Staff Applied Scientist, Monetization

About the Applied Science Team

We're looking for applied scientists with deep expertise in machine learning, optimization, building data products, and/or statistics. 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 machine learning, statistics, and computer science:

• Build a healthy marketplace. We evolve and manage the monetization mechanics of our marketplace, including defining the parameters that affect the prices we charge.
• 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.
• 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.
• 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.

Challenge 

We are looking for someone with deep expertise in applying scientific techniques to marketplace monetization. We need to maintain and grow a marketplace that brings homeowners and service professionals together, where the skills of professionals match with the project needs of homeowners, and where pricing and scheduling align to make these successful jobs done.

Responsibilities

• Initiate and drive applied science initiatives to completion, with a focus on the business impact of those projects
• Architect and deploy machine learning systems and algorithms to production
• Design and execute experiments, collect and analyze data to characterize our product and marketing
• Analyze a wide variety of data: structured and unstructured, observational and experimental
• Collaborate with engineers, data scientists, and economists to use sound statistical practices
• Maintain the right balance between speed of execution and scientific rigor when designing solutions
• Technical mentorship of other applied scientists

What you’ll need

If you don't think you meet all of the criteria below but still are interested in the job, please apply. Nobody checks every box, and we're looking for someone excited to join the team.

• Expert knowledge of machine learning techniques
• Experience building algorithms to serve complex human settings with dynamic behavior adaptation
• Experience in auctions and price theory
• Ability to effectively read, write, and debug code in programming languages such as Python
• Good knowledge of probability and statistics, including experimental design, optimization, and causal inference
• 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

Bonus points if you have

• Experience with NLP techniques, including familiarity with modern LLM designs and tools
• Demonstrated ability to create and drive technical and impactful roadmaps for the business, and lead seamless execution
• Expert knowledge of probability and statistics, including experimental design, predictive modeling, optimization, and causal inference
• Expert knowledge of auctions and price theory
• Experience with large-scale distributed systems
• Ph.D. in a relevant field

Thumbtack is a virtual-first company, meaning you can live and work from any one of our approved locations across the United States, Canada or the Philippines. Learn more about our virtual-first working model here. 

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