Staff Applied Scientist, Marketplace (Canada-Only)

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. Every day in every county of the U.S., people turn to Thumbtack to complete urgent repairs, seasonal maintenance and bigger improvements. We help homeowners know which projects to do, when to do them and who to hire from our growing community of 300,000 local service businesses. If making an impact inspires you, join us. Imagine what we’ll build together.

About the Applied Science Team

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, 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.

The challenge

As a Staff Applied Scientist on our New Hiring Experiences team, you will lead the technical direction for the matching and recommendation systems that connect millions of homeowners with the right service professionals. This is a staff IC role expected to influence and effectively partner with engineering and product leaders across retrieval, ranking, allocation, and pricing systems and teams: the core marketplace mechanics that determine whether the right customer finds the right pro at the right price.

You’ll help define the applied science technical roadmap for matching and recommendations, architect production ML systems that serve millions of daily matches, and partner with engineering, product, and economics leaders to evolve the marketplace. You’ll mentor senior applied scientists, raise the bar on technical rigor across the org, and be a voice on how agentic and LLM-powered experiences reshape what a marketplace can do for customers and pros.

What you’ll do

• Set and drive the multi-year technical roadmap for matching, ranking, and recommendation systems across the marketplace.

• Architect, build, and deploy production AI systems that serve millions of matches per week, including retrieval, ranking, allocation, and pricing components.

• Design and execute marketplace experiments at scale; collaborate with economists and data scientists on causal inference and counterfactual reasoning when randomized testing is constrained.

• Lead end-to-end on ambiguous, high-impact problems where the science approach, success metrics, and product framing all need to be defined.

• Provide technical mentorship to senior applied scientists; raise the technical bar through design reviews, paper readings, and applied science guild contributions.

• Partner with product and engineering leadership to translate marketplace strategy into a science investment plan, and to communicate technical tradeoffs to executive stakeholders.

• Represent Thumbtack externally where appropriate: industry-track papers, academic collaborations, recruiting talks.

In order to be successful, you must bring

• Ph.D. in a quantitative field (Computer Science, Machine Learning, Statistics, Operations Research, Economics, or related) or equivalent industry experience, plus 8+ years as an applied scientist with end-to-end ownership of production marketplace, recommendation, or ranking systems.

• Architected at least one production marketplace, recommendation, or recsys system from problem framing through deployment and ongoing operation, cross-cutting at least three of: retrieval, ranking, allocation, pricing, evaluation.

• Advanced knowledge of machine learning techniques (deep learning, learning-to-rank, embeddings and similarity-based approaches, sequence models, causal inference) particularly as applied to matching, recommendation, or marketplace problems; working experience with modern LLMs (OpenAI, Anthropic Claude, Gemini, AWS Bedrock, HuggingFace), the agentic tools built on top of them, and agentic AI development practices.

• Advanced knowledge of probability and statistics, including experimental design, predictive modeling, optimization, and causal inference.

• Strong coding ability in Python, SQL, and Go, with experience building on large-scale distributed systems (Spark, Kafka, real-time inference platforms).

• Track record of external technical contributions: industry publications, patents, or open-source work in recommendation systems, ranking, marketplace mechanics, or related applied ML domains (e.g., RecSys, SIGIR, KDD, WWW, NeurIPS industry tracks).

• Demonstrated ability to drive impactful technical roadmaps and lead seamless execution across multiple teams, mentor senior applied scientists, and communicate clearly to cross-functional partners across executive, product, engineering, and economics audiences.

Expected salary ranges

• For candidates living in Ontario and British Columbia, the expected salary range for the role is currently $232,100 - $300,300.

Actual offered salaries will vary and will be based on various factors, such as calibrated job level, qualifications, skills, competencies, and proficiency for the role.

Note: Thumbtack uses AI tools to support our resume screening process. However, our Recruiting team’s expertise and judgment guide hiring decisions.

Actual offered salaries will vary and will be based on various factors, such as calibrated job level, qualifications, skills, competencies, and proficiency for the role.

Thumbtack embraces diversity. We are proud to be an equal opportunity workplace and do not discriminate on the basis of sex, race, color, age, pregnancy, sexual orientation, gender identity or expression, religion, national origin, ancestry, citizenship, marital status, military or veteran status, genetic information, disability status, or any other characteristic protected by federal, provincial, state, or local law. We also will consider for employment qualified applicants with arrest and conviction records, consistent with applicable law.

Thumbtack is committed to working with and providing reasonable accommodation to individuals with disabilities. If you would like to request a reasonable accommodation for a medical condition or disability during any part of the application process, please contact: [email protected].

If you are a California resident, please review information regarding your rights under California privacy laws contained in Thumbtack’s Privacy policy available at https://www.thumbtack.com/privacy/.

We put as much craftsmanship into candidate safety as we do into the hiring experience itself. While scammers may try to impersonate our team, we’ll never ask you for money, banking info, or SSNs during hiring. Check out our blueprint on how to spot the fakes.