Data Scientist

Sardine; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Sardine is seeking a Data Scientist to develop machine learning solutions for fraud prevention. This remote role involves collaborating with clients and internal teams to enhance risk mitigation strategies while maintaining user experience.

The Data Scientist will design and deploy data-driven solutions that leverage ML to combat evolving fraud threats, building models and partnering with clients to create impactful risk strategies and scale models for production.

Qualifications

• Strong background in data science and quantitative modeling.
• Experience with fraud risk analytics and ML pipelines.
• Ability to communicate complex insights to non-technical audiences.

Responsibilities

• Develop ML models for fraud prevention and risk scoring.
• Collaborate with clients and internal teams to define requirements.
• Scale models for production and monitor performance in production.
• Explain findings to non-technical stakeholders and drive data-driven decisions.

Skills

Fraud prevention

Machine learning

Data science

Quantitative modeling

Risk assessment

Stakeholder communication

Job description

Sardine seeks a Data Scientist to develop machine learning solutions for fraud prevention. This remote role requires collaboration with clients and internal teams to enhance risk mitigation strategies. Candidates with a strong background in data science and quantitative modeling are encouraged to apply.

At Sardine, a leader in fraud prevention and AML compliance, the Data Scientist will design and deploy data-driven solutions that leverage machine learning to combat evolving fraud threats. The role involves building models and collaborating with clients to create impactful risk mitigation strategies while maintaining user experience.

The position emphasizes a data-first approach, requiring the Data Scientist to evolve risk metrics and support the engineering team in scaling models for production. Success in this role is defined by the ability to explain complex findings to non-technical stakeholders and to drive data-driven decision-making across teams.