Job Application Deadline: 11/30/2026
The OpportunityDAT's Science organization is seeking an Applied Science Manager to lead our Marketplace & Decision Optimization team. This team designs, builds, and calibrates the decision systems that shape how carriers discover loads, how brokers and carriers negotiate, and how DAT's marketplace clears. You will lead a group of talented applied scientists working on recommendation systems for carrier engagement, auction design, and automated offer negotiation. You will also help shape a new remit for the team: business decision optimization. DAT already grounds its most consequential commercial decisions in data, from pricing and packaging to product investment and growth strategy across our subscription and marketplace businesses. We want to bring Science deeper into those decisions, enriching them with causal framing and rigorous experimentation, and strengthening our ability to learn from every decision we make.
This is a player-coach role where you will:
- Lead, hire, and grow a team of applied scientists, setting the technical bar and the roadmap
- Stay hands-on, owning models, experiments, and analyses on the team's highest-leverage problems
- Bring experimentation and causal inference to decisions across both our transactional marketplace and our subscription business (DAT load boards, iQ, and beyond)
- Raise the experimentation bar across DAT, so product and engineering teams design better rollouts, run better tests, and make better decisions with the data they collect
You will be joining at a pivotal point in DAT's transformation, bringing together Convoy Platform's industry-leading marketplace technology with DAT's scale and deep market presence. The decisions your team shapes will move billions of dollars of freight and define how DAT grows.
You are someone who will- Lead scientists, and still enjoy doing the science: You have managed scientists and you enjoy it: hiring, coaching, giving direct feedback, and growing people into their next role. You also stay close to the work. You frame problems precisely, specify what needs to be built clearly enough for AI tools to execute it reliably, review modeling choices in depth, and take on hard technical problems yourself when that is the fastest path to impact.
- Rethink how science gets done: AI is changing what a scientist's day looks like, and you have a point of view on it. You use AI tools to move faster from question to evidence, and you stay rigorous about where they fall short. You think critically about which skills matter more as execution gets cheaper: problem framing, judgment, experimental design, and knowing what not to build. You bring an entrepreneurial bias to the team, deliberately trying new ways of working and keeping what earns its place.
- Raise the experimentation bar: You have changed how an organization learns before. You set standards for experiment design, metrics, and readouts, and you evangelize them with product, engineering and design leaders. Across DAT, people see you as the expert they go to before they decide on a rollout plan, launch an A/B test, or interpret a result.
- Elevate product decisions: You are a thought partner to product leaders, not a service queue. You help them frame the decision first, understand what good looks like, and see what science makes possible that they would not have asked for on their own.
- Influence across the organization: Marketplace work cuts across every product, engineering, and design team that touches brokers, carriers, and shippers. You build trust fast, communicate clearly with technical and non-technical audiences, push back constructively, and bring people along.
What You'll Do- Lead the Marketplace & Decision Optimization Team: Manage and grow a team of highly technical applied scientists. Set the technical roadmap, prioritize against business impact, and hire additional scientists as the remit expands.
- Drive Marketplace Mechanism Innovation: Lead the team behind advancing our carrier engagement recommendation systems, auction design, and automated negotiation models that improve liquidity, match quality, and economic outcomes for brokers and carriers
- Build Business Decision Optimization: Stand up science for subscription monetization and growth: price elasticity and willingness-to-pay, tier adoption and upgrade paths, upsell, cross-sell decisions, etc.
- Embrace our Experimentation and Causal Inference Obsession: Design and analyze experiments in a two-sided marketplace where standard A/B assumptions break down. Use switchback, cluster-randomized, and quasi-experimental designs when randomization is constrained, and quantify heterogeneous and long-term effects
- Set Experimentation Standards Across DAT: Define reusable standards for experiment design, power analysis, guardrail metrics, and readouts. Partner with our ML and Experimentation Platform team to make them the default for every team that ships
- Collaborate Cross-Functionally: Partner with product, engineering, design, finance, and go-to-market leaders to turn evidence into decisions on what DAT builds, prices, packages, and launches
The Skills and Experience You'll Bring- Advanced Education:D. or MS in Statistics, Economics, Econometrics, Computer Science, Operations Research, Applied Mathematics, or a related quantitative field
- Professional Experience: 6+ years of industry experience in applied science or data science (4+ with a Ph.D.), including 2+ years directly managing scientists
- Causal Inference and Experimentation Depth: Strong foundation in experimental design and causal inference, including power analysis, variance reduction, sequential testing, and quasi-experimental methods
- Technical Proficiency: Strong SQL and Python proficiency, and the technical depth to engage a highly technical team on the details: reviewing model and experiment designs, challenging assumptions, and earning scientists' trust. A track record of rigorous, decision-shaping analysis counts as much as production ML experience. Fluent in AI-assisted development, with the judgment to verify what AI tools produce
- Marketplace or Pricing Experience: Experience with two-sided marketplaces, auctions, pricing, or recommendation systems where multi-agent interactions shape outcomes
- People Leadership: Track record of hiring, developing, and retaining scientists, setting technical direction, and managing performance
- Influence and Communication: Demonstrated ability to change how a cross-functional organization makes decisions, and to explain nuanced statistical tradeoffs to senior technical and non-technical leaders
Bonus Skills - You have raised the experimentation bar at an organization before: built standards, review processes, or evangelizing an experimentation culture
- You have applied bandits, reinforcement learning, or adaptive experimentation in production
- You have worked on subscription or SaaS monetization: pricing, packaging, willingness-to-pay, or upgrade and retention modeling
- You have redesigned how a science team works around AI tools and can show what changed in speed and quality.
- You have experience in freight, logistics, or transportation technology
- Medical, Dental, Vision, Life, and AD&D insurance
- Parental Leave
- Flexible Vacation Time (FVT)
- An additional 10 holidays of paid time off per calendar year
- 401k matching (immediately vested)
- Employee Stock Purchase Plan
- Short- and Long-term disability sick leave
- Flexible Spending Accounts
- Health Savings Accounts
- Employee Assistance Program
- Additional programs - Employee Referral, Internal Recognition, and Wellness
- Free TriMet transit pass (Beaverton Office)
- Competitive salary and benefits package
- Work on impactful projects in a cutting-edge environment
- Collaborative and supportive team culture
- Opportunity to make a real difference in the trucking industry
- Employee Resource Groups
For Washington-based candidates, in compliance with the Washington State Pay Transparency Law, the salary range for this role is $208,000.00 - $275,790.00 + target bonus. DAT considers factors such as scope and responsibilities of the position, candidate's work experience, education and training, core skills, internal equity, and market and business elements when extending an offer.
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