Job description
Join HarborEdge Bank as a Senior Banking Data Scientist and elevate data-driven decision making across retail, corporate, and risk verticals. You will partner with risk, product, and technology teams to build scalable models, dashboards, and insights that drive revenue, reduce loss, and improve the customer experience.
We are seeking a strategic technologist who can translate complex data into actionable business outcomes and collaborate across functions to drive measurable impact in a regulated financial services environment.
Responsibility
- Lead the design, development, and deployment of advanced predictive models for credit risk, fraud, and customer behavior.
- Partner with data engineering to build scalable data pipelines and feature stores supporting production ML workloads.
- Develop and implement robust model monitoring, governance, and versioning practices to ensure reliability and compliance.
- Translate analytics into actionable business insights; present findings to senior stakeholders and executives.
- Drive experimentation, A/B testing, and ROI assessment for analytics initiatives.
- Mentor and coach junior analysts and data scientists; foster a culture of data-driven decision making.
- Ensure model explainability and regulatory alignment; collaborate on audits and risk assessments.
Qualification
- Master’s degree or PhD in data science, statistics, computer science, quantitative finance, or related field; or equivalent practical experience.
- 5+ years of banking or financial services analytics; deep knowledge of credit risk, fraud, AML, and customer analytics.
- Proficiency in Python or R; SQL; experience with Spark or similar big data frameworks; exposure to cloud platforms (AWS, GCP, Azure).
- Strong modeling skills across regression, tree-based methods, and ML pipelines; solid feature engineering expertise.
- Experience deploying models to production; model risk management, governance, and monitoring.
- Excellent communication and stakeholder management; ability to translate technical results for non-technical audiences.
- Experience with data visualization tools (Tableau, Looker) and dashboarding.
- Knowledge of regulatory standards ( Basel II/III, CCAR, SOX) and data privacy requirements; ability to explain model decisions.