Lead Data Scientist, AI Engineering

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 Lead Data Scientist, AI Engineering Lead Data Scientist, AI Engineering Overview Mastercard's AI Centre of Excellence is building the next generation of AI capabilities powered by large-scale transaction data, machine learning, and foundation models. We are transforming how AI solutions are developed by enabling teams to leverage reusable learned intelligence rather than building bespoke feature-engineering pipelines for every use case. We are seeking a Lead Data Scientist, AI Engineering to lead the development of advanced machine learning solutions across domains. This role combines deep expertise in predictive modelling, experimentation, and technical leadership to deliver measurable business impact.

This role focuses on applying machine learning, predictive modelling, and foundation-model representations to solve business problems at scale. Typical use cases include forecasting, propensity modelling, recommendation systems, behavioural analytics, and customer intelligence. While familiarity with Generative AI is beneficial, this is primarily an applied machine learning and data science leadership role rather than a conversational AI, RAG, or agentic systems engineering position. Role / Key Responsibilities Lead the design, development, and deployment of machine learning solutions that solve high-impact business problems. Define modelling approaches, experimentation frameworks, and success metrics for AI initiatives. Apply foundation-model embeddings and modern machine learning techniques to improve model performance and accelerate development. Drive projects from problem definition through model deployment and business impact measurement. Establish robust evaluation frameworks and benchmark new approaches against existing solutions. Partner with business, product, engineering, and analytics teams to identify and prioritise opportunities. Present technical findings and recommendations to stakeholders and senior leadership. Mentor and develop data scientists and AI engineers through technical guidance, reviews, and coaching. Contribute to hiring, capability development, and the long-term technical direction of the AI organisation. All About You Required Experience Proven experience leading machine learning projects from concept through production deployment. Experience solving predictive modelling problems such as attrition, forecasting, recommendation systems, propensity modelling, fraud detection, risk modelling, or customer analytics. Strong track record of delivering measurable business outcomes through machine learning. Experience leading technical teams, mentoring practitioners, and influencing technical direction. Required Technical Skills Strong expertise in machine learning, predictive analytics, statistical modelling, and experimentation. Advanced Python and SQL skills. Experience with machine learning frameworks such as Scikit-Learn, XGBoost, LightGBM, TensorFlow, or PyTorch. Strong understanding of classification, regression, forecasting, recommendation systems, ranking, clustering, and anomaly detection. Experience with feature engineering, representation learning, embeddings, and downstream machine learning workflows. Familiarity with transformer-based models and foundation-model applications. Experience working with Databricks, Spark, Azure, AWS, or GCP. Leadership & Communication Strong problem-solving and decision-making skills. Ability to lead through influence