Lead Data Scientist, Fraud Data Science & Innovation
Job Description
Description
Job Description
What’s the opportunity?
You will lead Fraud Management(FM) Machine Learning(ML) and Artificial Intelligence(AI) advancements, develop strategy for predictive fraud detection ML models and explore ML/AI technique application. You will set standards for ML model development, ensuring consistency, accuracy and repeatability, collaborate with partners and work with Fraud Strategy, Fraud IT and Enterprise Model Risk Management(EMRM) on emerging fraud risks, solution design and model validation frameworks. You will fully comprehend the technical architecture supporting the Fraud decisioning ecosystem and how it supports DSA detection requirements. As a Subject Matter Expert (SME) on FM initiatives, you will collaborate with stakeholders at varying levels of seniority.
What will you do?
- Lead the development and implementation of the Data Science Machine Learning (ML) strategy and act as key point of contact for all ML models
- Develop supervised fraud detection models and explore opportunities for unsupervised/anomaly detection applications
- Plan timelines, resource allocation, standards and best practices for ML model development with DS&I and be responsible for technical validation exercises for new model deployments
- Collaborate effectively with partners in Fraud IT on continuous improvement of the fraud detection ecosystem, understanding the technical requirements and their impact on the business users in DSA
- Work with EMRM to enable efficient model validation and develop a strong relationship with Fraud Strategy partners focused on identifying emerging fraud risks and how the application of ML can minimize these risks
- Identify opportunities and develop solutions to automate/enhance processes through analytical tools and workflows; and utilize technology tools to build the most effective solution; Python, R, Spark, PySpark, etc.
- Provide thought leadership to support Fraud Management’s key priorities where there is a dependence on data analytics, machine learning or data engineering
- Leverage expertise with ML and programming to provide support to the rest of the DSA team as required
What you need to succeed
Must have:
- 5+ years of experience in Machine Learning, data mining and statistics
- Strong practical knowledge of, and proven experience with, analytical software packages and programming languages: Python, R, SQL, etc.
- Working knowledge of Big Data Framework (Hadoop, etc.)
- Strong understanding of version control (Git/GitHub)
- Strong problem solving, research and quantitative skills
- Exceptional time management and organizational skills, ability to manage multiple projects simultaneously and prioritize workload effectively
- Proven ability to perform complex data analysis on large volumes of data
- Professional oral and written communication and presentation skills, including the ability to effectively communicate analytical recommendations to both technical and non-technical audiences.
- Knowledge of Canadian banking and
Company
RBC
Location
Toronto
Country
Canada
Salary
125.000
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