Full Stack Data Science Engineer
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Key skills for this role
About the Role
TD is seeking a Full Stack Data Science Engineer to join a high-impact analytics team. The role involves leading end-to-end performance diagnostics, designing scalable analytics assets, and implementing AI/ML models.
Key Skills for This Role
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.
Requirements
- 5 years of relevant experience in advanced analytics, data science, or applied AI/ML in domains such as financial services, technology, consulting, or similar industries.
- 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).
- Proficient in Python, PySpark, SQL, Power BI, and Databricks (or similar platforms).
- Strong experience with PySpark for big data processing and PyTorch for deep learning model serving.
- 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.
- Strong ability to frame and structure complex business problems in financial services / retail banking.
- Experience working with existing ML/AI models (adjusting inputs, interpreting outputs) and building or modifying models as needed.
- Proficient in creating clear, compelling dashboards, visualizations, and data stories tailored to diverse audiences.
- Strong relationship management, storytelling, and business communication skills for senior audiences.
- Experience in customer analytics within financial services (nice to have).
- Bilingual proficiency (English/French) (nice to have).
Full Job Posting
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, 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 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.
- Data Stewardship: Confident working with structured and unstructured data from multiple sources, ensuring data usability, cleanliness, and reliability.
- Core Analytical Tools: Proficient in Python, PySpark, SQL, Power BI, and Databricks (or similar platforms).
- 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).
- 5 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.
- 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).
Pay Details
- CAD 120,000 CAD 154,000 CAD.
- The pay details posted reflect a temporary market premium specific to this role that is reassessed annually.
- Eligible for variable compensation.
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