About the Role
The role involves directing data science projects, building predictive models, and translating business requirements into analytical frameworks while ensuring regulatory compliance and technical excellence.
Responsibilities
- Lead the design and implementation of machine learning systems for financial risk assessment
- Collaborate with engineering teams to integrate models into production environments
- Define data strategy aligned with business objectives in banking operations
- Mentor junior data scientists and foster best practices in model development
- Evaluate data quality and implement pipelines for real-time analytics
- Develop fraud detection algorithms using supervised and unsupervised methods
- Present insights to executive stakeholders and non-technical audiences
- Ensure models comply with financial regulations and audit standards
- Optimize credit scoring methodologies using advanced statistical techniques
- Drive A/B testing frameworks for product feature evaluation
- Oversee data governance policies within analytical workflows
- Coordinate with compliance teams on model risk management
- Improve customer segmentation models for personalized banking services
- Manage end-to-end lifecycle of data science projects
- Evaluate third-party data sources for integration potential
- Support due diligence for fintech partnerships and integrations
- Champion reproducibility and documentation across modeling efforts
- Identify opportunities for automation in reporting and monitoring
- Lead exploratory data analysis to uncover business trends
- Maintain awareness of advancements in financial machine learning
- Ensure ethical use of customer data in algorithmic decision-making
- Balance innovation with operational constraints in deployment
- Troubleshoot model performance degradation in production
- Establish KPIs for data science deliverables
- Facilitate knowledge transfer between technical and business units
Compensation
Competitive salary with performance-based bonuses and equity package
Work Arrangement
Hybrid with core office days in major financial hubs
Team
Cross-functional team of data engineers, ML researchers, and product specialists
Technology Stack
- Primary languages: Python, SQL
- Cloud infrastructure: AWS SageMaker, GCP Vertex AI
- Data platforms: Snowflake, Apache Kafka
- ML frameworks: Scikit-learn, XGBoost, TensorFlow
- Version control: Git, DVC
- Monitoring: Prometheus, Grafana
- CI/CD: Jenkins, GitHub Actions
Regulatory Environment
- Models subject to SR 11-7 guidelines
- Regular internal audits for model validation
- Compliance with anti-money laundering (AML) reporting
- Data handling per GDPR and CCPA standards
- Documentation aligned with FRB oversight expectations
Available for qualified candidates
