Senior Analyst, Data Science

About the role

We are seeking a curious and analytically rigorous Senior Analyst, Data Science to design and build models, analyses, and decision-support tools that drive transformation across the firm's home-office functions.

Responsibilities

• Design and execute end-to-end analyses that surface meaningful business insights, from data extraction and cleaning through modeling and interpretation.
• Apply statistical methods — including hypothesis testing, regression, and causal inference — to answer business questions with the rigor and clarity expected in a regulated environment.
• Translate complex analytical outputs into clear narratives and visualizations for business stakeholders and senior leadership.
• Build, validate, and deploy supervised and unsupervised machine learning models supporting use cases such as risk tiering, surveillance and alert prioritization, anomaly detection, segmentation, and workload/cost-to-serve modeling.
• Evaluate model performance using appropriate metrics and clearly communicate trade-offs, assumptions, and limitations to both technical and non-technical audiences.
• Stay current on advances in applied ML and bring emerging methods to bear on relevant business problems.
• Work closely with data engineers, product managers, business stakeholders, and subject matter experts to access, understand, and leverage data assets across the enterprise.
• Document analytical workflows, assumptions, code, and findings to ensure reproducibility, knowledge sharing, and audit readiness.
• Contribute to building a scalable data science practice by identifying opportunities to improve tools, processes, and methodologies.

Requirements

• 3+ years of experience in data science, quantitative analysis, or applied research role in a business setting.
• Bachelor's degree in Statistics, Mathematics, Computer Science, Economics, Data Science, or a related quantitative field required.
• Experience with Python for data manipulation, statistical analysis, and machine learning that goes beyond Jupyter notebooks; strives for clean, Git version-controlled code.
• Experience working with large-scale data in SQL & Snowflake; comfortable building and maintaining clean, reproducible data pipelines as needed to support modeling and analysis work.

Core Competencies

• Solid grounding in statistics, probability, and machine learning fundamentals.
• Hands-on experience with causal inference methods and experimental design.
• Exposure to anomaly detection techniques applied to surveillance, fraud, or risk problems.
• Experience working with large-scale data in SQL & Snowflake; comfortable building and maintaining clean, reproducible data pipelines as needed to support modeling and analysis work.
• Data visualization skills and the ability to communicate findings clearly to non-technical stakeholders.