Data Scientist

Scotiabank•Toronto, ON

6d•Onsite

About The Position

Scotiabank is seeking a highly specialized and innovative Data Scientist to join our Customer Insights Data and Analytics team. This role is central to building and deploying next-generation AI/ML products across the bank’s business lines, focusing on deriving powerful insights from multiple data sources and applying them to business opportunities. The ideal candidate will showcase superior storytelling while being an integral part of the organization’s AI/ML strategy, using cutting-edge Github Copilot and Gemini prompting techniques, and robust Python coding to solve high-impact business challenges. In this role, you will be a core member of the CID&A team, focused on collaboration with business lines and other stakeholders to identify opportunities that would benefit from a data science solution, creating value for both the bank and its customers. You will work closely with a diverse team of data scientists, data engineers, AI/ML product managers to understand business partner challenges and processes, turning those insights into scalable, working solutions. You will have direct exposure to working production models and will be responsible for creating new, high-impact AI/ML solutions. You will also understand how the Bank’s risk appetite and risk culture should be considered in decision making.

Requirements

Expert-level proficiency in Python for data manipulation, statistical modeling, and pipeline development.
Proven, hands-on experience designing and optimizing prompts for advanced large language models (specifically Gemini, or comparable LLMs) tailored for unstructured content analysis.
Direct experience working with business stakeholders to formulate a
Practical experience with ML/AI techniques, including supervised, unsupervised, and specifically deep learning and NLP methods.
Experience with big data tools such as SQL, Hadoop, and Spark.
Proven ability to ingest, clean, and work effectively with large volumes of structured and unstructured non-traditional data.
Experience with DevOps principles and/or software engineering best practices (e.g., Git, continuous integration/delivery, Jira).
University/Post graduate degree in a relevant STEM discipline (Science, Technology, Engineering, and Mathematics).
Effective communication skills with the ability to prepare clear project documentation and compelling presentations.
Ability to translate technical knowledge into tangible business value and collaborate effectively across technical and non-technical teams.
Working knowledge of visualization tools such as Power BI.

Responsibilities

Develop, test, and implement highly effective models optimized for specific document understanding tasks (e.g., data extraction, classification).
Write and maintain high-quality Python code to preprocess, process, and analyze large volumes of structured and unstructured documents, building robust data pipelines.
Design, build, and rigorously evaluate specialized machine learning models for document understanding, ensuring accuracy, fairness, and scalability.
Create data visualizations and reports to communicate insights. Have excellent communication skills to present these complex data insights.
Stay up-to-date on the latest advances in generative AI, Python coding libraries, machine learning best practices, and the field of Document AI.
Support Research & Development focused on the effective application of design thinking and advanced techniques.
Support high-impact analytical use cases focused on supporting a wide variety of business lines, delivering AI/ML products that simultaneously provide value to customers and the organization.
Collaborate with key stakeholders and partners to convert customer requirements into a scalable solution leveraging all available reusable components.
Understand how the Bank’s risk appetite and risk culture should be considered in decision making related to model development and deployment.
Collaborate seamlessly with data scientists, data engineers, software engineers, and ML product owners to implement scalable ML/AI products throughout the bank.