Data Scientist

Skills

About the Role

You will design and deploy data-driven solutions to detect and prevent fraud by rapidly prototyping proof-of-concept models and building scalable, production-ready systems. You will work directly with clients to understand their fraud challenges, evolve risk metrics and datasets, measure causal impact, and collaborate with engineering to operationalize and optimize models while preserving user experience.

Requirements

• 7+ years of experience in data science or quantitative modeling, ideally in risk or fraud contexts
• Advanced degree in a quantitative field (Mathematics, Statistics, Computer Science, Engineering, Economics, etc.)
• Strong working knowledge of Python, R, Spark, SQL, or equivalent
• Sharp critical thinking and creative problem-solving skills with a bias toward action
• Proven ability to explain complex technical findings to non-technical stakeholders and clients

Responsibilities

• Champion a data-first approach across internal teams and client engagements
• Build and deploy machine learning models to prevent fraud
• Design and prototype proof-of-concept models and production-ready solutions using machine learning and graph analytics
• Use data and models to support development of risk mitigation strategies while preserving user experience
• Work directly with clients to understand challenges and deliver data-driven solutions
• Evolve risk metrics, supporting datasets, and measure causal impact of initiatives
• Collaborate with engineering to scale models into production and optimize performance

Benefits

• Equity
• Early exercise for all options including pre-vested
• Remote-first work and work from anywhere
• Flexible paid time off and year-end break
• Health insurance, dental, and vision coverage for employees and dependents (US and Canada specific)
• 4% matching in 401k / RRSP (US and Canada specific)
• MacBook Pro provided
• One-time home office setup stipend
• Monthly meal stipend
• Monthly social meet-up stipend
• Annual health and wellness stipend
• Annual learning stipend