For our client, we are seeking a Staff Data Scientist to join the team of a leader in the Finance & Insurance space.
This role will focus on building and operationalizing machine learning solutions that strengthen fraud detection and prevention capabilities.
You will partner closely with Risk Strategy and Engineering to improve model performance, deployment reliability, and data quality across production pipelines.
The position also offers the opportunity to mentor teammates, shape best practices, and raise the overall standard of analytics and ML execution.
Location: Remote - US based candidates only, no visa sponsorship available
Compensation: $239,000 – $298,800 annually
Responsibilities
- Build, validate, and deploy ML models to detect and prevent real-time fraud
- Document, test, and monitor models for reproducibility and robustness
- Ensure data quality and reliability across machine learning pipelines and tools
- Collaborate with the Risk Strategy team on model applications and inputs
- Work with Engineering to optimize model deployment and observability
- Prototype and iterate on best practices while mentoring team members
Qualifications
- 7+ years of experience analyzing large datasets to solve problems and drive impact, including 5+ years in machine learning
- Proficient in SQL for managing imperfect data
- Expertise in Python with a focus on statistical modeling and machine learning
- Experience deploying and monitoring machine learning models in production
- Comfortable in fast-paced environments with evolving priorities
- Strong leadership skills to empower others and elevate team performance
- Ability to align strategic goals across teams with different timelines and architectures
Benefits
- Total rewards package includes base salary and equity
- Highly competitive compensation within the SaaS and fintech industry
- Offers based on experience, expertise, and internal pay equity
Our client is an equal opportunity employer. We encourage you to apply even if you don’t meet every qualification—your background could be exactly what this team needs.