Lead Data Scientist

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

📋 Description

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

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

• Collaborate with various cross-functional teams, including Portfolio Strategy, Engineering, Product, Underwriting, Sales, and Collections, to integrate models into our applications while proactively identifying and addressing issues in critical business domains.

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

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

• Articulate complex technical concepts and their business ramifications to both technical and non-technical audiences.

• Develop and maintain production-level machine learning pipelines and monitoring systems to ensure models are dependable, scalable, and continuously enhanced.

⛳️ 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 building and deploying credit risk models, particularly underwriting models, within the fintech, lending, or financial services sector is highly desirable.

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

• Proficient in Python, SQL, and Git.

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

• Experience 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 qualifications include experience with cloud data warehouses (e.g., Snowflake, Databricks), Arize, Metaflow, Sagemaker, decision engines (e.g., Taktile), and feature stores (e.g., Tecton).

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

🏝️ Benefits

• Medical

• Dental

• Vision

• Commuter benefits

• Flexible time-off policy

• Paid parental leave

• 401k match for US employees

• Wellness reimbursement

• Volunteering days

• Annual professional development budget

• Charitable donation match