Top AI & Machine Learning Jobs in Toronto, ON

Lead Data Scientist analyzing global fraud data to identify patterns, develop predictive models, and prevent payment fraud. The role builds fraud-related products and services, extracts insights from large datasets, collaborates with stakeholders, and presents findings to management and clients. Required skills include Python, Spark, complex data queries, and advanced data analysis. Experience with agentic AI systems and Unix is preferred, along with payments or fraud expertise.

Supports sales, customer success, and product development for Mastercard Identity through data analysis, machine learning, rules-based fraud models, and proof-of-value testing. Advises customers on fraud workflows, analyzes data features, supports API integration QA, and provides product recommendations. Builds customer relationships, translates business needs into technical solutions, contributes to product specifications and training, and serves as a subject matter expert on identity data and products.

Lead the development of AI and machine learning solutions at Mastercard, defining strategy and mentoring teams while ensuring compliance and enhancing consumer experiences.

Lead a data science team to design and deliver merchant registry and profiling ML capabilities for onboarding and monitoring. Define strategy, guide model development and validation, promote reusable data assets, partner with Product and Engineering, drive Agile delivery, hire and mentor team members, and communicate impact to senior leadership to scale production ML systems on modern cloud/data platforms.

Lead design and delivery of enterprise-scale AI/ML solutions for Ethoca, building and governing models (NLP, generative, multi-modal) to reduce chargebacks, improve dispute resolution, and scale production systems across cloud environments.

Lead development, deployment, and monitoring of end-to-end ML/DL/AI solutions. Responsibilities include data extraction, cleaning, modeling, evaluation, production deployment, documentation, and presenting results. Collaborate across teams, build prototypes and POCs, protect IP, and disseminate knowledge externally.