Research Scientist II

Responsibilities

• Build and improve fraud risk models and scoring systems using audio, behavioral, and metadata-based signals.
• Analyze fraud patterns across customer environments and translate findings into measurable improvements in model performance, investigation workflows, or mitigation strategies.
• Research and build a scam detection stack, from conception to realization.
• Partner with engineering and cross-functional teams to move successful research into production and improve fraud outcomes in live environments.
• Support high-priority fraud investigations by analyzing system behavior, fraudster attack patterns, and detection gaps, then recommending practical next steps.
• Improve the quality and precision of fraud-related identity signals, including voice-based indicators and repeat-offender detection.
• Design and maintain reproducible research workflows, internal tools, and evaluation pipelines.
• Contribute to technical reviews, knowledge sharing, and research documentation.
• Contribute to adjacent innovation areas, including emerging AI-assisted fraud-analysis workflows.

Requirements

• Advanced Degree (Master’s or PhD) in Computer Science, Mathematics, Statistics, Engineering, Artificial Intelligence, or a related quantitative field, or equivalent applied research experience.
• 3+ years of professional experience in machine learning, large-language models, fraud detection, natural language processing, risk modeling, speech or signal processing, anomaly detection, or a closely related domain.
• Strong Python skills and experience building research tooling, experimentation frameworks, or model evaluation workflows.
• Hands-on experience with modern machine learning frameworks such as PyTorch, TensorFlow, or Keras.
• A track record of translating research findings into practical improvements, whether in models, decision systems, or production-facing recommendations.
• Foundational knowledge of fraud, identity, consumer scams, authentication, risk scoring, or customer security concepts.

Nice-to-Haves

• Experience working on fraud or scam detection in voice, IVR, contact center, authentication, or adjacent trust and safety environments.
• Experience working on building and/or fine-tuning multi-modal foundation models.
• Experience improving precision and recall in real-world detection systems, including thresholding, scoring, watchlists, or entity-resolution style signals.
• Familiarity with metadata-driven risk signals such as telephony, carrier, device, account, or behavioral indicators.
• Experience with sequence modeling, event-based risk modeling, or other approaches used to detect evolving attack behavior.
• Familiarity with LLM-enabled research workflows, retrieval systems, or observability tools used to support analyst or fraud-investigation productivity.
• Working knowledge of C/C++, Go, or other production-oriented languages.