Log In
Sign Up
Google Looking for Data Scientist, Customer Experience Lab, Go To Market at Mountain View, CA, USA Full Time
This role may also be located in our Playa Vista, CA campus.
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mountain View, CA, USA; Atlanta, GA, USA; Chicago, IL, USA; New York, NY, USA; Los Angeles, CA, USA; San Francisco, CA, USA; Washington D.C., DC, USA.
Minimum qualifications:
- Master’s degree in a quantitative discipline (e.g., Statistics, Operations Research, Bioinformatics, etc.) or equivalent practical experience.
- 6 years of experience in data scientist roles, including with statistical data analysis (e.g., linear models, multivariate analysis, stochastic models, sampling methods).
- Experience with statistical software (e.g., R, SAS, Python), databases, and scripting languages (e.g. SQL).
Preferred qualifications:
- Experience applying data science methodology to survey research, including experience with sampling and weighting.
- Experience utilizing data engineering skills for pipelining, scripting and Python coding/querying, and data cleaning for downstream data analysis.
- Experience leading the building of data science models from start to finish, including defining a problem statement, exploring approaches, conducting analyses, and presenting to stakeholders.
- Domain knowledge in the digital advertising space and familiarity with Google Ads products.
About the job
As part of the Customer Lab team, you will lead data science analytics across Google’s advertising ecosystem. You’ll partner with Customer Lab Researchers and cross-functional senior leaders across Google’s Support, Product, Engineering, Brand, and Business organizations to bring actionable insights to the business. You will develop an understanding of business goals, risks, challenges, and opportunities to ensure that analytics efforts are supportive of business decision making and are actionable by executive leadership.
The Go-to-Market Operations (GtM) team ensures Google’s complex and ever-evolving Ads business runs smoothly. We are instrumental in setting go-to-market strategy, and ensuring flawless execution and operations against the strategy. We have teams embedded in each of the major Ads business areas as well as global teams that work across the business areas. Team members are analytical and strategic, with a pragmatic sense of how to get things done.
The US base salary range for this full-time position is $150,000-$223,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. 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
- Deliver impactful and actionable outcomes through problem statement definition, decomposition, and solution development. Communicate and visualize insights to multiple levels of stakeholders with clarity, informing decision-making.
- Develop data engineering methodologies including data source and feature identification and integration, data pipelining, feature engineering, and data munging and analysis using script/code driven methods that can translate from research to production.
- Analyze and develop explanatory, predictive, and prescriptive models using appropriate mathematical methods including frequentist statistics, Bayesian statistics, time-series analysis, supervised and unsupervised machine learning methods, natural language process (NLP), and semantic analysis.
- Develop and conduct experiments to validate findings from observational research.
- Ensure actionability of insights through strong internal and cross-functional collaboration.
