Senior Data Scientist - Fraud Data Infrastructure & Automation in United States at Jobgether

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Senior Data Scientist – Fraud Data Infrastructure & Automation in United States.

This role sits at the core of building and improving advanced fraud detection and identity verification systems through data science and machine learning. You will transform large, complex, and often adversarial datasets into actionable insights that directly shape fraud prevention and identity trust decisions. Operating in a high-impact, fast-moving environment, you will design scalable data pipelines, develop and optimize models, and apply emerging AI techniques to automate and accelerate fraud analytics. The position blends hands-on technical work with strategic problem-solving, requiring close collaboration with product, engineering, and risk teams. You will also explore innovative approaches such as LLMs and agentic AI to enhance data exploration and fraud detection workflows. This is a highly autonomous role where your work directly influences product performance and real-world risk decisions. You will join a team that values ownership, speed, and deep analytical rigor.

In this role, you will own end-to-end data science initiatives focused on fraud detection, identity verification, and scalable data infrastructure, ensuring both technical excellence and measurable business impact.

• Design, build, and maintain scalable data pipelines and workflows supporting analytics, fraud detection, and model development.
• Develop and deploy machine learning models using diverse data types such as tabular, text, images, and other structured/unstructured formats.
• Build and integrate agentic AI and LLM-based systems to automate data exploration, anomaly detection, and investigative workflows.
• Ensure data quality, integrity, and reliability through monitoring systems, validation frameworks, and anomaly detection mechanisms.
• Lead full ML and analytics lifecycle ownership, from problem definition through deployment and post-launch monitoring.
• Evaluate third-party data vendors and external datasets, designing experiments to assess quality, lift, and business value.
• Partner with Product, Engineering, and Risk teams to define requirements and deliver insights that shape fraud and identity strategy.
• Conduct advanced research into new data sources, algorithms, and fraud detection techniques.
• Communicate findings and recommendations to both technical and executive stakeholders with clarity and impact.
• Mentor peers and contribute to a culture of experimentation, learning, and high analytical standards.

The ideal candidate is a highly technical and impact-driven data scientist with strong experience in fraud, risk, or identity-related domains and a proven ability to deliver business outcomes from complex datasets.

• Master’s or PhD in a quantitative field (Computer Science, Statistics, Mathematics, Data Science) or equivalent experience.
• 5+ years of experience in data science, machine learning, or related roles in high-growth or fintech environments.
• Strong background in fraud prevention, risk modeling, or identity verification systems.
• Proven experience working with large-scale, messy, and real-world datasets to drive measurable impact.
• Proficiency in Python and SQL, with experience using ML frameworks such as PyTorch, TensorFlow, or scikit-learn.
• Deep understanding of ML techniques, model evaluation metrics, and data pipeline development.
• Experience building distributed data workflows using tools such as Spark, Airflow, or Databricks.
• Familiarity with evaluating third-party data sources and designing data quality and lift experiments.
• Experience with LLMs and agentic AI frameworks (e.g., LangChain, LangGraph, Ray) is highly preferred.
• Strong communication skills with the ability to translate complex technical findings into business insights.
• Ability to lead technical initiatives, influence cross-functional teams, and work autonomously.

• Opportunity to work on high-impact fraud detection and identity infrastructure challenges
• Remote-friendly work within approved US metro locations
• High ownership role with end-to-end responsibility for data science initiatives
• Exposure to advanced AI, LLMs, and agentic AI applications in production systems
• Collaborative environment with strong focus on innovation and technical excellence
• Opportunity to influence core product and risk decisioning systems
• Competitive compensation aligned with senior-level data science roles
• Work on large-scale, real-world datasets with direct business impact
• Strong culture of continuous learning, experimentation, and professional growth