Description
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 seeking a Senior AI Engineer 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.
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.
Requirements
- 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.
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 analytical problem-solving skills.
- Ability to influence stakeholders through technical expertise and data-driven recommendations.
- Excellent communication and collaboration skills.
- Ability to translate complex technical concepts into actionable business insights.
Minimum Qualifications
- Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field.
- 5+ years of experience in machine learning, data science, AI, or advanced analytics.
- Experience developing and deploying machine learning models in production environments.
- Demonstrated experience applying statistical and machine learning techniques to real-world business problems.
Preferred Qualifications
- Master's degree or PhD in Machine Learning, Artificial Intelligence, Computer Science, Statistics, Mathematics, or a related field.
- Experience with foundation models, embeddings, or representation learning.
- Experience in financial services, payments, banking, fintech, fraud, marketing analytics, or customer intelligence.
- Publications, patents, conference presentations, or other evidence of technical thought leadership.