Senior Model Risk Manager - AI/ML

Sobre el puesto

<p>Mercury is building the financial stack - intuitive, powerful, and safe for entrepreneurs and businesses of all sizes. We have made a deliberate, company-wide bet on AI/ML. Across fraud detection, financial crime prevention, credit decisioning, and internal operations, machine learning and AI models are becoming core to how Mercury works, and that portfolio is growing fast.</p> <p>As AI transforms financial services, every institution is being forced to ask a hard question: what does model risk management (MRM) actually mean in this new era? ML has powered fraud detection and credit decisioning for years, but the scope and technology has changed dramatically. Generative models, autonomous agents, and real-time systems are creating risks that existing MRM frameworks were never designed to govern. No one has fully solved this yet. We want to hire the person who will. Ideal candidates may come from a traditional model validation background with deep hands-on experience testing modern AI/ML systems, or from model development, applied AI, or research as data scientists, with a strong understanding of how risks emerge in complex systems and how to rigorously challenge them as they scale into production.</p> <p><em>*Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column N.A., Members FDIC.</em></p> <p>As Senior Model Risk Manager - AI/ML, you will define what model governance looks like for AI/ML at Mercury. That means continuously building and enhancing the frameworks, not just inheriting them. You will own validation, monitoring, and governance of Mercury’s AI/ML model portfolio, but more than that, you will be a thought leader in an industry-wide conversation about how MRM must evolve in the context of AI. You will partner closely with data scientists, engineers, compliance leads, and product teams, and you will help shape not just Mercury’s approach, but set a standard for what rigorous, forward-looking MRM on AI can look like in fintech.&nbsp;</p> <h4>Here are some of the things you will do:</h4> <p>Model Governance &amp; Monitoring Oversight</p> <ul> <li>Maintain and enhance Mercury’s model governance framework, including inventory standards, documentation templates, validation standards, and issue management.</li> <li>Assess whether first-line monitoring efforts are effective, proportionate to model risk, and sufficient to keep models fit for purpose over time.</li> </ul> <p>Model Validation</p> <ul> <li>Perform independent validation across predictive ML models, generative AI systems, and agentic workflows, covering data, assumptions, methodology, testing, and monitoring.&nbsp;</li> <li>Assess risks in LLM-powered applications, including RAG pipelines, tool use, autonomy boundaries, human oversight, and hallucination risk.</li> <li>Identify and document model limitations, failure modes, and emerging AI risks including drift, instability, fairness, and robustness concerns</li> </ul> <p>MRM Advisory</p> <ul> <li>Serve as a trusted advisor to data scientists, engineers, product teams, and risk partners throughout the AI/ML lifecycle to provide practical guidance on model risk, governance expectations, and control design without slowing responsible innovation.</li> <li>Evaluate new AI use cases for regulatory implications, materiality, and governance requirements prior to deployment.</li> <li>Help shape Mercury’s responsible AI standards, including explainability, bias assessment, testing, human oversight, and documentation.</li> </ul> <p>AI Enablement for MRM</p> <ul> <li>Develop and maintain AI-enabled automation tools to improve the speed, scale, and effectiveness of model governance and validation workflows.</li> <li>Modernize the MRM function to operate effectively in a fast-moving AI environment while maintaining strong governance standards.</li> </ul> <p>Culture and Advocacy</p> <ul> <li>Champion MRM as a strategic enabler of safe and scalable AI/ML adoption, not simply a control function.</li> <li>Build model risk literacy across engineering, product, data science, compliance, and risk teams.</li> </ul> <p>There are many paths that could lead you here. We think the strongest candidates will bring some combination of the following:</p> <ul> <li>Bachelor's degree in a quantitative field (e.g. Computer Science, Engineering, Statistics, Mathematics, etc.) with 6-10 years of meaningful hands-on experience developing or validating AI/ML models and systems, ideally in financial services or fintech.</li> <li>Strong technical foundations in Python, SQL, and modern ML tooling (e.g. scikit-learn, XGBoost); familiarity with LLMs, RAG systems, prompt engineering, and AI agent frameworks.</li> <li>Experience in evaluating and testing machine learning models (e.g. in fraud detection) and generative AI systems, including custom evals, red-teaming, or frameworks.</li> <li>Solid understanding of model risk governance principles and regulatory expectations (e.g. SR 11-7 / OCC 2011-12, SR 26-2).</li> <li>Deep appreciation of disciplined model governance and independent effective challenge.</li> <li>A healthy dose of skepticism combined with a constructive, solution-oriented approach.</li> <li>Comfort operating in ambiguity: capable of synthesizing fragmented technical, operational, and business context into a clear understanding of how complex models and AI systems actually work, and making sound judgments even without a complete playbook or perfect documentation.</li> <li>High agency and adaptability: able to operate effectively in a fast-moving environment where priorities evolve quickly, new ad hoc problems emerge regularly, and role boundaries are intentionally broad. You can operate effectively without tightly-defined scope, find the highest-leverage work, and get it done.</li> <li>Exceptional attention to detail across documentation, code base, testing artifacts and quantitative analysis.</li> <li>Strong written and verbal communication skills; you can explain model risk to a data scientist and to a regulator, and use different language for each.</li> </ul> <p>The total rewards package at Mercury includes base salary, equity, and benefits.</p> <p>Our salary and equity ranges are highly competitive within the SaaS and fintech industry and are updated regularly using the most reliable compensation survey data for our industry. New hire offers are made based on a candidate’s experience, expertise, geographic location, and internal pay equity relative to peers.</p> <p>Our target new hire base salary ranges for this role are the following:</p> <ul> <li>US employees (any location): $200,700 - $250,900</li> <li>Canadian employees (any location): CAD $189,700 - $237,100</li> </ul> <p>Mercury values diversity &amp; belonging and is proud to be an Equal Employment Opportunity employer. All individuals seeking employment at Mercury are considered without regard to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, sexual orientation, or any other legally protected characteristic. We are committed to providing reasonable accommodations throughout the recruitment process for applicants with disabilities or special needs. If you need assistance, or an accommodation, please let your recruiter know once you are contacted about a role.</p> <p>#LI-MZ1</p> <p>&nbsp;</p>