Join Google's Large Customer Sales (LCS) team as a Marketing Science Lead, serving as a strategic partner to leading global brands and agencies. This role challenges traditional thinking and drives business growth by leveraging Google's comprehensive advertising solutions across Search, YouTube, and Measurement.

As part of the LCS team, you will operate at the forefront of technology, collaborating with executives, shaping market strategies, and delivering measurable results that significantly impact major global businesses and propel Google's growth.

This is an opportunity to influence market-shaping strategies and deliver tangible results that impact major global businesses.

Develop and implement strategic client learning agendas and experimentation roadmaps, utilizing A/B testing and Google's proprietary methods to quantify and enhance business growth from Google Ads investments.

Translate complex analytical and experimental findings into clear, actionable business recommendations and executive-level presentations designed for immediate deployment by marketing leaders to drive revenue.

Serve as a trusted consultant to advertisers, assisting with data infrastructure audits and modernization to ensure long-term ad performance and efficient automated bidding in the face of privacy changes and signal loss.

Provide expert advice on budget allocation and testing feasibility. Lead executive reviews and workshops to foster investment confidence among sales teams and agency partners.

A Bachelor's degree or equivalent practical experience is required.

Possess at least 5 years of experience in media analytics, advertising sales, digital media and marketing, consulting, or financial analysis.

Demonstrated experience collaborating with partners to identify technical challenges and opportunities, and effectively selling AdTech/MarTech solutions.

An MBA or Master's degree in a quantitative field such as Data Analytics or Economics is preferred. Experience in quantifying ROI and improving ad spend ROI through a test-and-learn approach is highly valued, particularly in areas like experimental methods (A/B testing, Geo experiments, user lift experiments), advanced attribution (SKAN, MTA), or statistical methods (causal inference, MMM, Statistical MTA).