Applied Scientist

Summary

Join Thumbtack's Applied Science team as an applied scientist specializing in machine learning and contribute to enhancing the trust and safety of our platform. You will leverage your expertise to detect and prevent fraud, improve customer-provider matching, and model complex marketplace relationships. This role involves designing and deploying machine learning systems, conducting experiments, and collaborating with cross-functional teams. The position requires deep expertise in machine learning techniques, strong coding skills, and excellent communication abilities. Thumbtack offers a virtual-first work model with various benefits and perks.

Requirements

• Expert knowledge of machine learning techniques, including classification, anomaly detection, and natural language processing (NLP)
• 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
• Demonstrated ability to create and drive technical and impactful roadmaps for the business, and lead seamless execution
• 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

Responsibilities

• Initiate and drive applied science initiatives to completion with a focus on improving the trust and safety of the Thumbtack platform. This includes identifying opportunities to leverage machine learning to enhance spam and fraud detection, policy enforcement, and overall platform security
• Architect and deploy machine learning systems to production
• Design and execute experiments to evaluate the effectiveness of new models and strategies for improving trust and safety metrics such as reducing exposure to spam, detecting abuse and improving lead quality
• Analyze a wide variety of data: structured and unstructured, observational and experimental
• Collaborate with engineering, product, and operations teams to define problems, develop solutions, and ensure the successful implementation and monitoring of machine learning models for Trust & Safety
• Maintain the right balance between speed of execution and scientific rigor when designing solutions
• Contribute to improving the overall performance of Trust & Safety models by exploring new features, algorithms, and data sources

Preferred Qualifications

• Experience in applied science in a Trust & Safety, security, or risk domain
• Expert knowledge of probability and statistics, including experimental design, predictive modeling, optimization, and causal inference
• Experience in applied science in growth or marketing at a Marketplace
• Experience with large-scale distributed systems
• A Master’s or Ph.D. in a relevant field (e.g., Computer Science, Machine Learning, Statistics)

Benefits

• Virtual-first working model coupled with in-person events
• 20 company-wide holidays including a week-long end-of-year company shutdown
• Library (optional use collaboration & connection hub) in San Francisco
• WiFi reimbursements
• Cell phone reimbursements (North America)
• Employee Assistance Program for mental health and well-being