Senior Applied Scientist, Trust & Safety

About DAT

DAT Freight & Analytics is an award-winning employer of choice and a next-generation SaaS technology company that has been at the leading edge of freight and logistics innovation for nearly five decades. Founded in 1978, DAT operates the largest freight marketplace in North America - processing 250 million+ load posts annually and maintaining one of the largest repositories of freight market transaction data in the world.

On a defined path to $1 billion in revenue, DAT deploys a suite of software solutions, machine learning models, and intelligent automation tools that help brokers, carriers, and shippers price freight accurately, source capacity, reduce risk, and operate more efficiently. With nearly 700 teammates across offices in Denver, CO;
Portland, OR;
Seattle, WA;
Springfield, MO;
Toronto, ON; and Bangalore, India, DAT combines the credibility of a multi-decade market leader with the drive of a company that is not done disrupting the industry it helped build. For more information, visit

Job Final date to receive applications: 06/30/2026

The Opportunity

DAT's Trust and Safety Science team is seeking a Senior Applied Scientist to design and deploy the next generation of risk models and intelligent decision systems that help detect, prevent, and mitigate unsafe, fraudulent, or otherwise harmful behavior across our network. This role sits at the intersection of machine learning, risk decisioning, and product development, with a focus on building systems that protect customers and the marketplace while preserving healthy marketplace activity.

You will work on some of the most important trust and safety problems in digital logistics, including onboarding risk, behavioral risk detection, fraud and abuse detection, account integrity, network-graph risk modeling, and continuous monitoring throughout the customer lifecycle.

This is a hands-on, end-to-end science role where you will:

• 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.

You will be joining at a pivotal point in DAT's transformation as we automate more of the freight lifecycle and build the safest, most efficient automated marketplace in the freight industry. DAT has also accumulated a uniquely rich set of behavioral, operational, and risk data across its platforms (Convoy Platform, Trucker Tools, Out Go, DAT), that enables a strong foundation for behavior-drift modeling, account and identity abuse detection, and broader threat detection systems.

A key part of the opportunity is extending Convoy Platform's industry-leading CARVE product across the broader DAT ecosystem and evolving them into customer-facing risk products for a wider set of DAT customers.

This is a deeply technical role focused on building and product ionizing high-recall risk models and decision systems for high-stakes compliance and trust workflows, where protecting customers, minimizing missed risk, and making decisions that are measurable, explainable, and operationally defensible all matter. Just as importantly, these systems must act like a scalpel rather than a sledgehammer: in a fair marketplace, we need to target true risk precisely, avoid unnecessary friction for legitimate participants, and make nuanced decisions that balance recall, precision, customer protection, and marketplace health.

What You'll Do

• Build and product ionize 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.

The 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…