Lead data science initiatives for consumer credit risk models, deploying and optimizing machine learning pipelines to support strategic decisions at Capital One.
Compensation
McLean, VA: $135,600 - $154,800 for Sr Assoc, Data Science
Responsibilities
Partner with cross-functional teams to deliver data science products
Leverage Python, AWS, Spark to analyze large datasets
Optimize and deploy machine learning models through all development phases
Translate complex data insights into business goals
Requirements
Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or related)
Master's Degree in a quantitative field or MBA with quantitative concentration
Director-level Credit Policy Officer responsible for reviewing and validating credit rating models and analytical tools, maintaining validation processes, and ensuring compliance with regulatory expectations.
Locations (2)
Dublin, Ireland, Ireland
London, England, United Kingdom
Responsibilities
Review proposed and existing rating methodologies, evaluate analytical assumptions, quantitative approaches, model dependencies and consistency with analytical standards and regulatory expectations.
Maintain KBRA's validation processes and standards for models and tools used by rating teams, including those developed in Excel, Python and other programming languages and analytical platforms.
Conduct independent reviews and validations of credit rating models and tools, assessing conceptual soundness, underlying credit assumptions, design and functionality, calculation methodologies, data integrity, sensitivity to key risk drivers, limitations and the reasonableness of analytical outputs; work with analytical teams to address identified weaknesses.
Executive Director, AI for Clinical Intelligence and Evidence
Manage counterparty credit risk arising from the Group’s derivatives portfolio and security financing portfolio, providing analytical support and developing risk models.
Compensation
Competitive base salary. A suite of holistic, flexible benefits to suit every lifestyle.
Responsibilities
Manage counterparty credit risk (CCR) arising from the Group’s derivatives portfolio, security financing portfolio and provide analytical support to credit approvers and business on transaction flows, and portfolio analysis.
Support development and maintenance of methodology, models and framework applicable to counterparty pre-settlement risk, settlement risk, concentration risk within derivative portfolio and collateral pool.
Monitor the Bank’s counterparty credit risk from Global Market portfolios, perform risk analysis at both counterparty level and portfolio level to capture and highlight concentration risk, correlation risk and other non-conventional risks which are not captured in financial models.
Participate in the implementation and enhancement of CCR risk models within exposure management systems and oversee the end-to-end flow CCR exposure flow to ensure risk exposures are accurately captured and reported, collaborate with other control functions for model validation for compliance with internal and regulatory governance standard.
Conduct system testing and exposure validation exercises, including investigation of data discrepancies. Prepare and maintain relevant model, methodology and procedure documentation.
Senior Data Scientist (Predictive Maintenance system)
Support the design and implementation of an AI-driven Predictive Maintenance solution for industrial equipment.
Responsibilities
Analyze historical equipment, maintenance, operational, sensor and telemetry data to identify patterns associated with equipment degradation and failures.
Work with domain experts to understand failure modes, their potential indicators, and whether available data provides sufficient predictive signals.
Perform exploratory data analysis, data profiling, statistical analysis, correlation analysis and feature discovery.
Assess data quality, completeness, frequency and predictive value before model development.
Identify and engineer relevant features from time-series and operational data.
Support the design and implementation of an AI-driven Predictive Maintenance solution for industrial equipment.
Responsibilities
Analyze historical equipment, maintenance, operational, sensor and telemetry data to identify patterns associated with equipment degradation and failures.
Work with domain experts to understand failure modes, their potential indicators, and whether available data provides sufficient predictive signals.
Perform exploratory data analysis, data profiling, statistical analysis, correlation analysis and feature discovery.
Assess data quality, completeness, frequency and predictive value before model development.
Identify and engineer relevant features from time-series and operational data.
Develop and build full vehicle level structural finite element models to simulate and optimise NVH performance parameters.
Responsibilities
Develop and build full vehicle level structural finite element models
Collaborate with cross-functional teams to collect essential structural system inputs
Construct detailed system-level FE models for various design verification plans
Establish comprehensive meshing guidelines tailored for full vehicle and sub-assembly applications
Coordinate with the design engineering team to evaluate proposals and implement modifications
Requirements
Bachelor of Engineering (BE) or Bachelor of Technology (B.Tech) degree in Mechanical Engineering, Automotive Engineering, or a closely related technical discipline
Nice to have: Experience within the automotive manufacturing industry
Nice to have: vehicle body and chassis development
Nice to have: commercial vehicle or passenger vehicle engineering domains
Lead applied trajectory prediction and aircraft-response modeling for a safety-critical Run-Time Assurance product.
Compensation
Pay within range listed + Bonus + Benefits + Equity
Responsibilities
Lead development, tuning, and validation of TPA and recovery-behavior models using aircraft performance data, command-response assumptions, 3DOF/6DOF flyout concepts, wind effects, uncertainty bounds, and maneuver constraints.
Build Python-based analysis, simulation, HIL, and flight-test workflows to compare predicted versus observed aircraft behavior, identify model gaps, tune parameters, and maintain regression datasets.
Integrate and evaluate trajectory-prediction behavior through off-the-shelf or custom autopilot interfaces, including command modes, vehicle-state inputs, latency, mode transitions, control limits, and telemetry analysis.
Support hardware integration and flight-test campaigns across relevant platforms; help evolve trajectory-prediction and safety behaviors from single-aircraft use cases toward multi-agent collaborative CONOPS.
Produce algorithm handoff artifacts and contribute scoped C/C++ implementation, testing, and debugging as needed.
Maintain appropriate documentation of model and tool reviews, including results of independent validation of calculations, limitations and recommended enhancements, consistent with KBRA's established policies.
Monitor credit rating performance, including rating transitions and defaults, across asset classes and rating methodologies in Europe and globally.
Requirements
Bachelor's degree in finance, economics, mathematics, statistics, computer science or another relevant discipline. An advanced degree or relevant professional qualification is a plus.
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