Senior Data Scientist, AI Engineering

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About the role

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 Senior 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 Senior Data Scientist, AI Engineering to develop advanced machine learning solutions across domains. This role combines deep expertise in predictive modelling, experimentation, and applied machine learning to deliver measurable business impact. The successful candidate will partner closely with engineering, product, and business teams to bring innovative AI solutions from concept to production. What You'll Work On 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 role rather than a conversational AI, RAG, or agentic systems engineering position. Role / Key Responsibilities Design, develop, and deploy machine learning solutions that solve high-impact business problems. Define modelling approaches, experimentation methodologies, 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 development, deployment, and impact measurement. Develop robust evaluation frameworks and benchmark new approaches against existing solutions. Partner with business, product, engineering, and analytics teams to identify and prioritise opportunities. Communicate technical findings and recommendations to both technical and non-technical stakeholders. Contribute to the development of best practices, reusable assets, and modelling standards across the AI organisation. Support and mentor junior data scientists through technical guidance and knowledge sharing. All About You Required Experience Proven experience developing and deploying machine learning solutions in production environments. 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 working in cross-functional teams to bring data science solutions from concept to deployment. 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, repr

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