Global Product Lead, GTM Intelligence and Optimization

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 6 years of experience in product or program management.
  
• Experience with data analysis or data tools (e.g., SQL, MySQL, Tableau, etc.).
• Experience working with system design or product design.

Preferred qualifications:

• Experience with data-driven analysis and reporting.
  
• Ability to manage multiple stakeholders.
  
• Strong written and verbal communication skills.

About the job

In this role, you will partner closely with Data Science, Product, Engineering, Sales, and Program Management to deeply understand operational pain points, form business requirements, and prioritize ML/AI and causal inference solutions to influence Google Customer Solutions (GCS) Program impact measurement product development and landing.Google Customer Solutions (GCS) sales teams are trusted advisors and competitive sellers who maintain a relentless focus on customer success by bringing the best Google has to offer to small- and medium-sized businesses (SMBs), which are the backbone of our communities. As a member of our team, you’ll have the opportunity to work with company owners and make a real difference in their businesses by helping them grow. Together, we help shape the future of innovation for customers, partners, and sellers...and we have fun doing it.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $148000 - $215000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

• Lead the design, execution, and integration of impact measurement frameworks. Refine how we capture treatment depth, account for program volatility, and design frameworks that quantify sales and program effectiveness to drive business decisions.
• Drive a "product-first" mindset that influences technical data science roadmaps and establishes self-sustaining, automated operational infrastructures.
• Translate strategies and sales activity adoptions into actionable business requirements for Data Science and Engineering. Act as the bridge between sales needs and technical product constraints, ensuring the full depth of seller-client interactions is captured.
• Advocate the development and deployment of AI-enabled products and intelligent assistants. Eliminate manual data bottlenecks, establish a single source of truth, and empower leadership and strategy teams with insights that accelerate decision-making.
• Guide the extraction and standardization of advanced data signals, including speech-to-text pitch detection and customer engagement metrics.