Senior Data Scientist

Senior Data Scientist
📍 Location: San Francisco, California, United States (Remote - Candidates must resides in CA)
🏢 Industry: Financial Services
💼 Work Setting: Remote (Continental U.S.) with occasional travel.
Are you passionate about leveraging Machine Learning and Data Science to combat fraud, mitigate financial crime risks, and create safer, more trustworthy customer experiences?
We are looking for an experienced Machine Learning-focused Data Scientist to join a dynamic Risk and Fraud Analytics team. In this role, you will help design, develop, and deploy advanced machine learning solutions that strengthen fraud prevention capabilities, enhance customer protection, and support effective risk management strategies.
As a key contributor, you will collaborate closely with cross-functional teams including Risk, Product, Engineering, and Analytics to solve complex challenges, improve decision-making, and drive impactful business outcomes.
Key Responsibilities
Develop, validate, and deploy machine learning models to detect, predict, and prevent fraudulent activities in real time.
Ensure model reliability and reproducibility through thorough documentation, testing, monitoring, and performance evaluation.
Maintain high standards of data quality, integrity, and reliability across data pipelines and analytical workflows.
Partner with Risk and Business stakeholders to identify valuable model features, use cases, and opportunities for improvement.
Work alongside Engineering teams to streamline model deployment, scalability, monitoring, and observability in production environments.
Required Qualifications
5+ years of experience analyzing large datasets to solve business problems and generate measurable impact.
3+ years of hands-on experience in machine learning, predictive modeling, or advanced analytics.
Strong proficiency in SQL with experience handling complex and imperfect datasets.
Advanced Python programming skills with expertise in statistical analysis, machine learning, and data science best practices.
Experience deploying, managing, and monitoring machine learning models in production environments.
Ability to thrive in a fast-paced setting with changing priorities and evolving business needs.
Preferred Qualifications
Experience working in risk management, fraud detection, financial crime analytics, or related domains.
Familiarity with Generative AI, Large Language Models (LLMs), and their practical applications in risk assessment or fraud prevention.
Experience with modern data engineering and ETL tools such as dbt or similar platforms.
Understanding of model governance, compliance, and regulatory requirements within regulated industries.
Proven ability to build innovative solutions from the ground up in ambiguous or greenfield environments.

Work arrangement
Yes