Data Scientist

The Role

Scotiabank is seeking a highly specialized and innovative Data Scientist to join our Customer Insights Data and Analytics team. This role is central to delivering analytics and actionable insights that deepen understanding of client experience and the end-to-end client journey across the bank’s business lines, helping identify opportunities to improve outcomes for both clients and the business. The ideal candidate will bring strong storytelling and communication skills, along with deep analytical expertise, to translate complex data into clear, actionable insights.

They will use AI/ML, experimentation, visualization, and robust Python skills to solve high-impact business challenges related to client experience and journey performance.

The Data Scientist will be a core member of the CID&A team, partnering closely with business lines and stakeholders to identify opportunities where analytics can improve client experience and optimize the client journey. You will work with a diverse team of data scientists, data engineers, product partners, and business stakeholders to frame problems, analyze data across multiple touchpoints, and turn findings into scalable recommendations and solutions.

You will help uncover drivers of client behaviour, pain points, and moments that matter across the journey, while ensuring the Bank’s risk appetite and risk culture are considered in decision making.

• Develop, test, and implement analytical approaches that uncover meaningful patterns in client behaviour, experience, and journey performance across multiple channels and touchpoints.
• Write and maintain high-quality Python and SQL code to source, prepare, and analyze large volumes of structured and unstructured data, building robust analytical datasets and pipelines.
• Design and apply statistical, machine learning, and exploratory analytical techniques to identify drivers of client satisfaction, friction points in the client journey, and opportunities to improve engagement, retention, and overall experience.
• Create compelling data visualizations, dashboards, and reports that clearly communicate insights, trends, and opportunities related to client experience and client journey performance to business and executive stakeholders.
• Stay up to date on advances in AI, machine learning, experimentation, visualization, and data science best practices. Support research and development focused on applying design thinking and advanced analytical techniques to better understand and improve the client journey.

• Support high-impact analytical use cases across a wide variety of business lines, delivering insights that help improve client experience, remove journey friction, and create value for both customers and the organization.
• Collaborate with key stakeholders and partners to translate business questions and customer experience challenges into scalable analytical approaches, leveraging available data assets and 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, product owners, and business partners to deliver scalable analytics, insight frameworks, and measurement approaches across the bank.

• Expert-level proficiency in Python for data manipulation, statistical modeling, and pipeline development.
• Demonstrated experience applying AI and machine learning techniques to solve business problems, ideally in areas related to client experience, customer behaviour, journey analytics, or insights generation.
• Direct experience partnering with business stakeholders to frame ambiguous problems, define hypotheses, identify meaningful measures, and translate analytical findings into practical recommendations.
• Practical experience with analytical techniques such as statistical analysis, segmentation, predictive modeling, prompt engineering, and other methods used to understand drivers of client behaviour and experience outcomes.
• Experience with big data tools such as SQL, Hadoop, and Spark.
• Experience applying AI and GenAI capabilities to support analytics, insight generation, automation, or productivity use cases, with an understanding of how these technologies can enhance business decision-making.
• Experience working with cloud-based data and analytics environments, including platforms such as Google Cloud and Microsoft Azure, to support scalable data processing, model development, and deployment.
• Proven ability to ingest, clean, integrate, and analyze large volumes of structured and unstructured data from multiple sources to build a cohesive view of the client journey.
• Experience with Dev Ops principles and/or software engineering best practices (e.g., Git, continuous…