The posting
A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You'll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you'll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You'll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.
IBM Promontory is seeking a Quantitative Senior Principal with deep expertise in quantitative modeling, model risk management, financial services risk and regulatory compliance, and artificial intelligence (AI).
The successful candidate will play a key leadership role in delivering complex quantitative risk and model risk management (MRM) engagements, including model development, independent validation, and risk governance advisory services. The role also offers significant opportunities to advance AI capabilities through the development of innovative, reusable AI-enabled tools and analytical solutions for risk and compliance functions.
The ideal candidate combines strong quantitative and regulatory expertise with practical AI knowledge, hands-on technical capabilities, and proven consulting leadership. This individual should be comfortable leading complex client engagements, providing independent technical challenge, advising senior stakeholders, and contributing directly to the design and development of AI-enabled solutions.
Key Responsibilities
1. Quantitative Modeling & Model Risk Management
- Lead complex model risk management engagements, including independent model validation, quantitative model development, model risk assessments, and governance framework enhancements, while maintaining appropriate independence between model development and validation activities.
- Provide technical leadership across statistical, econometric, financial risk, machine learning, and AI-based models.
- Oversee comprehensive model validation activities, including conceptual soundness assessments, data quality reviews, methodology evaluations, implementation testing, sensitivity analysis, benchmarking, outcomes analysis, and ongoing performance monitoring, as appropriate to the model and its risk profile.
- Lead quantitative modeling and validation engagements across financial risk and compliance domains, such as credit risk, market risk, capital and stress testing, operational risk, financial crime, fraud, and fair lending, based on relevant expertise and client needs.
- Advise financial institutions on model risk governance, model inventories, risk classification and tiering, validation methodologies, model lifecycle management, and model risk remediation.
- Evaluate complex internally developed and third-party models, identify model limitations and weaknesses, and provide practical recommendations supported by sound quantitative analysis.
- Serve as a senior technical reviewer and subject matter expert, ensuring high-quality model validation reports, quantitative assessments, and regulatory-facing deliverables.
2. Artificial Intelligence & AI Asset Development
- Provide technical expertise in AI/ML methodologies and applications, including traditional machine learning, Generative AI, large language models (LLMs), and emerging agentic AI systems.
- Lead or support the design, development, evaluation, and validation of AI-enabled solutions for financial services risk and compliance use cases.
- Identify opportunities to develop and enhance reusable AI assets, analytical accelerators, and automation tools that improve the efficiency, accuracy, and scalability of risk advisory and model validation services.
- Contribute hands-on to AI solution development, including technical design, prototyping, Python-based implementation, model integration, testing, and evaluation, working closely with AI engineers and data scientists as appropriate.
- Apply AI technologies to use cases such as model validation automation, risk assessment, financial crime analytics, regulatory compliance reviews, and data-driven risk monitoring.
- Assess AI-specific risks and controls, including model explainability, bias and fairness, robustness, hallucinations, data privacy, security, human oversight, and monitoring.
- Collaborate across IBM Promontory and IBM Consulting to translate AI concepts and prototypes into practical, scalable, and reusable client solutions.
3. Financial Services Risk, Compliance & AI Governance
- Advise financial institutions on quantitative risk management, regulatory compliance, and governance expectations applicable to models and AI-enabled systems.
- Apply knowledge of relevant banking regulations and supervisory guidance, including the revised U.S. interagency model risk management guidance (SR 26-2), where applicable.
- Support the development, assessment, and enhancement of model risk management and AI governance frameworks, policies, standards, procedures, and controls.
- Evaluate the risks associated with AI/ML and emerging AI technologies, including Generative AI and agentic AI, and recommend governance and control approaches appropriate to their characteristics, use cases, and applicable regulatory environments.
- Integrate quantitative and AI risk considerations into broader enterprise risk management, operational risk, compliance, and internal control frameworks.
4. Client Engagement & Practice Leadership
- Lead multidisciplinary client engagements, providing oversight of project execution, technical quality, deliverables, and stakeholder communication.
- Serve as a trusted advisor to senior client stakeholders, including model risk management executives, risk officers, compliance leaders, and technology teams.
- Contribute to business development, including identifying client needs, shaping solutions, developing proposals, and supporting client presentations.
- Collaborate with risk, regulatory, technology, and AI specialists to develop integrated solutions that address complex financial services challenges.
- Mentor and develop quantitative professionals, support knowledge sharing, and contribute to practice methodologies, thought leadership, and intellectual property development.
The ideal candidate is a senior quantitative risk professional with deep model risk management expertise and a strong understanding of financial services regulation. They bring the technical credibility to lead challenging model development and validation engagements, the AI expertise to evaluate emerging technologies, and the practical skills to help develop innovative AI-enabled assets. Equally important, they demonstrate the consulting leadership required to deliver complex client engagements, build relationships, and contribute to the continued growth of IBM Promontory's quantitative risk and AI capabilities.
This job can be performed from anywhere in the US.
- Bachelor's degree or higher in a quantitative discipline, such as Statistics, Mathematics, Quantitative Finance, Economics, Econometrics, Computer Science, Engineering, or a related field.
- Significant professional experience, typically 10+ years, in quantitative risk management, model development, model validation, financial services risk consulting, or related fields.
- Demonstrated experience leading complex quantitative modeling, model validation, or model risk management engagements, with the technical depth to provide effective challenge and oversee high-quality delivery.
- Strong knowledge of statistical modeling, econometrics, quantitative risk methodologies, machine learning, model development, and independent model validation techniques.
- Strong programming and quantitative analytical skills, with experience using Python, R, SAS, or comparable tools; proficiency in Python is strongly preferred.
- Solid understanding of financial services risk management, regulatory compliance, and model governance frameworks, including familiarity with relevant U.S. banking supervisory expectations.
- Demonstrated expertise in AI/ML methodologies, model evaluation, and AI-related risks, with the ability to assess technical approaches, explain limitations, and provide practical guidance on AI applications in risk and compliance.
- Familiarity with Generative AI, LLM-based solutions, and their associated evaluation and governance considerations.
- Strong consulting and leadership capabilities, including client relationship management, project delivery, team development, and communication with senior stakeholders.
- Advanced degree (Master's or Ph.D.) in a quantitative discipline or relevant professional certifications such as FRM or CFA.
- Hands-on experience designing, prototyping, coding, testing, or deploying AI-enabled applications, reusable analytical assets, or automation solutions.
- Practical experience with modern AI technologies and frameworks, including LLM APIs, retrieval-augmented generation (RAG), AI agents, workflow orchestration, and AI evaluation techniques.
- Experience developing AI or advanced analytics solutions for financial services risk and compliance applications, such as model risk management, financial crime, fraud detection, or risk and control assessments.
- Familiarity with AI governance and Responsible AI frameworks, such as the NIST AI Risk Management Framework, and relevant AI regulatory requirements.
- Experience with cloud-based AI platforms, model development and deployment workflows, or IBM technologies such as watsonx and watsonx.governance.
- Experience developing reusable consulting assets, technical methodologies, or innovative risk and compliance service offerings.
