Senior Analyst, Model Risk Management at Toast

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

19 May, 26

Salary

0.0

Posted On

18 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Model Risk Management, Model Validation, Fraud Detection Models, Generative AI, LLM, Python, SQL, Machine Learning, Statistical Foundations, Risk Tiering, Model Inventory, SR 11-7, NIST AI RMF, Data Integrity, Explainability, Bias/Fairness Risk

Industry

Software Development

Description

Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy. Toast is driven by building the restaurant platform that helps restaurants adapt, take control, and get back to what they do best: building the businesses they love. Now, more than ever, the Toast team is committed to our customers. We’re taking steps to help restaurants navigate these unprecedented times with technology, resources, and community. Our focus is on building the restaurant platform that helps restaurants adapt, take control, and get back to what they do best: building the businesses they love. And because our technology is purpose-built for restaurants, by restaurant people, restaurants can trust thatwe’ll deliver on their needs for today while investing in experiences that will power their restaurant of the future. Bready* to make a change? As SeniorAnalyst in our Model Risk Management team, you will help manage the Model Risk Program including completing model validation reviews, maintaining the model risk inventory, and monitoring model performance particularly for Fraud and Gen AI Models. You will work closely with the Data Science Team, architects, engineers and product managers to assess the risk of model design, implementation, and use across product lines. A day in the life (Responsibilities) Support the implementation and day-to-day execution of the Second Line of Defense (2LOD) Model Risk Management (MRM) program for high-risk models, with particular focus on Fraud detection models (Transaction Fraud & Merchant Fraud) and Generative AI / LLM-based systems deployed across Toast. Assist in maintaining and enhancing the Model Risk Management framework, including policies, procedures, validation standards, governance documentation, templates, and best practices aligned with evolving regulatory and industry expectations. Enforce model lifecycle standards across development, implementation, use, monitoring, recalibration, change management, governance, and decommissioning, ensuring appropriate controls for traditional ML models as well as GenAI systems (e.g., RAG architectures, copilots, AI-assisted decision tools). Contribute to the development, risk-tiering, and ongoing maintenance of a comprehensive model inventory, including assessment of model impact, intrinsic risk (complexity and methodology), reliance on model outputs, and emerging AI-specific risks. Perform independent model validation reviews under the guidance of senior leadership, covering conceptual soundness, data integrity, model methodology, performance metrics (e.g., AUC, precision/recall, calibration), stability, bias/fairness risk, explainability, and monitoring frameworks. Produce validation reports and track issue remediation plans through closure. For Fraud models, evaluate class imbalance handling, threshold optimization, cost-sensitive performance metrics, operational overlays, rule-based controls, and portfolio-level impact analyses. For Generative AI systems, validate systems and evaluate risks related to hallucination, prompt injection, adversarial vulnerabilities, data privacy and leakage, model explainability limitations, bias, guardrails, output monitoring, jailbreak testing, regression testing, and human-in-the-loop controls. Partner with Data Science, Data Engineering, Product/Engineering, Information Security, Legal/Compliance, Finance, Credit Risk and Business teams to obtain documentation, perform effective challenge, conduct validation and oversee performance monitoring Prepare reports and executive materials summarizing model risk issues, validation findings, monitoring insights, and remediation status for leadership review, risk committees, audit committees, and internal audit engagements. Research and stay informed on industry developments in fraud analytics, machine learning, Generative AI governance, and regulatory guidance (e.g., SR 11-7, OCC 2011-12, NIST AI RMF). Propose enhancements to strengthen Toast’s Model Risk and AI Governance framework. Contribute to the development of model risk training materials and support delivery of training sessions to key stakeholders to enhance awareness of fraud model risk and AI-related risks. Support ad-hoc initiatives related to model risk governance, AI oversight, regulatory compliance, and enterprise risk management enhancements. What you'll need to thrive (Requirements) Advanced degree in Data Science, Statistics, Applied Mathematics, Computer Science, Engineering, or a related quantitative discipline 5+ years of relevant industry experience in Model Risk Management, Model Validation, Data Science, or Machine Learning within fintech, banking, payments, technology, or consulting environments, with exposure to fraud analytics, credit risk, or AI/ML model governance. Strong understanding of machine learning methodologies and statistical foundations, including model development, validation techniques, performance evaluation, calibration, and stability analysis. Hands-on experience with fraud detection models (e.g., transaction monitoring, chargeback prediction, merchant risk scoring) and familiarity with concepts such as class imbalance handling, threshold optimization, precision/recall trade-offs, and cost-sensitive evaluation. Familiarity with Generative AI and LLM-based systems (e.g., RAG architectures, embeddings, prompt engineering, AI copilots) and awareness of associated risks such as hallucination, bias, prompt injection, and data leakage. Strong proficiency in Python and SQL; experience with data science and ML tools such as Spark, scikit-learn, XGBoost, LightGBM, TensorFlow, or PyTorch. Familiarity with software engineering best practices (version control, CI/CD, testing frameworks, Airflow, AWS or cloud platforms) is a plus. Knowledge of model risk management frameworks and regulatory guidance (e.g., SR 11-7, OCC 2011-12) and familiarity with emerging AI governance frameworks (e.g., NIST AI RMF) is highly desirable. Ability to translate complex quantitative methodologies into clear business insights and effectively challenge first-line model development teams while maintaining strong cross-functional partnerships. Demonstrated critical thinking, analytical rigor, and structured problem-solving skills, with the ability to manage multiple priorities and deliver high-quality outputs under tight deadlines. Excellent written and verbal communication skills, including experience drafting formal validation reports, executive summaries, and committee-level materials. AI at Toast At Toast, one of our company values is that we're hungry to build and learn. We believe learning new AI tools empowers us to build for our customers faster, more independently, and with higher quality. We provide these tools across all disciplines, from Engineering and Product to Sales and Support, and are inspired by how our Toasters are already driving real value with them. The people who thrive here are those who embrace changes that let us build more for our customers; it’s a core part of our culture. Our Total Rewards Philosophy We strive to provide competitive compensation and benefits programs that help to attract, retain, and motivate the best and brightest people in our industry. Our total rewards package goes beyond great earnings potential and provides the means to a healthy lifestyle with the flexibility to meet Toasters’ changing needs. Learn more about our benefits at https://careers.toasttab.com/toast-benefits. How Toast Uses AI in its Hiring Process Throughout the hiring process, our goal is to get to know you. We use AI tools to support our recruiters and interviewers with tasks like note-taking, summarization, and documentation of interviews to ensure they can be fully focused on your conversation. All hiring decisions are made by people. Diversity, Equity, and Inclusion is Baked into our Recipe for Success At Toast, our employees are our secret ingredient—when they thrive, we thrive. The restaurant industry is one of the most diverse, and we embrace that diversity with authenticity, inclusivity, respect, and humility. By embedding these principles into our culture and design, we create equitable opportunities for all and raise the bar in delivering exceptional experiences. We Thrive Together We embrace a hybrid work model that fosters in-person collaboration while valuing individual needs. Our goal is to build a strong culture of connection as we work together to empower the restaurant community. To learn more about how we work globally and regionally, check out: https://careers.toasttab.com/locations-toast. Apply today! Toast is committed to creating an accessible and inclusive hiring process. As part of this commitment, we strive to provide reasonable accommodations for persons with disabilities to enable them to access the hiring process. If you need an accommodation to access the job application or interview process, please contact candidateaccommodations@toasttab.com. ------ For roles in the United States, it is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

Responsibilities

The role involves supporting the execution of the Second Line of Defense Model Risk Management program, focusing on high-risk models like Fraud detection and Generative AI systems. Responsibilities include performing independent model validation reviews, maintaining the model risk inventory, and enforcing model lifecycle standards.