Thumbtack is hiring a
Senior Data Scientist

Summary

The job is for a Marketing Senior Data Scientist at Thumbtack, a fast-growing company in the $600B+ home services industry. The role involves providing analytical support to growth marketing teams, driving optimization of various marketing channels, and contributing to the broader data science team.

Requirements

• 5+ years of marketing data science/analytics experience
• Familiarity with digital marketing and experience with campaign measurement and performance optimization
• Experience with running channel incrementality tests and building Marketing Mix Model
• Passion for problem-solving, critical thinking, and data analytics
• Sharp business judgment and the ability to see past the noise
• Strong communicator, attentive listener, and compelling explainer
• Expert in SQL, proficient in Python or R, proficient in Data visualization such as Looker, LookerStudio or Tableau

Responsibilities

• Provide a full suite of analytical support including analyzing user behaviors, identifying the target audience, campaign performance measurement, budget forecasting to marketing partners
• Identify opportunities and provide recommendations to optimize individual campaign as well as marketing channel mix
• Design and analyze experiments and other tests for causal inference
• Partner with MarTech stakeholders, PM, engineers, etc. to help scope new features, e.g., optimize performance via TV audience expansion, improve SEM auto-bidding
• Develop topic area expertise, e.g., campaign measurement, A/B testing, customer segmentation, targeting optimization
• In collaboration with cross-functional partners, build new solutions and capabilities to scale growth marketing

Preferred Qualifications

• Experience in relevant tech companies, e.g., consumer-facing or marketplace
• Experience in an analytical environment outside of analytics, e.g., management consulting
• Bachelor’s or advanced degree (preferred) in statistics, math, data science, computer science, or economics
• Experience in building various statistical and machine learning models, e.g. GLM, CLTV, statistical time series forecasting, attribution model