Applied Scientist, Pro Growth

Thumbtack helps millions of people confidently care for their homes. Thumbtack is the one app you need to take care of and improve your home — from personalized guidance to AI tools and a best-in-class hiring experience. We're looking for applied scientists with deep expertise in machine learning, optimization, building data products, and/or statistical models. As part of a small product team you will have full ownership over your domain. The Pro Growth pod is responsible for the science behind acquiring and activating the right pros, at the right time, in the right markets, to keep the marketplace healthy and growing. You'll work shoulder-to-shoulder with engineers, product managers, and other applied scientists on a portfolio that ranges from incrementality and market forecasting to LLM-powered onboarding agents. You'll own end-to-end: from problem framing, through experiment design, through deploying models to production, through measuring business impact.

Responsibilities

Initiate and drive applied science projects to completion, with a focus on the business impact of those projects on pro acquisition, activation, retention, and supply health; Develop, deploy, and optimize machine learning models for supply-side problems, including: lead scoring and pro acquisition targeting, market-level supply forecasting, pro onboarding and activation, retention and churn prediction, and agentic onboarding workflows; Design and execute well-powered marketplace experiments to measure model and product impact on supply and business metrics; Analyze structured and unstructured data such as marketplace interactions, pro profiles, external supply signals, to identify trends, opportunities, and risks related to supply health; Collaborate closely with engineering, product management, business development, and marketing teams to define problems, develop end-to-end solutions, and ensure successful implementation and monitoring of ML systems in production; Maintain the right balance between speed of execution and scientific rigor when designing solutions for a fast-paced marketplace environment; Contribute to evolving the team’s agentic-first ways of working, including LLM-powered tooling, eval coverage, and the use of AI agents in day-to-day applied science.

Requirements

Master’s degree in a quantitative field (Computer Science, Machine Learning, Statistics, Operations Research, Economics, or related), or equivalent industry experience; 3+ years of industry experience as an applied scientist, data scientist, or ML engineer with ownership of production ML models; Experience in applied science within a marketplace, supply chain, or growth domain; Solid knowledge of machine learning techniques such as classification, regression, embedding-based approaches, and causal inference; Familiarity with modern LLMs (OpenAI, Anthropic Claude, Gemini, AWS Bedrock) and experience with agentic AI development practices and tools; Good knowledge of probability, statistics, and econometric methods, including experimental design, causal inference, and optimization; Experience with large-scale distributed systems (Spark, Databricks, or similar); Ability to effectively read, write, and debug code in programming languages such as Python and SQL; Ability to break down complex problems rigorously and understand the tradeoffs necessary to deliver impactful projects; Ability to communicate clearly and effectively to cross-functional partners of various technical levels; Demonstrated ability to drive seamless execution end-to-end on at least one production ML project.