Senior Applied Scientist
Design and productionize ML risk models and decision systems to detect fraud, abuse, and unsafe behavior across a freight marketplace. Requires 5+ years building production ML systems and a quantitative MS/PhD.
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.
Skills and Experience You’ll Bring
- 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, with strong proficiency in Python and modern ML tooling and 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, with strong communication and collaboration skills to work effectively with technical and non-technical partners and bring models into production.
- 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.
Bonus Skills
- Worked on 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.
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