Staff Data Scientist

Role Overview

We're seeking a

Staff Data Scientist

to support the continued evolution of a document verification and fraud prevention platform used across highly scaled digital environments. This role blends applied machine learning, experimentation, analytics, and product-focused problem solving. You'll work closely with engineering, product, and operations teams to develop models, uncover insights, and build frameworks that improve both performance and decision-making.

Key Responsibilities

• Develop and enhance machine learning solutions focused on identity verification, document analysis, fraud detection, image assessment, and biometric-related use cases.
• Explore new data signals, modeling techniques, and detection strategies to improve prediction accuracy and risk outcomes.
• Collaborate with engineering teams to launch, monitor, and refine models operating in live environments.
• Investigate fraud patterns, customer behaviors, and emerging threats using large-scale datasets.
• Design rigorous model assessments and performance studies using offline and production data.
• Establish metrics, reporting frameworks, and monitoring capabilities to track model effectiveness and business impact.
• Create internal tools that streamline evaluation workflows, investigations, experimentation, and operational processes.
• Partner with stakeholders across product, operations, and technology teams to translate business challenges into data-driven solutions.
• Present findings and recommendations that help guide product strategy and machine learning investments.

Requirements

• Advanced degree or equivalent industry experience in Data Science, Computer Science, Statistics, Mathematics, or a related technical discipline.
• 5+ years of experience working in machine learning, advanced analytics, fraud, risk, or data science environments.
• Demonstrated success building, validating, and improving machine learning models used in production.
• Strong proficiency with Python and SQL.
• Experience leveraging modern data and ML platforms such as Databricks, Spark, SageMaker, or similar ecosystems.
• Solid foundation in experimentation, statistical methods, model measurement, and performance analysis.
• Ability to communicate complex analytical findings to both technical and non-technical audiences.

Compensation

Competitive base salary, equity opportunity, and comprehensive benefits package.