Staff Data Scientist developing advanced machine - learning models for MindBridge Analytics’ enterprise datasets. Leading experimentation, production deployment, technical standards and mentorship.
About the role
- Lead Data Scientist transforming complex data into strategic decisions at fintech company. Collaborate across teams to deploy advanced analytical solutions and mentor others.
Responsibilities
- Design, develop and deploy advanced statistical or machine learning models for credit risk, pricing, collections, fraud, and other high-impact business use cases that drive better data-driven decisions
- Lead end-to-end delivery of data science initiatives from problem framing and model design through deployment, monitoring and ongoing maintenance
- Partner with cross-functional teams including Portfolio Strategy, Engineering, Product, Underwriting, Sales and Collections to integrate models into our applications, and proactively identify and solve problems in critical business areas
- Define and set standards for model development, code quality, and documentation; guide technical design decisions across the team
- Act as a technical mentor to team members, fostering a culture of continuous learning and rigorous analytical standards
- Communicate complex technical concepts and business implications to both technical and non-technical stakeholders
- Build and maintain production machine learning pipelines and monitoring systems to ensure models are reliable, scalable and continuously improving
Requirements
- 8+ years of hands-on model development and deployment experience using advanced statistical and machine learning techniques such as generalized linear models, gradient boosting and deep learning
- Deep experience in building and deploying credit risk models, especially underwriting models, in the fintech, lending or financial services industry is highly preferred.
- Experience with real-time models, decisioning engines, and production-grade machine learning pipelines is preferred.
- Expert in Python, SQL and Git
- Experience with workflow orchestration tools, such as Metaflow is preferred
- Experience deploying and managing models within a cloud platform (AWS, Sagemaker)
- Strong foundation in statistics and machine learning, and knowledge of experimental design
- Excellent project management and communication skills
- Strong critical thinking and problem-solving ability
- Nice to have experience: cloud data warehouses (e.g. Snowflake, Databricks), Arize, Metaflow, Sagemaker, decision engines (e.g. Taktile), feature stores (e.g. Tecton)
- Bachelor's degree in Financial/Applied Math, Operations Research, Economics, and/or Statistics. Masters/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
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