Senior Business Data Scientist, Finance
Description For Senior 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:
- Partnering with Finance leadership to understand business context and deliver insights and prototypes.
- Working with Machine Learning Scientists and Engineers to improve model usability through appropriate metrics design.
- Collaborating on model development and improvement, including identifying new data sources, hypothesis testing, and feature engineering.
- Providing analyses using advanced analytics/statistical methods focused on business insights.
- Developing reusable analytic frameworks for consistent results across business areas.
- Contributing to a culture of learning and making Machine Learning accessible to the broader team and stakeholders.
This role requires a Master's degree in a quantitative discipline (e.g., Statistics, Engineering, Sciences) or equivalent practical experience, along with 4 years of industry experience in a Data Analyst or Data Science role. The ideal candidate will have experience in time series data analysis, stakeholder-facing roles, and proficiency in statistical software (e.g., Python), database languages (e.g., SQL), and data visualization tools (e.g., Tableau). Excellent communication skills are essential to translate technical solutions to leadership.
Responsibilities For Senior Business Data Scientist, Finance
- Partner with Finance leadership and their teams to understand business context, ideate, and deliver insights and prototypes
- Work with Machine Learning Scientists and Engineers to improve usability of the Machine Learning models through the design of appropriate metrics
- Collaborate with Machine Learning Scientists to develop and improve models (e.g., identifying new data sources, hypothesis testing, feature engineering, model prototyping, analyzing model output and model explainability)
- Provide analyses through advanced analytics/statistical methods that tell a story focused on business insights
- Develop reusable and robust analytic frameworks to ensure consistent results across business areas
- Contribute to a culture of learning, sharing and making Machine Learning accessible across the broader team and our stakeholders
Requirements For Senior Business Data Scientist, Finance
- Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience
- 4 years of experience in the Industry in a Data Analyst or Data Science role, analyzing data sets to solve business problems through statistical methods and predictive analytics
- Experience in data analysis for time series data, to solve business problems in complex, fast-moving, and ambiguous business environments with strong data intuition and business acumen
- Experience in stakeholder-facing or client-facing roles (e.g., previous consulting role)
- Experience with statistical softwares (e.g., Python), database languages (e.g., SQL), and data visualization tools (e.g., Tableau)
- Excellent written and verbal communication skills to translate technical solutions and methodologies to leadership
