The ad says“Familiarity with modern LLMs (OpenAI, Anthropic Claude, Gemini, AWS Bedrock) and experience with agentic AI development practices and tools”
The ad says“Solid knowledge of machine learning techniques such as classification, regression, embedding-based approaches, and causal inference”
The ad says“Solid knowledge of machine learning techniques such as classification, regression, embedding-based approaches, and causal inference”
The ad says“Experience with large-scale distributed systems (Spark, Databricks, or similar)”
The ad says“Good knowledge of probability, statistics, and econometric methods, including experimental design, causal inference, and optimization”
The ad says“Solid knowledge of machine learning techniques such as classification, regression, embedding-based approaches, and causal inference”
The ad says“Good knowledge of probability, statistics, and econometric methods, including experimental design, causal inference, and optimization”
The ad says“Familiarity with modern LLMs (OpenAI, Anthropic Claude, Gemini, AWS Bedrock) and experience with agentic AI development practices and tools”
The ad says“We’re looking for applied scientists with deep expertise in machine learning, optimization, building data products, and/or statistical models”
The ad says“We’re looking for applied scientists with deep expertise in machine learning, optimization, building data products, and/or statistical models”
The ad says“Good knowledge of probability, statistics, and econometric methods, including experimental design, causal inference, and optimization”
The ad says“Ability to effectively read, write, and debug code in programming languages such as Python and SQL”
The ad says“Solid knowledge of machine learning techniques such as classification, regression, embedding-based approaches, and causal inference”
The ad says“Experience with large-scale distributed systems (Spark, Databricks, or similar)”
The ad says“Ability to effectively read, write, and debug code in programming languages such as Python and SQL”
The ad says“We’re looking for applied scientists with deep expertise in machine learning, optimization, building data products, and/or statistical models”
The ad says“Good knowledge of probability, statistics, and econometric methods, including experimental design, causal inference, and optimization”
As part of a small product team you will have full ownership over your domain, so you should be a person who dreams big, then executes well
At Thumbtack, the Applied Science team is responsible for a wide variety of problems spanning AI, machine learning, statistics, and computer science:
Improve customer and service provider matching. Matching and optimization algorithms are fundamental to Thumbtack’s product: we now service millions of matches per week. Identifying better matches between customers and service providers has an incredible impact on the experience of customers and professionals transacting on our platform
Model complex relationships in the presence of many confounding factors. Predictive modeling problems are everywhere across our product. Our team works to scope, design and implement machine learning models to support Thumbtack’s product and marketing
Characterize marketplace dynamics. Thumbtack’s marketplaces consist of thousands of active markets across our service categories and U.S. cities. Via exploratory data analysis and experimental design, our team works to understand trends and behaviors within these markets
Build AI features. Use state-of-the-art approaches such as large language models (LLMs), reinforcement learning and agentic workflows to enhance customer / pro experience, increase acquisition and marketplace efficiency, and to automate and enhance internal processes
Build a healthy marketplace. We evolve and manage the monetization mechanics of our marketplace, including defining the parameters that affect the prices we charge
Thumbtack constantly works to balance supply and demand across thousands of local markets and hundreds of service categories
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
As an Applied Scientist on Pro Growth, you’ll build models and experiments that shape who joins Thumbtack, how they get activated, how we forecast supply needs by market and trade, and how we accelerate time-to-value for new pros
We leverage machine learning, causal inference, and optimization to strategically grow and shape our pro supply base
Our work directly contributes to meeting demand projections, unlocking strategic partnerships, and ultimately enhancing customer experience and platform growth by ensuring a robust and high-quality supply of pros
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
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
Benefits
Parental leave
Health insurance
Community matters | Sure, our ways of connecting have changed. But we haven’t missed a beat, from virtual concerts to “TTversary” celebrations to weekly catch-ups
Always be improving | We bring out the best in our colleagues — and our product — by pushing each beyond what we believe we’re capable of. We give honest feedback, even when it’s hard
Wellness is a way of life | We promote balance with generous PTO and company-wide holidays — and find our centers every Wellness Wednesday with yoga featuring a Thumbtack pro
A worthy mission | In a world where tech is often seen as eroding good jobs, we’re helping small business owners thrive. Our product makes entrepreneurship accessible to everyone
Driven by our values | Our values are the compass by which we make all our decisions. Each one matters: Lead with why, Make it count, Own it, Choose teamwork, and Say what you mean
Plus, amazing benefits | All the benefits you’d expect, plus a few more: generous parental and fertility benefits, $1800 for Thumbtack services per year and a $1200 remote work stipend
Virtual-first working model coupled with quarterly in-person events and Camp Thumbtack
20+ company-wide holidays including two week-long shutdowns
Libraries (collaborative workspaces) in San Francisco, Salt Lake City, Toronto, and Manila
Cell phone and WiFi reimbursements
Subscriptions and Employee Assistance Program for mental health and well-being
Preferred experience
We’re looking for applied scientists with deep expertise in machine learning, optimization, building data products, and/or statistical models
Solid knowledge of machine learning techniques such as classification, regression, embedding-based approaches, and causal inference
Good knowledge of probability, statistics, and econometric methods, including experimental design, causal inference, and optimization
Ability to break down complex problems rigorously and understand the tradeoffs necessary to deliver impactful projects
Demonstrated ability to drive seamless execution end-to-end on at least one production ML project
Experience in applied science within a marketplace, supply chain, or growth domain
Familiarity with modern LLMs (OpenAI, Anthropic Claude, Gemini, AWS Bedrock) and experience with agentic AI development practices and tools
Experience with large-scale distributed systems (Spark, Databricks, or similar)
3+ years of industry experience as an applied scientist, data scientist, or ML engineer with ownership of production ML models
Ability to communicate clearly and effectively to cross-functional partners of various technical levels
Ability to effectively read, write, and debug code in programming languages such as Python and SQL
Master’s degree in a quantitative field (Computer Science, Machine Learning, Statistics, Operations Research, Economics, or related), or equivalent industry experience
