Business Data Scientist, gDATA

Minimum qualifications:

• Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
• 3 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a relevant PhD degree.

Preferred qualifications:

• 4 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a relevant PhD degree.

About the job

Google's leadership team hand-picks thorny business challenges, and members of BizOps work in small teams to find solutions. As part of this team you fully immerse yourself in data collection, draw insight from analysis, and then zoom out to develop compelling, synthesized recommendations. Taking strategy one step further, you also persuasively communicate your recommendations to senior-level executives, roll-up your sleeves to help drive implementation and check back-in to see the impact of your recommendations. The US base salary range for this full-time position is $141,000-$202,000 + 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.

Responsibilities

• Support the development and implementation of data-driven solutions with a focus on large language models (LLMs) and generative AI to solve key business problems. Help optimize support operations, automate case routing, and ultimately make Google Ads products more usable for our customers.
• Collaborate with cross-functional partners to identify challenges and opportunities, translating business needs into actionable data science solutions.
• Contribute to "problem solutioning" and "solution translating," shepherding projects from ideation to implementation and ensuring complex findings are communicated clearly to both technical and non-technical stakeholders.
• Help pioneer the future of gTech by staying ahead of the curve, applying emerging trends in data science to take on the impossible problems that stand between gTech and success.