Business Data Scientist, Finance

As a Quantitative Analyst at Google, you will be responsible for analyzing large data sets and building expert systems to improve understanding of the Web and product performance. You'll perform complex statistical analysis on non-routine problems and work with engineers to embed models into production systems. The role involves managing fast-changing business priorities and interfacing with product managers and engineers.

You'll be part of the Finance Data and Analytics (DnA) team, which combines business acumen, technology, and innovation to organize data, enable insights, and create a data-driven Finance organization at Google. As part of a growing Data Science team, you'll work on strategic business challenges across multiple areas (e.g., Ads, YouTube, Search, Play) through the lens of business generation. Collaboration with Data Scientists, Data Engineers, and Project Managers is key to create data products that enable finance partners to make informed decisions and manage risks and opportunities.

Key responsibilities include:

1. Partnering with Finance leadership to understand business context, ideate, and deliver insights and prototypes.
2. Working with Machine Learning Scientists and Engineers to improve model usability through appropriate metrics design.
3. Collaborating on model development and improvement, including identifying new data sources, hypothesis testing, feature engineering, and model explainability.
4. Providing analyses using advanced analytics/statistical methods to tell a business-focused "story".
5. Developing reusable and robust analytic frameworks for consistent results across business areas.
6. Contributing to a culture of learning, sharing, and making Machine Learning accessible across the team and stakeholders.

This role requires a strong background in data analysis, especially for time series data, to solve complex business problems in fast-moving environments. Experience in stakeholder-facing roles, proficiency with statistical software (e.g., Python), database languages (e.g., SQL), and data visualization tools (e.g., Tableau) is preferred. Excellent communication skills are essential to translate technical solutions to leadership effectively.