Staff Data Scientist

About Our Client:

The organization operates in the fintech industry, focusing on providing secure and trustworthy financial services. It addresses financial crime risks such as fraud detection and customer behavior monitoring through technology and data-driven solutions designed to protect customers and strengthen the broader financial system.

About the Opportunity:

The Staff Data Scientist supports the Risk team by developing and deploying machine learning solutions for real-time fraud detection and prevention. This role strengthens fraud defenses by improving model accuracy, reliability, and observability while ensuring high-quality data and robust production workflows. The position also provides technical leadership, collaborates across Risk and Engineering teams, and mentors other team members.

Responsibilities:

• Build, validate, and deploy machine learning models for real-time fraud detection and prevention.

• Support model reproducibility and robustness through documentation, testing, validation, and monitoring.

• Ensure data quality, reliability, and consistency across data pipelines, analytical tools, and modeling workflows.

• Collaborate with Risk Strategy and Engineering teams to optimize model inputs, deployment processes, monitoring, and observability.

• Lead technical efforts to prototype, evaluate, and iterate on new fraud detection solutions.

• Establish and promote best practices for machine learning development, deployment, and model operations.

• Translate complex risk and fraud challenges into scalable data science solutions.

• Mentor team members and provide technical guidance on machine learning, modeling, and production systems.

• Contribute to cross-functional initiatives that improve fraud prevention and customer protection.

Requirements:

• 7+ years of experience using large datasets to solve complex problems and deliver measurable impact, including 5+ years of machine learning experience.

• Strong proficiency in SQL for analyzing, transforming, and working with imperfect or complex datasets.

• Strong Python skills with experience in statistical modeling and machine learning.

• Proven experience deploying, monitoring, and maintaining machine learning models in production environments.

• Strong understanding of machine learning methodologies, model evaluation, and production best practices.

• Ability to work effectively in a fast-paced and continuously changing environment.

• Demonstrated technical leadership skills with the ability to align teams across differing priorities, systems, and architectures.

Preferred Qualifications:

• 1+ year of relevant experience in risk management, fraud detection, or financial crime prevention.

• Familiarity with large language models or generative AI applied to risk management or fraud detection.

• Experience with modern data engineering and ETL tools such as dbt.

• Knowledge of model governance and risk management practices within financial or regulated industries.

• Experience developing solutions for ambiguous, greenfield, or rapidly evolving problem areas.

• Strong mentoring, communication, and cross-functional collaboration skills.

Pay Range and Compensation Package:

• U.S. base salary range of $239,000–$298,800 USD annually.

• Canadian base salary range of $225,900–$282,400 CAD annually.

• Total compensation may include base salary, equity in the form of stock options or RSUs, and benefits.

Equal Opportunity Statement: Our client is an equal opportunity employer. They celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, or national origin.

Note: RemoteHunter is not the Employer of Record (EOR) for this role. Our purpose in this opportunity is to connect exceptional candidates with leading employers. We help job seekers worldwide discover roles that match their goals and guide them to complete their full application directly through the hiring company’s career page or ATS.