ML/AI Engineer II-2

Job Description Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we're helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title And Summary ML/AI Engineer II-2 Overview Mastercard's Business & Market Insights (B&MI) group empowers organizations to achieve growth & innovation goals by providing unparalleled data-driven insights and advanced analytics. By leveraging proprietary data and global expertise, B&MI helps businesses make smarter, more informed decisions that drive profitability and success. We turn complex data into actionable strategies that lead to better outcomes and sustained competitive advantage. We are currently looking for a Senior Engineer, Machine Learning Engineering for Operational Intelligence Program, within B&MI group. This role would entail development and delivery of secure, scalable, and high-performing AI/ML solutions. As a senior technologist, this role will also focus on engineering best practices, next gen innovation and stakeholder management, while fostering a culture of continuous learning and technical excellence within the team. Roles And Responsibilities - Lead the design and development of AI and analytics solutions spanning classical machine learning, time-series forecasting, statistical modeling, deep learning, and emerging agent-based architectures. - Develop predictive and prescriptive models using supervised, unsupervised, and probabilistic approaches including regression, tree-based models, clustering, anomaly detection, Bayesian inference, and ensemble methods. - Build and optimize time-series forecasting frameworks leveraging ARIMA/SARIMA, ETS, Prophet, VAR models, state-space models, LSTM/GRU-based deep forecasting, and ML-based hybrid forecasting pipelines for financial and operational use cases. - Integrate Generative AI and multi-agent systems (LangGraph, CrewAI, AutoGen) with traditional ML and statistical methods to enable reasoning-driven automation, intelligent decision support, and domain-aware task execution. - Perform exploratory data analysis, feature engineering, and hypothesis-driven insights using statistical testing, experimental design, root-cause analysis, and uncertainty quantification to guide business-critical decisions. - Create reusable model components, frameworks, and evaluation workflows including model selection, hyperparameter tuning, cross-validation, drift detection, and benchmarking across classical ML and GenAI capabilities. - Ensure model governance, explainability, and responsible AI practices, using interpretability frameworks (SHAP, counterfactuals, partial dependence) along with fairness, transparency, and compliance standards. - Collaborate closely with business stakeholders, product teams, and data engineering partners to translate domain challenges into measurable analytical solutions with quantifiable benefits and ROI. - Monitor performance and continuously improve production models, leveraging statistical diagnostics, error decomposition, A/B experimentation, and closed-loop learning strategies. - Stay current with advances in machine learning, statistical modeling, deep learning, and agentic AI, evaluating emerging methods and incorporating them into future platform and capability roadmaps. All About You - Master's/bachelor's degree in computer science or engineering, and a considerable work experience with a proven track-record of successfully building complex projects/products and delivering to aggressive market needs. - Advanced-level hands on experience designing, building and deploying both conventional AI/ML solutions and LLM/Agentic solutions. - Strong analytical and problem-solving abilities, with quick adaptation to new technologies, methodologies, and systems. - Strong applied knowledge and hands on experience in advanced statistical techniques, predictive modelling, machine learning algorithms, GenAI and deep learning frameworks.. Corporate Security Responsibility All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must: - Abide by Mastercard's security policies and practices; - Ensure the confidentiality and integrity of the information being accessed; - Report any suspected information security violation or breach, and - Complete all periodic mandatory security trainings in accordance with Mastercard's guidelines.