About the Role
DAT’s Convoy Platform team is seeking a Staff Applied Scientist to design and deploy the next generation of marketplace algorithms and intelligent decision systems that power the movement of freight across the nation. You will tackle some of the most challenging problems in digital logistics: matching supply with demand at scale, allocating resources efficiently under uncertainty, and building systems that can make and refine complex, high-judgment decisions continuously as new information arrives.
This is a hands-on, end-to-end science role where you will:
- Conceptualize, propose, implement and iterate on algorithms that optimize a dynamic two-sided marketplace in real time
- Build decision engines that learn from feedback and guide operational and product outcomes
- Blend techniques from machine learning, optimization, and causal inference to deliver measurable marketplace improvements
- Take ideas from research to production, ensuring seamless integration into our operational systems
You will be joining at a pivotal point in DAT’s transformation, bringing together Convoy’s industry-leading technology with DAT’s scale and deep market presence to create the most advanced digital freight network in the country. The solutions you build will help move billions of dollars of freight more efficiently, reducing waste and increasing value for shippers, brokers, and carriers.
Responsibilities
As a Staff Applied Scientist, you will set technical direction, mentor other scientists, and deliver solutions whose impact scales across teams and the broader Convoy Platform, not just within individual projects.
- Drive Market Mechanism Innovation: Enhance our industry-leading auction mechanism to better match broker loads with carriers, tackling dynamic pricing, marketplace fairness, and efficiency at scale
- Develop AI-Powered Customer Experiences: Build and deploy next-generation AI products, including LLM-driven text and voice interfaces, enabling customers to interact more naturally through chat, voice assistants, and automated workflows
- Build Risk & Trust Systems: Create robust fraud detection and risk management models to maintain the safety and integrity of our network
- Design Recommendation Systems: Deliver intelligent load recommendations, using Convoy and DAT’s rich user behavior data. Test, learn and iterate with multi-armed bandits.
- Embrace our Experimentation and Causal Inference Obsession: Design and analyze experiments to accurately measure the impact of new features and pricing strategies across our marketplace
- Set Technical Direction: Mentor other scientists and deliver solutions whose impact scales across teams and the broader Convoy Platform
- Collaborate Cross-Functionally: Lead successful collaborations with product managers, engineers, and data scientists to ensure seamless integration of modeling solutions within engineering systems
- Stay Current with Innovation: Maintain expertise in the latest advancements in machine learning, optimization techniques, and market dynamics to drive innovation and maintain competitive edge
- Drive smarter, data-informed decision-making through advanced Science features.
- Develop feedback-aware automated decision-making systems.
- Engage collaboratively with cross-functional teams.
- Innovate Deliberately by staying up-to-date with the latest advancements.
Requirements
- Advanced Education: Ph.D. or MS in Computer Science, Statistics, Applied Mathematics/Operations Research, Engineering, or related quantitative field
- Professional Experience: Minimum of 7+ years of experience developing and deploying machine learning solutions in production environments
- Technical Proficiency: Expert-level proficiency with Python and production-grade machine learning frameworks (PyTorch, TensorFlow, scikit-learn), with hands-on experience in large language models (LLMs), retrieval-augmented generation (RAG), and modern natural language processing (NLP) toolkits
- Systems Experience: Demonstrated ability to build, deploy, and monitor both classic and generative AI models in real-world systems, with comfort working with APIs, microservices, and data pipelines
- Problem-Solving Aptitude: Proven track record of translating ambiguous business problems into automated, scalable decision-making tools with a bias for action and operational excellence
Nice to have
- Experience with two-sided marketplaces, financial markets, and economic theory
- Built and maintained mature production ML models in large, complex systems
- Applied foundational or cutting-edge research, especially in NLP, LLMs, reinforcement learning, or optimization, to unlock real-world business value
- Experience in freight, logistics, or transportation technology