Sr. Full Stack Data Science Engineer (Toronto)

Sr. Full Stack Data Science Engineer 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.

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.

Qualifications & Skills

Business Acumen: frame and structure complex, finance‑industry problems; translate findings into clear, actionable recommendations; comfortable influencing senior executives.

Applied Analytics Expertise: explore data, identify patterns, rigorously test hypotheses, and provide high‑impact insights.

ML/AI Lifecycle Familiarity: work with existing models, adjust inputs, interpret outputs, and build or modify models; solid knowledge of deep learning and large language models.

Cloud Experience: Azure or AWS, Databricks, Kubernetes, Docker, Azure Machine Learning, Azure Data Factory.

Visualization & Communication: create dashboards and data stories for diverse audiences, including C‑suite leaders.

Data Stewardship: work with structured and unstructured data, ensure cleanliness and reliability, and build or improve data pipelines.

Core Analytical Tools: Python, PySpark, SQL, Power BI, Databricks (or similar platforms).

Non‑Technical Skills: strong relationship management, storytelling, and business communication.

Education & Experience

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 sectors such as financial services, technology, or consulting.

Data manipulation proficiency in SQL, PySpark, and Python.

AI & ML expertise: predictive analytics, NLP, supervised and unsupervised learning, generative AI, model development and deployment, experimentation, and optimization.

Data visualisation skills with Power BI or Tableau.

Experience with cloud and big‑data platforms such as Azure (ADF, Synapse, Databricks) and Snowflake.

Data engineering: ETL/ELT pipelines, Apache Spark.

Nice‑to‑Have

Experience in customer analytics within financial services (engagement, onboarding, cross‑sell, retention, productivity insights).

Expertise in optimizing analytical assets (data pipelines, models, dashboards) to drive measurable business impact.

Bilingual proficiency in English and French.

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