Minimum qualifications:
- Master's degree in Statistics, Mathematics, Bioinformatics, Economics, a quantitative field, or equivalent practical experience.
- 3 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a relevant PhD degree.
- 2 years of experience in a data science field.
- Experience with statistical software (e.g., R, Python, MATLAB) and database languages (e.g., SQL).
Preferred qualifications:
- 4 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a relevant PhD degree.
- Experience delivering insights from ML to customers (problem scoping/definition, modeling, interpretation).
- Experience using or deploying digital analytics and measurement solutions.
- Proficiency with data extraction and modeling tools (SQL, Python), and an understanding of the statistical algorithms used in Marketing Analytics.
- Proficiency in Computer Vision and NLP in the context of marketing analytics, and the ability to bring cutting-edge generative AI technologies to customer issues in marketing.
About the job
The gTech Ads Marketing Data Science team helps measure and optimize marketing ROI for Google’s biggest clients. We build bespoke models that address client’s key business issues. We are multi-disciplined professionals, PhDs, statisticians, economists, engineers, and former consultants with deep experience in Data Science, Machine Learning, and Marketing Analytics. We learn the innovative technologies that drive Google products and bring those innovations to life in the context of specific client needs.
In this role, you will be responsible for applying best data science practices to solve our ads customers complex problems in marketing. You will be passionate about bringing the best of Google’s ML capabilities to build innovative solutions that address our customer's problem-space. You will be responsible for executing on gTech’s strategy and goal to deliver applied data science to Google's biggest ads customers.
Google creates products and services that make the world a better place, and gTech’s role is to help bring them to life. Our teams of trusted advisors support customers globally. Our solutions are rooted in our technical skill, product expertise, and a thorough understanding of our customers’ complex needs. Whether the answer is a bespoke solution to solve a unique problem, or a new tool that can scale across Google, everything we do aims to ensure our customers benefit from the full potential of Google products.
To learn more about gTech, check out our video.
The US base salary range for this full-time position is $141,000-$202,000 + 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
- Lead data science aspects of client engagements in the area of marketing effectiveness and marketing portfolio management with knowledge of Machine Learning (ML) and statistics.
- Collaborate with customers to resolve their issues and identify the best statistical techniques that can solve the problem. Own the development of modeling framework.
- Engage important stakeholders to assess data and model readiness, and be able to scale a proof-of-concept to solution.
- Work with customer and internal teams to translate data and model results into tactical insights that are actionable for decision-making. Co-present to and work with clients to integrate recommendations into business processes.
- Collaborate with Product/Engineering teams to increase capabilities of our Applied Data Scientist (DS) team, employing methods which create opportunities for scale, proactively helping to drive innovation.
