Senior Applied Scientist

What You’ll Do

• Build and productionize fraud, safety, and risk systems for high-recall decisioning, with controls that preserve precision, fairness, and explainability in high-stakes workflows.
• Design graph, network-link analysis, entity-resolution, and anomaly-detection algorithms that identify hidden relationships, behavioral drift, account abuse, and emerging threat patterns across users, carriers, digital fingerprints and physical assets.
• Develop continuous risk monitoring, alerting, and policy decisioning across onboarding, booking, and load execution, combining ML models, heuristics, feedback loops, and human-in-the-loop review where appropriate.
• Move proactively and with urgency against evolving fraud patterns, rapidly iterating on approaches while building scalable, adaptable detection and decisioning systems rather than brittle one-off patches or manual hacks.

Responsibilities

• Conceptualize, propose, implement, and iterate on models and algorithms for fraud detection, risk scoring, and trust and safety decisioning.
• Build decision engines that learn from feedback and support actions such as step-up verification, review prioritization, and automated access controls.
• Apply machine learning, graph and network algorithms, anomaly detection, and other quantitative methods to deliver measurable improvements in fraud prevention and operational effectiveness.
• Take ideas from research to production, ensuring the solutions you build integrate cleanly into operational and product systems.
• Own a model, service, API, or pipeline end-to-end, including quality, monitoring, iteration, and cross-functional coordination.

Requirements

• PhD or MS in Computer Science, Statistics, Applied Mathematics, Operations Research, Engineering, or another quantitative field.
• 5+ years of experience developing and deploying machine learning, statistical, or decisioning solutions in production environments.
• Strong proficiency in Python and modern ML tooling.
• Hands-on experience building reliable, production-quality data and model workflows.
• Ability to develop algorithmic solutions and decision systems while maintaining explainability, interpretability, and defensibility in high-stakes risk and compliance workflows.
• Experience owning a model, service, API, or pipeline end-to-end, including quality, monitoring, iteration, and cross-functional coordination.
• Strong communication and collaboration skills to work effectively with technical and non-technical partners.
• Demonstrated ability to frame ambiguous business problems as scalable automated decision systems and deliver practical solutions with measurable impact.
• Experience in one or more of the following areas: fraud detection, trust and safety, risk modeling, anomaly detection, rare-event modeling, identity or abuse detection, graph or network analysis, or related decision systems.

Nice-to-Haves

• Experience with systems that combine models, heuristics, human review, and operational workflows to make high-stakes decisions.
• Experience with two-sided marketplaces, pricing, financial markets, or economic systems.
• Experience in freight, logistics, transportation technology, or adjacent operational domains.

Compensation

For Washington-based candidates, the salary range for this role is $183,000.00 - $226,000.00 + target bonus.