Google

Minimum qualifications:

• Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
• 5 years of work experience with analysis applications (extracting insights, performing statistical analysis, or solving business problems), and coding (Python, R, SQL), or 2 years of experience with a Master's degree.

Preferred qualifications:

• Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.

About the Job

The Shopping MCAD (Merchant Content Acquisition and Discovery) Data Science team plays a critical role in realizing Google's Shopping goal of being an essential part of shopping journeys for consumers and the best place for merchants to connect. Our core mission is to enable data decision making at scale to drive Shopping to achieve its goals.

Our team is committed to providing actionable insights, building scalable data products, and collaborating closely with engineering, product, and business teams. We strive to be a central driver of decision-making within Shopping, enabling the MCAD organization to effectively grow and enrich Google's Shopping ecosystem.

The US base salary range for this full-time position is $132,000-$189,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Conduct in-depth data analysis to identify trends, patterns, and insights that inform decisions and drive improvements across the MCAD organization.
• Collaborate closely with engineering, product, and business teams to define project requirements, translate problem-solving findings into actionable recommendations, and build scalable solutions.
• Design and evaluate experiments to measure the impact of new features, algorithms, and strategies, ensuring data-driven iteration and optimization.
• Build and maintain data pipelines and reporting dashboards to monitor key metrics, track performance, and provide stakeholders with clear and concise insights.
• Collaborate with Engineering to develop and evaluate advanced investigative models and machine learning algorithms to address key tests in merchant and content acquisition, shopping understanding graph enrichment, and content discoverability.