Senior Fraud ML Engineer

Carbon Data Solutions; New York (NY); On-site; USD 150,000 - 190,000; Full time

Benefits offered by this job

Flexible PTO

Health and wellness stipend

Relocation support to NYC or SF

In-office perks: lunch, snacks, drinks

Job summary

Ramp is seeking a fraud ML leader to build core models, design data architectures, and set strategic roadmaps to mitigate fraud while minimizing friction for legitimate users. You will partner with product and engineering across design, implementation, execution, and analysis.

Lead end-to-end ML initiatives, deploy models to production, and collaborate with Fraud Engineering and Data Platform teams to leverage diverse data sources for context-rich decisions.

Qualifications

• Bachelor’s degree in a quantitative field.
• 5+ years of industry experience as ML Engineer, Applied Scientist or Data Scientist.
• Strong Python experience across ML techniques and backend engineering.
• Experience deploying ML models to production.
• Strong knowledge of SQL (Snowflake, Postgres).
• Fluency with agentic AI tools for software development and data analysis.
• Thrives in a fast-paced startup environment with iterative solutions.

Responsibilities

• Apply statistical and ML techniques to detect fraud, platform abuse, and identity theft.
• Prototype and productionize ML models and rules-based systems.
• Collaborate with Fraud Engineering and Data Platform teams to leverage data sources.
• Influence processes, tools, and systems for scalable, better decisions.

Skills

Strong Python experience

ML/DS expertise

SQL proficiency

AI tools fluency

Startup environment adaptability

Education

Bachelor's degree in Math, Economics, Physics, CS

PhD (nice-to-have)

Tools

numpy

pandas

scikit-learn

PyTorch

Job description

Ramp is seeking a fraud ML leader to build core models, design data architectures, and set strategic roadmaps to mitigate fraud while minimizing friction for legitimate users. You will partner with product and engineering across design, implementation, execution, and analysis.

Lead end-to-end ML initiatives, deploy models to production, and collaborate with Fraud Engineering and Data Platform teams to leverage diverse data sources for context-rich decisions.