Mastercard
Vancouver, Canada
+ 3 other locations
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.
Data Scientist II
Role Overview
Build and productionize enterprise-grade AI and Generative AI solutions for Mastercard. This role combines strong software engineering with model fine-tuning, Databricks-based ML engineering, AWS deployment, and end-to-end MLOps.
Key Responsibilities
Design, develop, test, and maintain scalable AI/ML applications, APIs, and reusable engineering components.
Fine-tune and evaluate foundation models using techniques such as LoRA, QLoRA, PEFT, and supervised fine-tuning.
Build RAG solutions, embeddings workflows, vector-search applications, and AI agents.
Create end-to-end ML pipelines for data preparation, training, evaluation, deployment, monitoring, and retraining.
Use Databricks, PySpark, MLflow, Unity Catalog, Workflows, Vector Search, and Model Serving for governed model development and operations.
Implement CI/CD, automated testing, observability, model monitoring, and production support practices.
Partner with data science, engineering, product, security, privacy, and governance teams to deliver reliable and responsible AI solutions.
Required Skills & Experience
3–4 years of experience in AI/ML engineering, software engineering, data science, or a related field.
Strong Python, SQL, object-oriented programming, data structures, API development, testing, Git, and software design fundamentals.
Hands-on experience with Databricks, Spark/PySpark, MLflow, model registries, and production ML workflows.
Practical AWS experience, including secure cloud architecture, container deployment, monitoring, and access controls.
Experience with PyTorch, Hugging Face, scikit-learn, FastAPI/Flask, Docker, Kubernetes, and CI/CD.
Understanding of model performance, latency, scalability, cost optimization, data quality, and responsible AI controls.
Preferred
Experience with enterprise GenAI, financial services or payments data, LangChain/LangGraph, and production-grade RAG or agentic AI systems.
Education
Bachelor’s or Master’s degree in Computer Science, Information Technology,or a related STEM field.
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: