Customer Experience Data Scientist, Customer Experience Lab, Go-To-Market

Key Skills

Bayesian networks, Data Analysis, Data science techniques, Database, Machine learning techniques, Natural Language Processing (NLP), Python Programming, R Programming, SAS, SQL, Statistics

Job Description

Minimum qualifications:

• Master's degree in quantitative disciplines (e.g. Statistics, Operations Research, Bioinformatics, etc.) or equivalent practical experience
• 6 years of experience in relevant data scientist role, including with statistical data analysis (such as 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
• Demonstrated experience utilizing Data Engineering skills for pipelining, scripting and Python coding/querying, data cleaning for downstream data analysis
• Demonstrated domain knowledge in the digital advertising space and familiarity with Google Ads products
• Demonstrated leadership in building data science models from start to finish, including defining a problem statement, exploring approaches, conducting analyses, presenting to stakeholders

About the job

The Business Strategy & Operations organization provides business critical insights using analytics, ensures cross functional alignment of goals and execution, and helps teams drive strategic partnerships and new initiatives forward. We stay focused on aligning the highest-level company priorities with effective day-to-day operations, and help evolve early stage ideas into future-growth initiatives

As part of the Customer Lab team, you will be leading data science analytics among Google’s advertising ecosystem partnering 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 objectives, 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 $146,000-$220,000 + 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 for new hire 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

• Leverage critical thinking and problem statement definition, decomposition, and problem solving skills to deliver impactful and actionable outcomes. 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, 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