Applied Science Manager, Marketplace

DAT•Seattle, WA

7d•$208,000 - $275,790•Hybrid

About The Position

DAT is seeking an Applied Science Manager to lead its Marketplace & Decision Optimization team. The team develops and calibrates decision systems that influence how carriers discover loads, how brokers and carriers negotiate, and how DAT’s marketplace operates. The manager will lead applied scientists working on carrier-engagement recommendations, auction design, and automated offer negotiation, while expanding the team’s work into business decision optimization for subscription monetization, pricing, packaging, and growth. This is a player-coach role: the manager will hire, coach, and develop scientists, set the technical roadmap and standards, and remain hands-on with models, experiments, and analyses. The role will bring causal inference and rigorous experimentation to decisions across DAT’s transactional marketplace and subscription businesses, and will raise the quality of experimentation across the organization. The manager will collaborate with product, engineering, design, finance, and go-to-market leaders to turn evidence into decisions about what DAT builds, prices, packages, and launches. DAT describes a hybrid work environment. The company offers a competitive salary and benefits package, and Washington-based candidates are given a salary range of $208,000 to $275,790 plus target bonus. The application deadline is November 30, 2026.

Requirements

Ph.D. or master’s degree in Statistics, Economics, Econometrics, Computer Science, Operations Research, Applied Mathematics, or a related quantitative field.
At least 6 years of industry experience in applied science or data science, or at least 4 years with a Ph.D.
At least 2 years of direct experience managing scientists.
Strong foundation in experimental design and causal inference, including power analysis, variance reduction, sequential testing, and quasi-experimental methods.
Strong SQL and Python proficiency and the technical depth to review model and experiment designs, challenge assumptions, and earn the trust of a highly technical team.
Track record of rigorous, decision-shaping analysis; production machine-learning experience is not required in place of such analysis.
Fluency with AI-assisted development and the judgment to verify AI-generated work.
Experience with two-sided marketplaces, auctions, pricing, or recommendation systems in settings where multi-agent interactions shape outcomes.
Experience hiring, developing, and retaining scientists, setting technical direction, and managing performance.
Demonstrated ability to influence cross-functional decision-making and explain nuanced statistical tradeoffs to senior technical and non-technical leaders.

Nice To Haves

Experience establishing experimentation standards, review processes, or an organization-wide experimentation culture.
Production experience applying bandits, reinforcement learning, or adaptive experimentation.
Experience with subscription or SaaS monetization, including pricing, packaging, willingness to pay, or upgrade and retention modeling.
Experience redesigning how a science team works with AI tools, with evidence of resulting changes in speed and quality.
Experience in freight, logistics, or transportation technology.

Responsibilities

Lead, hire, manage, and develop a team of applied scientists, including setting the technical bar and roadmap.
Stay hands-on with the team’s highest-impact models, experiments, and analyses; review modeling choices and take on challenging technical problems as needed.
Advance carrier-engagement recommendation systems, auction design, and automated negotiation models to improve marketplace liquidity, match quality, and economic outcomes.
Build science capabilities for subscription monetization and growth, including price elasticity, willingness to pay, tier adoption, upgrades, upselling, and cross-selling.
Design and analyze experiments for a two-sided marketplace, using approaches such as switchback, cluster-randomized, and quasi-experimental designs when standard randomization is constrained.
Quantify heterogeneous and long-term effects of interventions.
Establish reusable standards for experiment design, power analysis, guardrail metrics, and readouts, and partner with the ML and Experimentation Platform team to make them broadly available.
Partner with product, engineering, design, finance, and go-to-market leaders to inform decisions about products, pricing, packaging, and launches.
Promote rigorous experimentation and improve how teams design rollouts, run tests, and interpret results.
Use AI tools to accelerate scientific work while applying judgment to verify outputs and maintain rigor.
Communicate statistical and technical tradeoffs clearly, influence cross-functional partners, and constructively challenge assumptions.