Applied Science Manager, Marketplace

DAT•Seattle, WA

4h•$208,000 - $275,790•Hybrid

About The Position

DAT Freight & Analytics is seeking an Applied Science Manager to lead its Marketplace & Decision Optimization team. The team builds and calibrates decision systems that influence carrier load discovery, broker-carrier negotiations, and marketplace clearing. The manager will lead work on carrier engagement recommendations, auction design, and automated negotiation, while expanding the team’s remit into business decision optimization for subscription monetization and growth. This is a player-coach role: the manager will hire, coach, and develop applied scientists; set technical direction and priorities; and remain hands-on with models, experiments, and analyses. The role will bring causal inference and rigorous experimentation to commercial decisions across DAT’s transactional marketplace and subscription businesses, and raise experimentation standards across the organization. The manager will work closely with product, engineering, design, finance, and go-to-market leaders to turn evidence into decisions about what DAT builds, prices, packages, and launches.

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 proficiency in SQL and Python, with 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 this.
Fluency with AI-assisted development and the judgment to verify AI-generated work.
Experience with two-sided marketplaces, auctions, pricing, or recommendation systems where multi-agent interactions shape outcomes.
Track record of 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 raising an organization’s experimentation standards by building guidelines, review processes, or an 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 changes in speed and quality.
Experience in freight, logistics, or transportation technology.

Responsibilities

Lead, hire, coach, and grow the Marketplace & Decision Optimization team of applied scientists, setting the technical bar and roadmap.
Stay hands-on with models, experiments, and analyses on the team’s highest-impact problems.
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, upsells, and cross-sells.
Design and analyze experiments for a two-sided marketplace, using switchback, cluster-randomized, and quasi-experimental methods when standard randomization is constrained.
Quantify heterogeneous and long-term effects of interventions.
Define reusable experimentation standards for design, power analysis, guardrail metrics, and readouts, and partner with the ML and Experimentation Platform team to make them standard practice.
Raise experimentation quality across DAT by advising teams on rollout plans, tests, and interpretation of results.
Partner with product, engineering, design, finance, and go-to-market leaders to guide decisions about products, pricing, packaging, and launches.
Use AI tools to accelerate scientific work while validating their output and maintaining analytical rigor.