Senior Data Scientist

This is a hybrid position requiring presence in Vancouver, BC.

What You'll be doing:

• Create data-driven insights in order to enrich the end user experience
• Communicate with multiple teams by creating reports in order to share insights and overall data story
• Work alongside senior level stakeholders to scope analytics projects to ensure data science deliveries are driving business impact
• Evaluate consumer fintech data by using various methods and tools including but not limited to; Machine Learning, Statistics, Data Analysis, Quantitative Finance, and Modern Programming
• Facilitate data driven initiatives in the following areas to paint a cohesive data picture:
• Customer Segmentation
• Underwriting
• Lifetime Value Optimization
• Financial product development
• Fraud and Ethical Lending Analysis

What You Should Already Have:

• A Bachelor’s degree in a quantitative field, such as Finance, Computer Science, Economics, Mathematics or a similar technical discipline
• 5+ years experience in applying analytics, data engineering or data science
• Proficiency in working with SQL, Python and/or R across complex real world datasets
• Proficiency in using modern data visualization tools, such as Tableau, PowerBI or similar
• Experience deploying ML models using machine learning as a service platforms such as Amazon SageMaker, Google Cloud AI, and Microsoft Azure
• Excellent written and verbal communication skills
• Aptitude for critical thinking as it relates to complex problems and challenge assumptions in order to implement creative solutions

What Would Be Nice To Have:

• A Master's degree or higher, focused in data science, analytics finance, or software engineering
• Experience applying data science consumer finance, fintech and other Financial Industry domains
• Familiarity with related disciplines such as consumer finance, project
• management, user experience design, software development and digital
• marketing
• Experience in establishing data technology foundations for agile business
• intelligence, data lakes and data workbenches, and applied machine learning/AI
• Experience with large-scale data analysis technologies (e.g. Relational and
• NoSQL data stores, Spark framework, and cloud development in the AWS
• Ecosystem)
• Specializations and applied experience in the following areas:
• Consumer Credit and Risk Modeling
• Fraud and Anomaly Detection
• Customer Segmentation and Intent Modeling
• Marketing Attribution and Targeting
• Recommendation Engines