Data Scientist, Carbon Footprint, Google Cloud
Note: Google’s hybrid workplace includes remote and in-office roles. By applying to this position you will have an opportunity to share your preferred working location from the following:
In-office locations: Warsaw, Poland.
Remote location(s): Poland.
- Bachelor's degree in Data Science, Economics, Statistics, or an analytical field, or equivalent practical experience.
- Experience building data infrastructure and SQL pipelines.
- Experience solving complex problems, growing in ambiguity and change, and achieving significant impact.
- Experience or knowledge in energy, sustainability, carbon emissions, and accounting.
- Familiarity with cloud computing platforms and concepts (e.g. GCP).
About the job
At Google, data drives all of our decision-making. Quantitative Analysts work all across the organization to help shape Google's business and technical strategies by processing, analyzing and interpreting huge data sets. Using analytical excellence and statistical methods, you mine through data to identify opportunities for Google and our clients to operate more efficiently, from enhancing advertising efficacy to network infrastructure optimization to studying user behavior. As an analyst, you do more than just crunch the numbers. You work with Engineers, Product Managers, Sales Associates and Marketing teams to adjust Google's practices according to your findings. Identifying the problem is only half the job; you also figure out the solution.
Through your professional expertise and judgment, you work on multiple projects concurrently, developing analyses, models and methods to support and verify the impact of your recommendations. You share your expertise by consulting on projects, serving on committees and mentoring team members.
- Partner with engineering to maintain data pipelines and build automated data validation solutions.
- Working closely with Data Scientists, Product Managers, Engineering, Program Management, and sustainability/carbon experts to fix and validate data issues.
- Explore the data aspects of new product requirements (e.g. more granular data features) and machine learning models.
- Improving data quality and carbon apportionment methodology in our queries.
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