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.
As a part of the Finance Data and Analytics (DnA) team, you will use data to inform business, process and product decisions across Google. In this role, you will have the opportunity to transform Finance processes using AI. Along with your product team within DnA, you will partner with finance and engineering teams to drive process efficiency and transformation, primarily through agentic and AI solutions.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $163000 - $237000 (USD) + 15% bonus target + equity + benefits
Learn more about
benefits at Google.
Responsibilities
- Work cross-functionally with analysts, engineers, and program managers to develop and deploy agentic and AI solutions.
- Use a product-driven mindset to transform key Finance processes using Agents and AI.
- Communicate results to peers, stakeholders and leaders.
- Partner with your product team to solve problem and deliver end-to-end process transformation.
- Operationalize and monitor solutions. Design and deploy model end points that adhere to high-availability SLAs, implementing necessary health checks, retries, and fallback mechanisms.
Minimum qualifications:
- Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
- 4 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.
- 1 year of experience building and deploying AI Agents.
Preferred qualifications:
- 6 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.
- Experience building agentic workflows (e.g., ADK, context management, prompt engineering) to transform corporate processes.
- Advanced proficiency in Python and SQL.
- Ability to containerize and deploy agents/models to production along with proficiency in observability tools to monitor performance, health, and drift.
- Demonstrated command of classical ML modeling (e.g., time series forecasting, trees).
- Background in computer science or software development.