You enjoy thinking outside the box and exceeding expectations. You will work in partnership with model users (e.g., Loss Forecasting, Finance, Controller), Finance Automation & Execution (FAE), and Technology to enhance the Credit Loss data consumption in the Cloud.

Job Summary:

As a Credit Forecast Transformation Senior Associate, you will lead projects to ensure seamless access to historical and forecast data while adding valuable insights. You'll establish best practices in data consumption and BI tool solutions for reporting and analytics, overcome obstacles, drive discussions to achieve the target state, and support the '1-click execution' vision for the Forecasting team.

Job Responsibilities:

Develop a unified reporting and analytics suite/platform for all teams within Consumer and Community Banking (CCB) Loss Forecasting. Collaborate with stakeholders to build requirements for a semantic layer and cube to ensure easy data retrieval for reporting and analytics purposes; enable AWS services and capabilities for fine-grain forecast analytics. Support the automation of non-model components (e.g., Qualitative Models/QMs, management judgment/overlays, etc.) in Frame/Databricks. Effectively communicate solutions, roadmaps, and progress to key stakeholders, including the Forecasting team, Project Management, Finance, and Technology. Facilitate training and mentoring among peers on cloud capabilities and offerings, as well as analytical and reporting tools. Ensure firmwide controls and governance are followed, escalate issues and risks appropriately, and collaborate with key stakeholders for resolution and recurrence prevention. Foster an environment of continuous improvement.

Preferred Qualifications, Capabilities, and Skills:

Strong problem-solving and interpersonal skills: a highly motivated, proactive team player, confident in challenging the status quo, able to manage multiple projects simultaneously, and ready to work in a fast-paced environment. Prior experience with forecast execution or analytics in finance, loss forecasting, risk management, or a related capacity. Proficient in data aggregation and analytical/ML tools (e.g., SQL, Python, or PySpark data frame). Experience with both structured and unstructured data, as well as semantic layers and cube dimensions. Familiarity with cloud offerings and capabilities such as S3 Bucket, EMR, SageMaker, Databricks, QuickSight, or ThoughtSpot, and data catalog tools. Knowledge of Agile/Productivity tools (e.g., JIRA, Confluence, GitHub, IAHUB). Proficiency in MS Office (Excel, Word, PowerPoint, Visio) for creating procedures, process maps, and data analysis. Ability to present findings and recommendations to senior management and other stakeholders.

All application requirements (including updated resume - please include specifics of your career) should be posted, submitted and completed in the Oracle tool.