Sr. Full Stack Data Science Engineer

Work Location: Toronto, Ontario, Canada Hours: 37.5 Line of Business: Analytics, Insights, & Artificial Intelligence Pay Details: $154,000 - $199,500 CAD The pay details posted reflect a temporary market premium specific to this role that is reassessed annually. TD is committed to providing fair and equitable compensation opportunities to all colleagues. Growth opportunities and skill development are defining features of the colleague experience at TD. Our compensation policies and practices have been designed to allow colleagues to progress through the salary range over time as they progress in their role. The base pay actually offered may vary based upon the candidate's skills and experience, job-related knowledge, geographic location, and other specific business and organizational needs. As a candidate, you are encouraged to ask compensation related questions and have an open dialogue with your recruiter who can provide you more specific details for this role. Job Description: Department Overview Join a high-impact analytics team that shapes business decisions through data, insights, and AI/ML. Collaborate with business leaders and cross-functional teams to uncover opportunities, build scalable analytics solutions, and translate complex analysis into actionable insights. Key Responsibilities Lead end-to-end performance diagnostics across customer, product, and advisor dimensions to identify growth, efficiency, and primacy opportunities. Translate curated data into actionable insights through hypothesis development, testing, analysis, and stakeholder storytelling. Design and deliver scalable analytics assets, including datasets, dashboards, segmentation frameworks, and predictive AI/ML models. Investigate, evaluate, and implement AI/ML tools and algorithms to solve complex business problems. Develop compelling visualizations and data stories tailored to technical and non-technical audiences. Partner with business owners to drive advanced analytics and AI/ML adoption. Lead cross-functional collaboration with data scientists, engineers, IT partners, and business process owners. Provide subject-matter expertise, mentorship, and guidance on advanced analytics and AI/ML methodologies. Identify emerging analytical trends and data needs to improve repeatable and scalable solutions. Required Qualifications & Skills Business Acumen : Strong ability to frame and structure complex business problems in financial services / retail banking, connect analytical insights to commercial levers (growth, efficiency, customer and advisor outcomes), and translate findings into clear, actionable recommendations. Demonstrated comfort engaging with senior executives and C‑suite stakeholders, influencing decisions through concise, insight‑driven storytelling. Applied Analytics Expertise : Demonstrated ability to creatively explore data, identify non‑obvious patterns, and rigorously test hypotheses to solve complex business problems. Brings an entrepreneurial mindset to analytics by proactively identifying opportunities, challenging assumptions, and delivering high‑impact insights that drive informed decision‑making. ML/AI Lifecycle Familiarity : Experience working with existing ML/AI models (adjusting inputs, interpreting outputs) and building or modifying models as needed. Solid knowledge of applied Machine Learning, Deep Learning, Large Language Models Solid cloud experience with Azure or AWS and cloud AI/ML services such as Databricks, Kubernetes, docker and container orchestration, Azure Machine Learning, Azure Data Factory Visualization & Communication : Proficient in creating clear, compelling dashboards, visualizations, and data stories tailored to diverse audiences, including senior executives and C‑suite leaders, translating complex analysis into concise, decision‑ready narratives. Data Stewardship : Confident working with structured and unstructured data from multiple sources, ensuring data usability, cleanliness, and reliability. Able to build or modify data pipelines or analytical assets. Core Analytical Tools : Proficient in Python, PySpark, SQL, Power BI, and Databricks (or similar platforms) for data preparation, analysis, and collaboration. Strong experience with PySpark for big data processing and PyTorch for deep learning model serving. Non-Technical Skills : Strong relationship management, storytelling, and business communication skills for senior audiences. Education & Experience A graduate or undergraduate degree in a quantitative or analytics-focused discipline (e.g., Business Analytics, Data Science, Statistics, Mathematics, Engineering, Computer Science, Finance, Actuarial Science). 7 years of relevant experience in advanced analytics, data science, or applied AI/ML in domains such as financial services, technology, consulting, or similar industries Data Manipulation : SQL, PySpark, Python AI & ML : Predictive Analytics, Natural Language Processing (NLP), Supervised and Unsupervised Learning, leveraging Generative AI tools and APIs, Model Development and Deployment, Experimentation and Optimization including emerging capabilities and their application in analytical workflows. Data Visualization : Power BI, Tableau Cloud & Big Data Platforms : Azure (ADF, Synapse, Databricks), Snowflake Data Engineering : ETL/ELT Pipelines, Apache Spark Nice-to-Have Experience in customer analytics within financial services (e.g., engagement, onboarding, cross-sell, retention, productivity insights). Expertise in optimizing analytical assets (data pipelines, models, dashboards) to drive measurable business impact. Bilingual proficiency (English/French). Aperçu du département Joignez-vous à une équipe d’analytique stratégique qui soutient la prise de décision d’affaires grâce à des analyses rigoureuses, aux données et aux capacités d’intelligence artificielle et d’apprentissage automatique (IA/AA). En partenariat étroit avec les leaders d’affaires et les équipes transversales, vous contribuerez à identifier des occasions à forte valeur ajoutée, à développer des solutions analytiques durables et à transformer des analyses complexes en recommandations claires, concrètes et responsables. Responsabilités principales Diriger des analyses de performance de bout en bout couvrant les dimensions clients, produits et conseillers, afin d’identifier des occasions d’amélioration liées à la croissance, à l’efficacité opérationnelle et à la relation client. Convertir les données en informations exploitables par l’élaboration d’hypothèses, leur validation analytique et la communication structurée des constats aux parties prenantes. Concevoir, développer et maintenir des actifs analytiques évolutifs, incluant des ensembles de données, des tableaux de bord, des cadres de segmentation et des modèles prédictifs en IA/AA. Évaluer et mettre en œuvre des outils, techniques et algorithmes d’IA/AA afin de répondre à des enjeux d’affaires complexes, dans le respect des cadres de gouvernance et de gestion des risques. Produire des visualisations et des récits de données clairs et percutants, adaptés à des publics techniques et non techniques. Travailler en étroite collaboration avec les partenaires d’affaires afin de favoriser l’adoption de l’analytique avancée et de l’IA/AA à l’échelle de l’organisation. Assurer une collaboration efficace avec les équipes de science des données, d’ingénierie, des TI et les responsables des processus d’affaires. Agir comme expert-conseil, en offrant du mentorat et de l’accompagnement sur les méthodologies avancées en analytique et en IA/AA. Surveiller les tendances émergentes en analytique et les besoins en données afin d’améliorer la réutilisabilité, la robustesse et l’évolutivité des solutions. Qualifications et compétences requises Sens des affaires et communication exécutive Capacité démontrée à structurer et à résoudre des problématiques complexes dans les services financiers et les services bancaires de détail. Aptitude à relier les résultats analytiques