Thumbtack needs to balance supply and demand in its marketplace, focusing on acquiring, retaining, and enabling the success of larger professionals to meet high demand projections, unlock strategic partnerships, and enhance customer experience and platform growth.
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
- 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 lead seamless execution.
Responsibilities
- Develop, deploy, and optimize machine learning models and algorithms for various supply shaping problems, including: Targeted large pro acquisition and lead scoring.
- Pro retention and churn prediction for high-value segments.
- Optimization of pro expansion (e.g., category, geo, budget).
- Enabling new features for marketplace matching and ranking algorithms specifically for large and licensed pros.
- Leveraging external data (e.g., credentialing databases) for pro quality assessment and targeting.
- Design and execute marketplace experiments to evaluate the effectiveness of new models, features, and strategies on key supply and business metrics.
- Analyze a wide variety of data, including marketplace interactions, pro profiles, financial data, and external data sources, to identify trends, opportunities, and risks related to supply health.
Other
- Initiate and drive applied science initiatives to completion with a focus on improving the acquisition, retention, and expansion of large professionals on the Thumbtack platform.
- 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 machine learning 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 improving the overall performance and robustness of supply shaping models by exploring new features, algorithms, and data sources.
