Bachelor’s degree in a quantitative discipline (e.g., computer science, statistics, operations research, bioinformatics, economics, computational science, mathematics, physics, electrical engineering, industrial engineering) or equivalent practical experience.
Experience with statistical software and database languages (e.g., SQL, Python, R, MATLAB, TensorFlow etc.).
Master’s degree in a quantitative discipline or equivalent practical experience.
Experience in managing, manipulating and presenting data, including extract, transform, load (ETL) development, database design, reports creation and data visualization.
Excellent stakeholder and project management skills, e.g., expectation setting, education, conflict management, prioritization.
Experience in applying statistical and machine learning methods to solve real-world problems, with evidence of leadership through full end-to-end delivery.
About the job
At Google, data drives all of our decision-making. As part of the Finance Business Intelligence and Analytics team, you will use data to inform business and product decisions across the company. Using your technical skills, business acumen and creativity, you will build solutions to generate insights that will allow clients to quickly and accurately see how our key business products and processes are performing. Previous experience addressing business problems through data analytics will have equipped you well for this role. You will work on projects from inception to delivery, ensuring that you deliver high-quality and relevant solutions to help intelligently grow our business.
The name Google came from “googol,” a mathematical term for the number 1 followed by 100 zeros. And nobody at Google loves big numbers like the Finance team when providing in depth analysis on all manner of strategic decisions across Google products. From developing forward-thinking analysis to generating management reports to scaling our automated financial processes, the Finance organization is an important partner and advisor to the business.
Partner with Finance leadership and their teams to understand business context, defining data analytics solutions that are an excellent fit for their evolving needs.
Serve as a technical expert and a cross-functional analytics consultant, mentor analyst colleagues and provide consultation to other projects and teams.
Contribute to the development of the team’s tools, skills, standards, culture and impact.
Apply statistical and machine learning methods to solve problems, including the full delivery lifecycle (e.g., automating processes for data retrieval, data preparation, exploratory data analysis, building and evaluating models, interpreting results, transitioning solutions to production, performance monitoring, etc.).