Business Data Scientist, Machine Learning

Mid • On-site

$166,000 - $244,000/yr

Mountain View, CA , +1

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.

Preferred qualifications:

Experience designing and implementing causal ML models.
Experience designing and building large datasets for ML modeling.
Experience with statistical modeling packages.
Excellent presentation skills.

About the job

The Global Business Strategy and Operations team is part of the Go-to-Market organization that makes sure Google's business pursues the best strategy and executes flawlessly. Team members are exceptional in business strategy and operations, are problem-solvers and strategic, yet highly pragmatic and results-oriented. This team architects the future of Google’s global ads business. The function drives Go-to-Market strategy and analytics, operational efficiency and productivity, risk and compliance, Chief Business Officer and Chief of Staff team, and ads policy strategy.

The Data, Insights, and Analytics (DIA) partners with various Global Business Strategy and Operations team members to drive the best decisions possible with data. We build creative and novel Machine Learning (ML) solutions and analyses to drive strategy. We help our organization make nuanced, smart, and difficult decisions to ensure the Ads Business continues to thrive.

In this role, you will be significantly improving how customers are assigned to the most optimal sales channel, as our current manual decision model, while nuanced, is limited by historical data and a narrow understanding of what drives customer performance. You will be tasked with developing a causal ML model to automate and enhance these assignments, while also uncovering new data patterns that could revolutionize how our business manages clients. You will need to be a strategic thinker, using your business and product judgment to guide our data strategy. Your problem-solving skills will be key to ideating, discovering, and integrating hundreds, if not thousands, of data signals, ensuring they are perfectly structured for our causal ML problem.

The US base salary range for this full-time position is $166,000-$244,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

Develop core dataset and signal repository, using SQL or other scripting language, for Machine Learning. Develop expansive dataset that describes the Google Ads Customer, in as much detail as reasonable, that will enable us to establish causal effects from our business efforts.
Investigate, ideate, build, and streamline major signal repository to enable ML and unique insights.
Analyze data, partner with other Business Data Scientists, to help us understand drivers of historical business performance. Leverage insights to determine areas of opportunity for improvement in business treatment.
Build causal ML models to improve business channel assignments and treatment.

Google

Google LLC started as a PhD project by Larry Page and Sergey Brin in 1998 at Stanford University. Google LLC has blossomed into a behemoth of the tech world. With its mission to organize the world's information and make it universally accessible and useful, Google’s search engine is its crown jewel. Online advertising, via AdWords and AdSense, forms the backbone of its financial success. Beyond search, Google has ventured into cloud computing, hardware, and software development. The innovative PageRank algorithm revolutionized search engine technology, and surviving the dot-com bubble burst and going public in 2004 spurred its meteoric growth. Acquiring YouTube stands as a testament to Google’s strategic expansion.