# Senior Applied Scientist

**Company:** [DAT Freight & Analytics](https://hotfix.jobs/companies/dat-freight-analytics)
**Location:** Seattle, WA
**Role:** ML Engineering
**Salary:** $183k – $226k/yr
**Experience:** 5+ years
**Skills:** Machine Learning, Python, Fraud Detection, Risk Modeling, Anomaly Detection, Graph Analysis, Network Analysis, Entity Resolution, Decision Systems, Explainable Ai, Production Ml, Trust And Safety
**Posted:** 2026-07-15

> Senior Applied Scientist building and productionizing high-recall fraud detection, risk scoring, graph-based anomaly detection, and decision engines for Trust & Safety in a freight marketplace. Requires advanced degree, 5+ years production ML experience, and strong Python skills.

## Job Description

## 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.

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