Data Scientist

at  Propel Holdings Inc

ABOUT US:

Propel (TSX: PRL) is the fintech company building a new world of financial opportunity by facilitating access to credit for consumers underserved by traditional financial institutions. Through its AI-driven platform, Propel evaluates customers in a more comprehensive way than traditional credit scores can. Our revolutionary fintech platform has already helped consumers access over one million loans and lines of credit and over one billion dollars in credit.
To build a new world of opportunity we bring together the brightest talent to help us build opportunities. We are entrepreneurs and believe in measuring success through results and growing within; talent and hard work never goes unnoticed. At Propel, we are here to change the way employees, customers and shareholders succeed together.
We are a team of passionate entrepreneurs, who foster curiosity and growth in our employees. Our culture is why we’ve been so successful and why our employees choose Propel to build their careers. Its also why we’re one of North America’s fastest growing companies and a Best Place to Work.
Join us as we change the way employees, customers and shareholders succeed together.

REQUIREMENTS

• University degree in relevant STEM disciplines (Mathematics, Computer Sciences, Electrical/Computer/Software Engineering or similar degree).
• Strong quantitative/statistical modeling capabilities, along with 2-3 years of experience in credit scoring and model development.
• 2-3+ years of experience within the consumer lending environment preferred.
• Production experience with experimental design, statistical analysis, machine learning and predictive modeling (e.g., cross-sell, upsell, attrition, acquisition, and lookalike models).
• Programming skills in Java, R or Python.
• Experience with common machine Learning libraries in R, Python, Spark.
• Experience with UNIX tools and shell scripting.
• Solid SQL skills for querying relational databases (e.g., SQL Server, DB2, MySQL).
• Experience using and implementing visualization tools like D3, Tableau or DOMO.

• Ingest massive volumes of structured and unstructured format data, model, transform and store it in a variety of data stores.
• Leverage distributed and open-source computing tools (e.g. Spark, Hadoop, R, Python) for analysis, data mining and modeling.
• Collaborate with Data engineering and operational teams to deploy models and algorithms in production, across different channels and customer platforms.
• Create and apply model and algorithm testing strategies to measure conduct multi-variate testing and A/B testing to measure effectiveness of models and make ongoing changes.
• Prepare detailed documentation to outline data sources, models and algorithms used and developed.
• Present results to business line stakeholders and help implement real data-driven changes.
• Design and Develop statistical models for usage in: Underwriting, Existing Customer Management, Marketing Campaigns, and Collection/Recovery.
• Assessing, cleaning, merging, and analyzing large datasets.
• Design and Develop business logic, pricing strategies, business forecasts, while optimizing profitability.
• Utilize advanced statistical software to develop linear/non-linear, parametric/non-parametric, and classical/machine learning based predictive modeling/data mining analytic methodologies to minimize credit/fraud losses, maximize response and approval rates, and/or profitability of products.
• Assist with the implementation of scorecards
• Writing of clear and detailed model documentation.
• Provide solutions and ideas to business partners to solve complex modeling and other analytic problems.