Full Stack Data Science Engineer

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

• 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, 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 (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).

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