ParamInfo
Website:
paraminfo.com
Job details:
Job Title: Lead Model Developer
Job Location: Remote, India
Experience: 5+ Years
Notice Period: Maximum 30 Days
Budget: Up to 27 LPA - Fixed components
Domain: International Banking
Skills: Lead Model developer, Wholesale Model Monitoring, Data Management, Financial Risk Management, Python, SQL, Credit Risk or PD model/model development, Banking processes, Oracle
Job Description:
Key Result Areas:
Technical & Delivery Responsibilities
- Support the data preparation and model development, validation, and lifecycle management of Wholesale Rating Models
- Support model development and validation including:
- Corporate/Wholesale Rating Models including Large Corporate, Mid Corporate, SME, Sovereign, FI, HNWI, Project Finance, and Specialized Lending
- Explore new approaches for rating model development for low default portfolios
- Master Rating Scale (MRS) calibration
Mandatory / Essential:
- Strong foundation in Statistics and Mathematics.
- At least 4-5 years of banking experience, out of which 1-2 years are spent in related areas
- Strong analytical skills and knowledge on Financial Risk Management
- At least 2-3 years of banking experience in related areas
- Excellent computer skills (especially R, Python, SAS, Excel, Visual Basic, SQL & Oracle)
Data Management
- Lead end-to-end data lifecycle management for model development, including sourcing, profiling, transformation, and documentation.
- Identify data requirements for model development.
- Coordinate with IT and data engineering teams to streamline data pipelines required for model development .
Wholesale Model Monitoring
- Outline the goals of model monitoring, considering factors like accuracy and other relevant metrics based on the model purpose
- Determine the expected performance of the respective models on historical data
- Implement monitoring tools to continuously assess model performance. This includes monitoring input data quality, predictions
- Communicate the model performance to relevant stakeholders to ensure the prompt attention to potential problems.
- Regularly review and improve the model performance process based on the feedback, change in underlying data and evolving business requirement.
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