Lead ML Scientist, Pricing

ABOUT THE ROLE AND THE TEAM: 

The Knot Worldwide is seeking a Lead ML Scientist, Pricing, to architect the economic engine of our global wedding marketplace. In this role, you will move beyond traditional predictive analytics to build structural models of supply and demand, design incentive-compatible auction mechanisms, and simulate counterfactual market scenarios. You will work at the intersection of Microeconomics and Machine Learning to ensure that our pricing strategies optimize value for millions of couples and hundreds of thousands of vendors. As a strategic leader, you will partner with the VP of Monetization to translate complex economic insights into actionable business strategies.

RESPONSIBILITIES: 

• Strategic Leadership: Partnering with the VP of monetization to translate complex economic insights into actionable business and pricing strategies.
• Structural Demand Modeling: Researching, developing, and maintaining hierarchical Bayesian models to estimate price elasticities across heterogeneous vendor segments and utilize these models to inform dynamic pricing and subscription tiering decisions.
• Auction Mechanism Design: Designing and optimizing the bidding algorithms for our vendor advertising platform and simulating auction formats to maximize revenue and ecosystem health.

Marketplace Simulation: Building an “Auction Gym” (agent-based model) to stress-test pricing policies and understand complex feedback loops before production deployment.

• Causal Inference & Experimentation: Designing rigorous market-level experiments (Switchback, Synthetic Control) to measure the true incremental impact of pricing interventions.

Vendor Supply Estimation: Using dynamic discrete choice models to estimate vendor opportunity costs and labor supply elasticity

SUCCESSFUL LEAD MACHINE LEARNING SCIENTISTS HAVE: 

• A PhD or Master's Degree in Economics (Industrial Organization), quantitative marketing, operations research, or computer science (algorithmic game theory).
• A minimum of 3+ years of experience in a two-sided marketplace or an economic consulting firm.
• Demonstrated experience and deep understanding of structural estimation, mechanism design, and causal machine learning.
• Experience in designing and implementing auction-based pricing models
• Expert proficiency in Python or R; experience with causal inference libraries (EconML, CausalML) and optimization solvers.
• A collaborative attitude that fosters a culture of data-driven decision making across the organization.
• An innovative spirit that thrives on exploring new ideas and approaches to solve challenging problems.

WORK MODEL:

This role is Together@TKWW-eligible and based near one of our office hubs. You’ll be expected to work in the office two days a week as part of our hybrid work model.

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