• Develop AI and ML solutions to drive improved business performance and efficiency across domains • Lead end-to-end AI/ML projects from understanding business needs to data preparation, model development, solution deployment, and post-production monitoring • Dissect and solve complex business problems using analytical and quantitative approaches to identify patterns and correlations in large complex data sets • Integrate and curate data from a wide range of internal and external sources to develop rich datasets for predictive & prescriptive modeling as well as machine learning • Define and implement best practices to generate accurate reports, visualizations & dashboards to explain results simply and succinctly to technical and non-technical users • Create models for visual recognition, processing of natural language that analyze complex data to make predictions or decisions, mimicking human intelligence • Identify the most effective model architecture and parameters to drive accuracy and performance • Liaise with stakeholders to understand their needs and concerns and develop concise analytics to provide actionable insights to influence strategy and performance • Build partnerships, share knowledge, and deliver valuable predictive insights to inform strategic decisions, while highlighting potential risks and opportunities

Role Description As part of the Data Intelligence team, the Data Scientist strengthens decision-making in alignment with World Vision Canada’s mission and impact. The role delivers advanced analytics that enable confident action, improve experiences, and support digital enablement and innovation. - Designs and delivers end-to-end analytics solutions, from data sourcing and preparation through to development. - Applies statistical and machine-learning techniques to solve complex business problems. - Partners with cross-functional and technical teams to support deployment and maintenance. - Communicates insights clearly to drive understanding, adoption, and action. - Contributes to scalable, integrated solutions in a digital environment. - Advances insights-led decision-making and supports the Data Intelligence mandate. - Integrates diverse datasets and translates analysis into actionable insights. Responsibilities - Leads the design and development of advanced analytics solutions (e.g., customer lifetime value, churn prediction, segmentation). - Defines analytical approaches to ambiguous problems by partnering with stakeholders. - Sources, integrates, and prepares complex datasets from internal and external systems. - Applies statistical, machine learning, and data mining techniques to identify trends and patterns. - Enables deployment and ongoing performance of analytics solutions. - Translates analytics outputs into actionable recommendations through storytelling and data visualization. - Supports adoption and effective use of insights by guiding stakeholders. - Strengthens data trust and accessibility by contributing to well-structured data assets. - Identifies and implements improvements to analytical methods and tools. Qualifications - Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, or a related quantitative field, or equivalent experience. - 3–5 years of experience in data science or advanced analytics, including relevant academic research. - Strong foundation in statistics and machine learning, with experience applying techniques such as regression, classification, clustering, forecasting, and experimentation. - Strong Python or R (e.g. pandas, NumPy, scikit-learn) and SQL for working with large, complex datasets. - Experience working with modern data platforms (e.g. Azure, Snowflake). - Expertise in Natural Language Processing and applying domain knowledge to extract insights from unstructured data. - Experience designing and delivering end-to-end analytics solutions. - Ability to translate ambiguous business problems into analytical approaches and actionable insights. - Strong communication skills, with the ability to explain complex analysis clearly. - Certification in machine learning or big data and experience working in Agile environments considered assets. Benefits - Health Spending Account. - Up to 6% matched pension contributions. - Parental leave top-up. - Generous paid vacation, sick days, wellness and personal days. - Office closed extra days before long weekends (6x/year).

• As a Data Scientist, your mission will go far beyond the numbers: you will be challenged to translate complex business needs into predictive models and intelligent solutions. • In a strategic capacity, you will take a leading role in evolving our ecosystem of financial and digital products, creating direct, large-scale impact on our customers' lives.

• Own the technical architecture and engineering strategy for AutoQuote's cloud-native platform — spanning AI feature integration, data pipeline infrastructure, microservices design, and platform reliability. • Lead the design and delivery of the highest-complexity, highest-impact engineering initiatives on the team, setting the architectural patterns and engineering standards that guide the broader organization. • Define and enforce software quality standards, AI usage guidelines, and engineering best practices across the AutoQuote engineering organization. • Partner with engineering leadership, product management, and program stakeholders to translate program strategy into a coherent technical roadmap and prioritized backlog. • Evaluate, prototype, and champion emerging AI capabilities — including LLM integration, agentic frameworks, and AI-assisted development tooling — driving adoption across the team and into the product. • Drive responsible AI governance across AutoQuote, including prompt engineering standards, AI output evaluation practices, and compliance with Cisco security and data handling policies. • Mentor and technically develop senior and mid-level engineers; serve as the primary technical authority and critical issue point for complex engineering decisions. • Lead architecture reviews, critical design decisions, and cross-functional technical alignment sessions. • Represent AutoQuote engineering in program-level and executive forums; communicate technical tradeoffs, risks, and decisions with clarity to both technical and non-technical audiences.