Data Scientist

RBC•Toronto, ON

6d•Onsite

About The Position

In this role, you will design and implement data science / machine learning solutions using RBC’s enterprise suite of AI and analytics tools. The group specializes in taking full advantage of large data sets to explore and discover new insights that would have not been possible with traditional analytics. Leveraging leading edge technologies and capabilities, the group applies generative AI, machine learning, and statistical modelling techniques to help RBC improve data controls, understand the changing business environment, discover new growth opportunities, and determine where improvements can be made.

Requirements

Bachelor’s in Computer Science, Engineering, Statistics, Mathematics, or a related quantitative field.
1-2 years of experience delivering data-driven solutions using machine learning, AI, predictive analytics, and/or statistical modeling.
Familiar with popular programming languages, libraries, and frameworks for data science (Python, SQL, pandas, numpy, sklearn).
Experience in data visualization and communicating technical concepts to a non-technical audience.
Excellent problem solving, collaboration, and organizational skills.

Nice To Haves

Master’s or Ph.D. in a quantitative field.
Experience with data extract, transform, and load processes on a variety of data types.
Experience with generative AI, deep learning frameworks (PyTorch, TensorFlow), vector databases, and/or graph data.
Knowledge of lifecycle of data science products from design to delivery.

Responsibilities

Support the development and deployment of statistical / AI / ML solutions, working in a team of data scientists and data engineers.
Collect, transform, and analyze large datasets (structured and unstructured) to extract insights, support model development, and enable predictive analytics.
Validate, measure, and document model performance in accordance with business and stakeholder requirements.
Collaborate with business partners to produce insights from business-defined requirements and hypotheses.
Leverage visualization tools/packages to story-tell and to convey data-driven insights with actionable recommendations to business partners and key stakeholders.
Quickly learn new methods, tools, and technologies.