As a Credit Forecast Transformation Associate in the team, you will lead the loss forecast transformation project, enabling the Loss Forecasting team to deliver robust and flawless end-to-end Net Charge-Offs (NCOs) forecast execution, from model input to output provision. You will establish best practices in both model and non-model dataset creation and consumption, and support the implementation of the '1-click execution' vision across the Forecasting team.
Job responsibilities
- Lead working group discussions to streamline processes for both model and non-model components, as well as NCO dataset variables, creating comprehensive outputs for reporting and analytics
- Streamline both model and non-model components processes and NCOs dataset variables, creating comprehensive outputs that will be used most of the time
- Manage the projects and communicate solutions, roadmaps, and progress to multiple stakeholders (Forecasting team, Project Management, Finance, Product Owner, and Technology)
- Assist the Forecasting team in automating non-model loss forecast components (e.g., QMs, management judgment, overlays) in Cloud (e.g., FRAME/Databricks)
- Collaborate with multiple stakeholders to enable AWS services and capabilities for fine-grain Loss forecast analytics by defining requirements for a semantic layer or reporting cube for easy retrieval of overlays and reporting processes;
- 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
Required qualifications, capabilities, and skills
- Strong problem-solving and interpersonal skills: a highly motivated, proactive, team player, with the ability to challenge the status quo and to manage multiple projects simultaneously, and ready to work in a fast-paced environment
- Proficient in data aggregation and analytical/ML tools (e.g., SQL, Python / PySpark dataframe)
- 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, 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
- Prior experience with forecast execution or analytics in finance, loss forecasting, risk management
- Comfortable to present findings and recommendations to senior management and other stakeholders.