Full Stack Data Science Engineering Specialist

The Toronto-Dominion Bank (Canada) (Toronto ON, Canada) Follow 2 days ago

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.
Applied Analytics Expertise : Demonstrated ability to creatively explore data, identify non‑obvious patterns, and rigorously test hypotheses to solve complex business problems.
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, and Large Language Models.
Solid cloud experience with Azure or AWS and cloud AI/ML services such as Databricks, Kubernetes, Docker, Azure Machine Learning, and 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.
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).
10 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, Generative AI tools and APIs, Model Development and Deployment, Experimentation and Optimization.
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).

Language Requirement (Quebec only)

Sans Objet

Benefits

Our Total Rewards package reflects the investments we make in our colleagues to help them and their families achieve their financial, physical, and mental well‑being goals. Total Rewards at TD includes a base salary, variable compensation, and several key plans such as health and well‑being benefits, savings and retirement programs, paid time off, banking benefits and discounts, career development, and reward and recognition programs.

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