Lead Data Scientist

This is a fully remote position, open to applicants in Canada.

📋 Description

• Design, develop, and implement sophisticated statistical or machine learning models for credit risk, pricing, collections, fraud, and other significant business applications that enhance data-driven decision-making.

• Oversee the comprehensive delivery of data science projects from problem identification and model creation to deployment, monitoring, and ongoing maintenance.

• Collaborate with cross-functional teams, including Portfolio Strategy, Engineering, Product, Underwriting, Sales, and Collections, to integrate models into our applications and proactively address challenges in critical business areas.

• Establish and maintain standards for model development, code quality, and documentation; guide technical design choices across the team.

• Serve as a technical mentor for team members, promoting a culture of continuous learning and high analytical standards.

• Convey complex technical ideas and their business implications to both technical and non-technical audiences.

• Construct and oversee production machine learning pipelines and monitoring systems to guarantee models are dependable, scalable, and continually enhancing.

⛳️ Requirements

• Over 8 years of practical experience in model development and deployment utilizing advanced statistical and machine learning methods such as generalized linear models, gradient boosting, and deep learning.

• Extensive experience in constructing and deploying credit risk models, particularly underwriting models, within the fintech, lending, or financial services sectors is highly desirable.

• Familiarity with real-time models, decisioning engines, and production-level machine learning pipelines is preferred.

• Proficient in Python, SQL, and Git.

• Experience with workflow orchestration tools, such as Metaflow, is preferred.

• Background in deploying and managing models on cloud platforms (AWS, Sagemaker).

• Strong grounding in statistics and machine learning, along with knowledge of experimental design.

• Exceptional project management and communication abilities.

• Strong critical thinking and problem-solving skills.

• Preferred additional experience includes cloud data warehouses (e.g., Snowflake, Databricks), Arize, Metaflow, Sagemaker, decision engines (e.g., Taktile), and feature stores (e.g., Tecton).

• A Bachelor's degree in Financial/Applied Math, Operations Research, Economics, and/or Statistics is required; a Master's or PhD is a plus.

🏝️ Benefits

• Medical

• Dental

• Vision

• Flexible time-off policy

• Paid parental leave

• RRSP match

• Wellness reimbursement

• Volunteering days

• Annual professional development budget

• Charitable donation match