Full Stack Data Science Engineer

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. 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.

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. 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. Demonstrated comfort engaging with senior executives and C‑suite stakeholders, influencing decisions through concise, insight‑driven storytelling. Demonstrated ability to creatively explore data, identify non‑obvious patterns, and rigorously test hypotheses to solve complex business problems. 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. Proficient in creating clear, compelling dashboards, visualizations, and data stories tailored to diverse audiences.

Bachelor's Degree