Data Scientist, Research, YouTube

3 Months ago • 6-8 Years • Data Analyst • $177,000 PA - $266,000 PA

Job Summary

Job Description

YouTube Data Science is all about making better data-driven decisions to improve YouTube. This role focuses on ensuring fairness and inclusivity across YouTube's products and systems, working on areas like Creator Fairness, URG Safety, and GenAI fairness. You'll collaborate with stakeholders, translate business questions into actionable analyses and models, and leverage your data expertise to address crucial issues. Responsibilities include gathering and compiling data, designing and evaluating models, and ensuring data quality. You'll need strong analytical skills, proficiency in coding languages (e.g., Python, R, SQL), and experience working with large datasets.
Must have:
  • Master's degree in relevant quantitative field
  • 8 years of experience using analytics
  • Coding proficiency (Python, R, SQL)
  • Experience with data querying and analysis
  • Strong analytical and problem-solving skills
Good to have:
  • PhD degree
  • 10 years of relevant experience
  • Experience with data infrastructure
  • Knowledge of fairness and bias in AI systems
Perks:
  • Bonus
  • Equity
  • Benefits

Job Details

Minimum qualifications:

  • Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, a related quantitative field, or equivalent practical experience.
  • 8 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of work experience with a PhD degree.

Preferred qualifications:

  • 10 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 8 years of work experience with a PhD degree.

About the job

YouTube Data Science is all about making better data-driven decisions to improve YouTube. Racial Justice, Equity, Product Inclusion (REP) is a horizontal team that supports various Product Managers and Engineering teams and ensures that YouTube builds fair and inclusive products and systems. Here, fairness pertains to eliminating bias from automated systems (e.g., ranking, nomination, AI generation, classification, etc.). The team works in numerous areas including ensuring Creator Fairness, URG Safety, and has a particular focus on GenAI fairness.

The US base salary range for this full-time position is $177,000-$266,000 + 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 .

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). Independently format, re-structure, and/or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.

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