Business Data Scientist, Customer Voice, Analytics

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.
  
• Experience working with Large Language Models, prompt engineering, and fine-tuning techniques.

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.
  
• Experience with vector databases, embedding models, transformer models, and clustering algorithms.

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.

Our team, within Go-to-Market (GTM), serves as the strategic intelligence partner for product teams, transforming massive volumes of unstructured conversational data into quantified, trusted insights that bridge the gap between customer feedback and product decisions. This is a high-visibility initiative critical for accelerating the Ads product adoption flywheel and shaping GTM strategy for priority products.

You will develop the models to extract nuanced themes and actionable signals from hundreds of thousands of customer conversations. Beyond text extraction, you will apply core machine learning and statistical methods to cluster feedback, predict customer behaviors, and ensure our insights are reported with rigorous statistical confidence.

The US base salary range for this full-time position is $138,000-$198,000 + 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.

Our team, within Go-to-Market (GTM), serves as the strategic intelligence partner for product teams, transforming massive volumes of unstructured conversational data into quantified, trusted insights that bridge the gap between customer feedback and product decisions. This is a high-visibility initiative critical for accelerating the Ads product adoption flywheel and shaping GTM strategy for priority products.

Our team, within Go-to-Market (GTM), serves as the strategic intelligence partner for product teams, transforming massive volumes of unstructured conversational data into quantified, trusted insights that bridge the gap between customer feedback and product decisions. This is a high-visibility initiative critical for accelerating the Ads product adoption flywheel and shaping GTM strategy for priority products.

You will develop the models to extract nuanced themes and actionable signals from hundreds of thousands of customer conversations. Beyond text extraction, you will apply core machine learning and statistical methods to cluster feedback, predict customer behaviors, and ensure our insights are reported with rigorous statistical confidence.

You will develop the models to extract nuanced themes and actionable signals from hundreds of thousands of customer conversations. Beyond text extraction, you will apply core machine learning and statistical methods to cluster feedback, predict customer behaviors, and ensure our insights are reported with rigorous statistical confidence.

You will develop the models to extract nuanced themes and actionable signals from hundreds of thousands of customer conversations. Beyond text extraction, you will apply core machine learning and statistical methods to cluster feedback, predict customer behaviors, and ensure our insights are reported with rigorous statistical confidence.

Responsibilities

• Design, train, and deploy NLP models and unsupervised machine learning algorithms to identify emerging trends and friction points within sales transcripts.
• Build predictive machine learning models that leverage text-derived features to forecast key outcomes.
• Apply robust statistical methods (e.g., confidence intervals, significance testing, sampling strategies) to your findings, ensuring the metrics and themes we report to product and GTM leadership are reliable and statistically sound.
• Transform complex NLP and ML outputs into clear, compelling narratives, effectively communicating the "so what" and the degree of certainty behind the data.