3 of 3 Permanent Model Validation Jobs in Berkshire

Responsibilities: Engage in the validation and approval sign off of the firm's models across Liquidity Risk, Market Risk, and Counterparty Risk models. Challenge model assumptions, implementations, and mathematical formulations. Review and oversee the monitoring of the performance of models including outcomes, verification, and benchmarking. Understand and communicate the risks of model limitations to senior management. Requirements … Education: PhD/Masters in a finance/mathematical/quantitative field Prior Experience: 3-5 years in model validation of liquidity/market/counterparty risk models. Knowledge: Strong understanding and experience working with ILST/VaR models Technical: Python More ❯

and Analytics teams and collaborate with product and engineering teams to work on Media Measurement and Data Solutions products. This includes data analysis, design of statistical and machine learning model methodologies and codebases, and model validation. The ideal candidate is a self-starter comfortable working with large datasets and knowledgeable in statistical and machine learning techniques, with an … eagerness to learn and contribute to the development and validation of products that align Cint capabilities with the market. What you will do Contribute to discovery and development phases for new and existing products/models relating to media measurement Participate in model development, validation and maintenance Analyze large datasets to identify trends, patterns, and insights, ensuring … ad hoc client-specific requests including performing analyses, data manipulation and producing summary results. Collaborate with cross-functional teams to deliver on broader project goals. Participate in developing methodologies, model validation, and maintenance and enhancement of existing statistical and machine learning models. Support evaluation and validation of both internal and external products to ensure Cint’s success. More ❯

pipelines, and ethical product delivery. Key Responsibilities Collaborate with product teams to identify opportunities for ML and data science integration Build and maintain data pipelines to support experimentation and model development Develop and deploy machine learning models in production environments Present insights and results to technical and non-technical stakeholders Apply statistical methods and model validation techniques … user-centric data products Stay curious, research new methods, and continually build your technical capability What We’re Looking For Proficiency in Python or similar languages for scripting and model building Hands-on experience with machine learning, model deployment, and performance evaluation Strong communication skills – able to simplify complex ideas for various audiences Awareness of Agile methodologies and More ❯