQL queries, stored procedures, and views to perform data extraction, data cleaning, and data analytics tasks;
- Utilizing JIRA's agile project management capabilities to capture data requirements, define issues and user stories, and track progress using workflows and dashboards; and
- Applying techniques across one or more of the following machine learning models: multivariate regressions, Bayesian probabilities, clustering algorithms, machine learning, dynamic programming, stochastic-processes, queuing theory, and/or algorithmic knowledge.
80% telecommuting permitted. Must be able to commute to the designated local office.
Domestic and/or international travel up to 80% is required.
