About us:
Since 2014, Spring Financial has been simplifying the lending process for millions of Canadians facing all types of financial circumstances. Spring offers a growing range of financial products and services which currently include personal loans, mortgages, credit cards and secured loans - all of which are made available to Canadians without having to visit a branch!
We are high-energy entrepreneurs who operate with a real sense of urgency. We have created some of the best in market products and sales processes allowing millions of Canadians to get access to financing online, via text or over the phone all from the comfort of their own home! To learn more about our products please visit our website here: www.springfinancial.ca.
What You'll be doing:
- Create data-driven insights in order to enrich the end user experience
- Participating with other team members throughout the entire lifespan of each project, ensuring that our data-driven solutions are developed, implemented, and optimized to the highest standards of excellence
- 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 Bachelors degree in Data Science or a quantitative field, such as Finance, Computer Science, Economics, Mathematics or a similar technical discipline and 5+ years experience in applying analytics, data engineering or data science; or
- Master's Degree or PhD in Data Science or a quantitative field, such as Finance, Computer Science, Economics, Mathematics or a similar technical discipline and 4+ years experience in applying analytics, data engineering or data science
- Proficiency in working with SQL, Python 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:
- 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
