Senior Data Scientist - Remote Fraud Risk AI

Oscilar; United States; Remote; USD 120,000 - 170,000; Full time

Benefits offered by this job

Remote-first culture

401k

Health insurance (US)

Unlimited PTO

Job summary

Oscilar is seeking a Data Scientist to build and deploy advanced fraud detection models. As an early ML team member you’ll shape our stack and help protect customers’ businesses from fraud across the platform.

You will collaborate with engineering, product, and operations; analyze large datasets with Spark and Dask; evaluate models; and communicate results to stakeholders while upholding data privacy.

Qualifications

• 3+ years in data science focusing on fraud prevention and/or anti-money laundering.
• Proficiency in Python.
• Cloud development (AWS, GCP, Azure) and MLOps is a plus.
• Strong knowledge of ML algorithms and statistical techniques for fraud detection.
• Experience with large datasets using distributed systems like Apache Spark and Dask.

Responsibilities

• Develop and implement advanced fraud detection models to identify and prevent fraud across the platform.
• Collaborate with engineering, product, and operations to design fraud detection systems and processes.
• Analyze large datasets to identify patterns and insights to improve prevention strategies.
• Evaluate and optimize existing models to adapt to evolving fraud trends.
• Stay up-to-date with fraud detection and ML trends and apply them to our stack.
• Communicate results to technical and non-technical stakeholders to drive decisions.
• Ensure data privacy and security compliance in all analyses.

Skills

Python

Cloud platforms

ML algorithms

Spark

Dask

Data analysis

Communication

Independence + teamwork

Tools

Apache Spark

Dask

AWS

GCP

Azure

Job description

Oscilar is seeking a Data Scientist to build and deploy advanced fraud detection models. As an early ML team member you’ll shape our stack and help protect customers’ businesses from fraud across the platform.

You will collaborate with engineering, product, and operations; analyze large datasets with Spark and Dask; evaluate models; and communicate results to stakeholders while upholding data privacy.