Research Scientist II

Pindrop

12h•$160,000 - $185,000•Remote

About The Position

As a Research Scientist II on the Fraud Research team, you will help improve how Pindrop detects, scores, and investigates fraud and scams across voice and IVR interactions. You will work on applied machine learning problems that directly impact fraud and scam prevention for major enterprise customers, balancing core model development with real-world investigation and analysis. This is a high-impact opportunity to join Pindrop’s Research organization and work on fraud problems that matter in the real world. Your work will directly influence how we detect fraud, investigate suspicious behavior, and improve protection for major financial institutions and other enterprise customers. You’ll collaborate closely with strong technical peers across research and engineering, work on meaningful applied machine learning challenges, and help shape the next generation of fraud detection capabilities at Pindrop.

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.
Persistent, curious, and scientifically rigorous, especially when working through ambiguous data, noisy signals, or fast-evolving fraud behavior.
Comfortable owning research workstreams from problem definition through experimentation, analysis, and recommendation.
Ability to communicate clearly with both technical and non-technical partners, and explain tradeoffs, assumptions, and results in a practical way.
Care deeply about reproducibility, documentation, and building research that can stand up in real production settings.
Motivated by high-impact security and fraud problems and want your work to influence real customer outcomes.

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.

Responsibilities

Build and improve fraud risk models and scoring systems using a combination of 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 for our customers.
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 that help the team experiment efficiently and measure impact clearly.
Contribute to technical reviews, knowledge sharing, and research documentation that helps the broader organization understand and apply your work.
Contribute to adjacent innovation areas, including emerging AI-assisted fraud-analysis workflows, when relevant to team priorities.

Benefits

Competitive compensation package, including RSUs (Restricted Stock Units) for all employees
Remote-first environment
Unlimited Paid Time Off (PTO)
Generous health and welfare plans to choose from - including one employer-paid “employee-only” plan!
Best-in-class Health Savings Account (HSA) employer contribution
Low-cost vision and dental plans for you and your family
Paid Parental Leave - Including birth, adoptive & foster parents
One year of diaper delivery for your newest addition to the family!
Recurring monthly phone and internet allowance
Enhanced fertility and GLP-1 benefits
Annual Learning & Development stipend