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Specifically, the candidate will provide guidance on model validation across a wide range of asset classes and will support the design and implementation of a variety of risk management functions including stress testing and Value-at-Risk. The candidate will support the modeling and risk product strategy, business analysis, development lifecycle, and specifications.
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Work with RT Controls Simulation engineer to develop models and complete HIL testing, model based development. Deliver robust documentation, programming and p testing of Real Time Controls including HIL integrated testing.
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This may include mortgage products and securities, borrower behavior, investment and hedging strategies, residential property valuation, macroeconomic models, including housing prices and interest rate, financial valuation of finance assets and derivatives, economic capital, and stress testing.
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Collaborate with the Financial Planning & Analysis team on the execution of stress testing, reverse stress tests, credit risk charges, and other initiatives. Assess the impact of agriculture, agribusiness, and rural infrastructure portfolio trends on model performance and develop timely approaches to address changes, specifically within Farmer Mac’s loss allowance model (strong accounting, modeling, and Current Expected Credit Losses (CECL) experience required.
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Oversee design and build out of appropriate data governance to ensure that data is complete, consistent, correct, accurate, and timely for internal testing and future submission to the US Federal Reserve Bank; oversee determination, design, and implementation of CCAR production infrastructure, data, and technology needs (e.g. modeling data, model production environment, regulatory reporting.
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This specialized nurse will primarily support Cardiology and Interventional Radiology, but also many clinical departments for conscious sedation procedures for patients with the ASA1 and ASA2 medical determiniations throughout the hospital (Peri-OP, Nursing, and Endoscopy) and cardiac monitoring, when required (Lexisan procedures, CT and MRI testing.
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Coordinate with Internal Audit and relevant Control teams (e.g., Model Validation/ Risk Management) to ensure effective controls are in place across the CCAR lifecycle and there is clear alignment of expectations related to CCAR problems; offer solutions and manage senior stakeholder expectations.
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Data Gathering, Preparation, and Governance : Oversee design and build out of appropriate data governance to ensure that data is complete, consistent, correct, accurate, and timely for internal testing and future submission to the US Federal Reserve Bank; oversee determination, design, and implementation of CCAR production infrastructure, data, and technology needs (e.g. modeling data, model production environment, regulatory reporting.
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Work individually or as part of a team to execute model validation activities and testing against BSA/AML and OFAC models, including assessing risks related to model design, data, output, and governance.
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Provide care coordination for patients with HCM undergoing a multitude of comprehensive diagnostic and therapeutic interventions, including alcohol septal ablation, surgical septal myectomy, genetic testing and advanced cardiac imaging.
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Is responsible for performing interest rate risk simulations, capital and liquidity stress testing and most likely income projections utilizing the Asset Liability Management Model among other models.
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Software testing and integration What you'll need Bachelor's degree in related field or directly related experience Experience using RHEL, Ansible, Cisco Cisco or Linux certification Security + certification Experience operating and solving issues with SATCOM networks and terminals Experience with Linux based servers Ability to work with IP/Network problems at all layers of OSI model Ability to pass skills testing for grip and static strength, dynamic lifting, and work/positional tolerances.
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Our team is comprised of former product managers, economists and engineers from Google, Netflix, Amazon and Meta who saw how costly it is to support high-quality decision science tooling and incrementality testing.
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Experience with hands on testing and model validation. Drive and participate in a culture of rapid protoyping and testing. Utilize advanced computational tools and techniques, such as finite element analysis (FEA) and computational fluid dynamics (CFD.
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Build and help set direction across the entire machine learning development process to implement machine learning algorithms in production, including exploratory data analysis, data modeling, feature engineering, model training and tuning, testing, deployment, and monitoring.
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