Staff Data Scientist

Role overview A staff-level data scientist role embedded within a risk team at a fast-growing fintech, focused on fraud detection and financial-crime monitoring across the customer lifecycle. The position blends hands-on machine learning with technical leadership, prototyping models that protect customers while preserving a smooth user experience. It is suited to someone who can move between deep modeling work and cross-team strategy alignment.

Responsibilities - Design, validate, and deploy machine learning models that detect and prevent first- and third-party fraud in real time. - Strengthen model reproducibility and robustness through documentation, testing, and ongoing monitoring of production systems. - Maintain data quality and reliability across pipelines and analytical tools used by the risk function. - Collaborate with risk strategy on feature ideation and model applications, and partner with engineering to optimize deployment and observability. - Act as a technical lead, prototyping solutions, iterating on best practices, and helping uplevel the rest of the data science team. - Drive strategic alignment across teams with differing roadmaps, timelines, or architectures to keep fraud initiatives moving.

Requirements - Seven or more years of experience analyzing large datasets to drive impact, with at least five years specifically in machine learning. - Proficiency in SQL, including working confidently with imperfect real-world data. - Proficiency in Python with applied experience in statistical modeling and machine learning. - Track record of deploying and monitoring machine learning models in production environments. - Demonstrated leadership ability, helping peers grow while delivering strong individual work. - Comfort operating in a fast-paced environment where priorities evolve quickly.

Nice to have - One or more years of experience in risk, fraud, or related financial-crime domains. - Familiarity with large language models or other generative AI techniques applied to risk or fraud problems. - Experience with modern data and ETL tools such as dbt. - Background in model governance practices required in regulated industries like finance. - Experience building zero-to-one solutions in ambiguous or greenfield problem spaces.

Benefits and work setup Compensation includes competitive base salary plus equity in the form of stock options or RSUs, with a total rewards package benchmarked against the SaaS and fintech industry. New-hire offers are calibrated to experience, expertise, geographic location, and internal pay equity. Listed US salary range is $239,000 to $298,800 and the Canadian range is $225,900 to $282,400 CAD.