Senior Data Scientist, Fraud Applied AI and Innovation

RBC•Toronto, ON

3d•Onsite

About The Position

This role involves applying machine learning, artificial intelligence, and advanced analytical methodologies to support Fraud Management's key priorities. This includes developing predictive models for fraud detection, optimizing existing tools, creating automated workflows, and designing innovative solutions. The Senior Data Scientist will represent the Applied AI & Innovation team as a Subject Matter Expert (SME) on projects, collaborate with stakeholders, and assist in developing best practices and training junior team members.

Requirements

2+ years’ experience in Machine Learning, data mining and statistics
Ability to perform complex data analysis on large volumes of data and present findings to stakeholders
Knowledge of Canadian banking and payment industry, payments transaction data and financial fraud
Strong practical knowledge of, and proven experience with, analytical software packages and various programming languages (Python, SQL, etc.)
Working knowledge of Big Data Framework (Hadoop, Spark, etc.)
Exceptional communication and presentation skills, including ability to effectively communicate analytical recommendations to both technical and non-technical audiences
Excellent time management skills and demonstrated ability to prioritize workload and manage multiple projects simultaneously
Degree in a quantitative discipline and strong problem solving, research and quantitative skills

Nice To Haves

Post-graduate degree in a quantitative discipline
Experience with Docker and Kubernetes
Prior experience in fraud detection or data analytics

Responsibilities

Develop machine learning (ML) models for real-time fraud detection and explore opportunities for applying ML solutions to other challenges within FM, while following all the standards set for model development.
Contribute to developing ML strategy for FM, collaborate with peers to integrate the model into the detection ecosystem and monitor, evaluate and optimize tools and technologies developed to improve FM efficiency.
Collaborate with Detection Analytics partners to incorporate feedback and ensure any changes resulting in an impact to the detection ecosystem are communicated.
Identify opportunities and develop automated pipelines to replace or enhance existing processes, utilizing the full suite of available technology and tools to build the most effective solution.
Provide thought leadership to support FM’s key priorities where there is a dependence on data analytics or machine learning and deliver on all initiatives that support strategic imperatives in line with project timelines.
Develop predictive data models, quantitative analyses and visualization of targeted, big data sources and oversee acquisition of data while ensuring data quality and comprehensiveness.
Lead data exploration and analytic projects, provide on-going coaching of big data topics and explore semantic data capabilities through NLP, text mining and machine learning techniques.