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In this role, you will be responsible for applying best data science practices to solve our ads customers problems in marketing. You will be passionate about bringing the best of Googles Machine Learning (ML) capabilities to build innovative solutions that address our customer's problem-space. You will be responsible for executing on gTechs strategy and goal to deliver applied data science to Google's largest ads customers.
The Business Operations and Strategy team at Google plays a critical role in defining and driving strategic, operational and organizational improvements across the company. Also known as "BizOps", the group operates like an internal consulting group working on a range of critical projects and issues. BizOps creates strategies for promoting growth of our products like YouTube, Chrome and Mobile. They handle issues around partner development, strategy work in emerging markets such as Africa and India, as well as pricing strategies across our B2B and consumer products. The BizOps team is at the forefront of Google's fast-moving strategic priorities, addressing operational challenges and enabling innovation.
Responsibilities
Lead data science aspects of client engagements in the area of marketing effectiveness and marketing portfolio management with Machine Learning and statistics.
Collaborate with customers to unpack their problems and identify the best statistical techniques that can solve the problem; own the development of a modeling framework.
Engage important stakeholders to assess data and model readiness and be able to scale a proof-of-concept to a larger solution, collaborate with Product/Engineering teams to increase and optimize capabilities of our data scientist (DS) team, employing methods which create opportunities for scale, proactively helping to drive innovation.
Work with customer and internal teams to translate data and model results into tactical and insights that are actionable for decision.
Develop comprehensive understanding of Google data structures, and metrics, advocating for product changes where needed.

Be part of a team that pushes boundaries, developing custom silicon solutions that power the future of Google's direct-to-consumer products. You'll contribute to the innovation behind products loved by millions worldwide. Your expertise will shape the next generation of hardware experiences, delivering unparalleled performance, efficiency, and integration.

The AI and Infrastructure team works on the worlds toughest problems, redefining whats possible and the possible easy. We empower Google customers by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Googler Cloud customers, and billions of Google users worldwide. Were at the center of amazing work at Google by being the flywheel that enables our advanced AI models, delivers computing power across global services, and offers platforms that developers use to build services.

In AI and Infrastructure, we shape the future of hyperscale computing by inventing and creating world-leading future technology, and drive global impact by contributing to Google infrastructure, from software to hardware (including building Vertex AI for Google Cloud). We work on complex technologies at a global scale with key players in the AI and systems space. Join a team of talented individuals who not only work together to keep data centers operating efficiently but also create a legacy of driving innovation by building some of the most complex systems technologies.


Responsibilities
Develop and optimize the overall layout of the chip, including partitioning, macro and IP placement, and pin placement.
Design and implement efficient power delivery networks (power grids) to ensure stable power to all parts of the chip.
Develop and validate high-performance, low-power clock networks (Clock Tree Synthesis - CTS) to ensure proper synchronization across the entire chip.
Develop, enhance, and maintain custom scripts (e.g., Tcl, Perl, Python) for automation and improved efficiency.
Conduct extensive design rule checks (DRC) to ensure the layout adheres to manufacturing rules, performing layout versus schematic (LVS) checks to verify that the physical layout matches the logical design.