Data Scientist, Research, Google Display Ads Optimization

2 Months ago • 3 Years + • $141,000 PA - $202,000 PA

Job Summary

Job Description

This Data Scientist role at Google focuses on optimizing Google Display Ads. Responsibilities include collaborating with stakeholders to define analytical questions, translating business needs into tractable analyses and models, leveraging custom or existing data infrastructure, designing and evaluating models to solve complex problems, and gathering, cleaning, and validating data from various sources using tools like SQL, R, and Python. The ideal candidate will possess a Master's or PhD in a quantitative field, strong analytical and problem-solving skills, and experience in managing investigative projects. Experience with machine learning and mathematical optimization is preferred.

Must have:

Master's degree in quantitative field
3+ years analytics experience
Coding proficiency (Python, R, SQL)
Data analysis & problem-solving skills
Project management experience

Good to have:

PhD in related field
Machine learning & optimization experience
Experience with Python and SQL

Perks:

Bonus
Equity
Benefits

7 skills required

7 skills required for this role

Add these skills to join the top 1% applicants for this job

unity

python

sql

data-science

data-analytics

mathematical

team-management

Job Details

Minimum qualifications:

Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
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 PhD degree.
1 year of experience managing investigative projects.

Preferred qualifications:

PhD degree in Mathematics, Statistics, Operations Research, Economics or a related technical field.
Experience with machine learning and mathematical optimization.
Experience programming in Python and SQL.
Excellent data analysis and problem-solving skills.

About the job

At Google, data drives all of our decision-making. Quantitative Analysts work all across the organization to help shape Google's business and technical strategies by processing, analyzing and interpreting huge data sets. Using analytical excellence and statistical methods, you mine through data to identify opportunities for Google and our clients to operate more efficiently, from enhancing advertising efficacy to network infrastructure optimization to studying user behavior. As an analyst, you do more than just crunch the numbers. You work with Engineers, Product Managers, Sales Associates and Marketing teams to adjust Google's practices according to your findings. Identifying the problem is only half the job; you also figure out the solution. 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 .

Responsibilities

Collaborate with stakeholders in cross-projects and team settings to identify and clarify business or product questions to answer. Provide feedback to translate and refine business questions into tractable analysis, evaluation metrics, or mathematical models.
Use custom data infrastructure or existing data models as appropriate, using specialized knowledge. Design and evaluate models to mathematically express and solve defined problems with limited precedent.
Gather information, business goals, priorities, and organizational context around the questions to answer, as well as the existing and upcoming data infrastructure.
Own the process of gathering, extracting, and compiling data across sources via relevant tools (e.g., SQL, R, Python). Separately format, re-structure, or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.