Data Scientist III

TD•Toronto, ON

21h•CA$96,900 - CA$136,800•Onsite

About The Position

Within Analytics, Insights and Artificial Intelligence (AI2), this role will help develop and deliver AI, machine learning, advanced analytics, and business intelligence capabilities that support North American Customer Operation (NACO) AI Strategy and Roadmap. The work spans foundational data and reporting, interaction analytics, agent assist, knowledge management, next best offer/action, workforce management, conversational IVR and routing, authentication, quality assurance, compliance, and emerging agentic AI use cases. The NACO team is transforming how TD supports customers and colleagues across contact centres in 5 business pillars – CPB, US, Wealth, TDI, & Customer Assistance. NACO’s purpose is to bring TD to life through personalized, effortless experiences, while becoming a strategic distribution channel that enables simple and fast solutions and deepening customer relationships. You’ll join a collaborative, multifaceted team of data scientists, machine learning model developers, data engineers, product owners, technology partners, and control functions working together to turn AI-enabled capabilities into impactful business outcomes. Together, we help NACO improve customer and colleague experiences, unlock operational efficiency, strengthen quality and compliance, and create value across TD.

Requirements

5+ years of experience in data science, advanced analytics, or a related quantitative field
Degree in a quantitative discipline (e.g., data science, statistics, computer science, engineering, finance); graduate degree preferred
Advanced proficiency in Python, SQL, and modern analytics platforms (e.g., Databricks)
Strong foundation in statistics, experimentation, applied machine learning, and working with large datasets (structured and unstructured)
Experience delivering production‑ready analytics solutions in a cloud environment
Strong communication and storytelling skills, including the ability to present complex analytical concepts, sound recommendations, influence decisions, and trade-offs to business partners, executives, and technical audiences.
Ownership mindset, commitment to risk management, responsible AI, data governance, privacy, compliance, and internal policy expectations.
Experience with data visualization tools such as Tableau or Power BI.

Nice To Haves

Knowledge of Contact Centre business operations is preferred.

Responsibilities

Work closely with NACO Product Owners and act as a trusted advisor to identify high-value use cases, clarify requirements, assess feasibility, and convert business needs into practical analytics, machine learning, and AI solutions.
Translate AI concepts to business outcomes that matter, crafting clear narratives that support technical and non-technical audiences.
Lead data science initiatives from business problem definition through planning, solution design, development, validation, implementation support, measurement, and sustainment.
Plan and estimate work effort from cross functional data scientists, model developers, data engineers and other technology & control partner teams to deliver scalable AI capabilities for all NACO pillars.
Use Python, PySpark, SQL, PowerBI, statistical methods, machine learning, natural language processing, experimentation, and cloud-based analytics tools to extract, prepare, analyze, model, and operationalize large and complex datasets.
Liaise with Enterprise AI COE where necessary to deliver state-of-the-art AI solutions.
Identify key data sources, assess data quality and feasibility for data access and data inputs that are used by AI solutions.
Leverage Test & Learn methodologies to calculate the value of AI solutions and provide data insights for decision making.
Document dataflow, technical designs, test plans and results, including governance documentation that is used for second line approvals with Privacy, Compliance, and Model Risk Management.
Follow TD Scaled Agile standards for documenting work in Jira/Confluence/Github, managing timelines and ensuring quality standards for AI2 deliverables are met consistently.
Proactively resolve risks and impediments that impact AI2 deliverables, weigh out trade-off decisions, and work with executive leadership to break down barriers.
Provide thought leadership on emerging analytics techniques, responsible AI, industry trends, and best practices.