Research Scientist II
Research Scientist II building and improving fraud risk models and scam detection systems using audio, behavioral, and metadata signals. Requires an advanced degree and 3+ years of applied ML experience with Python and modern ML frameworks.
About the job
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.
Skills
Python, PyTorch, TensorFlow, Keras, Machine Learning, LLMs, Natural Language Processing, Fraud Detection, Risk Modeling, Speech Processing, Signal Processing, Anomaly Detection
Similar jobs
ML Engineering jobs
Office Hours
San Francisco, CABuild reproducible systems for AI model benchmarking, including datasets, evaluation pipelines, containerized environments, scoreboards, and analysis tools. The role requires 4+ years of professional engineering experience, strong Python, dataset rigor, and Docker expertise.
Applied Intuition
Sunnyvale, CA
Develop and deploy ML-first behavior prediction and planning systems for autonomous vehicles, forecasting the motion and interactions of road users. Requires a bachelor's degree, deep learning lifecycle expertise, and at least three years of production software experience with C++ or Python.
LangChain
New York, NYBuild and teach reliable AI agent systems through customer workshops, technical content, guidance, and reference implementations. The role requires strong Python and agent-development experience plus a background delivering customer-facing technical training.
Roboflow
San Francisco, CA
Build reinforcement-learning environments, evaluations, datasets, and scalable infrastructure for frontier AI capabilities. The role suits a high-agency generalist engineer with experience in agents, evaluations, or RL workflows and strong communication skills.
Beacon Biosignals
Boston, MADevelop and productionize machine- and deep-learning algorithms for biosignal and EEG data used in medical devices, clinical development, and diagnostics. The role requires 4+ years of industry experience, DSP and statistics expertise, PyTorch proficiency, and familiarity with regulated environments and production ML practices.