Senior Data Scientist, Research Cloud
@ Google

Bengaluru, Karnataka, India
$200,000
On Site
Full Time
Posted 24 days ago

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About the Job

The Senior Data Scientist, Research Cloud at Google works on the Cloud Supply Chain Data (CSCD) Data Science and Product team. The team builds productivity and data products, develops AI/ML/statistical models, and provides insights to help Google Cloud Supply Chain and Operations (CSCO) define and achieve business goals.

Minimum Qualifications

A Master’s degree in a quantitative field (Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering) or equivalent practical experience, along with 5 years of experience using analytics to solve product or business problems, including coding (e.g., Python, R, SQL) and statistical analysis, or 3 years with a PhD degree.

Preferred Qualifications

8 years experience using analytics or 6 years with a PhD, experience with data metrics and analysis, solid understanding of measurement, statistics and program evaluation, ability to learn Supply Chain Operations, and strong problem-solving and business judgment skills.

Responsibilities

  • Develop machine learning and statistical models for anomaly detection, trend forecasting, pattern classification, and process optimization in Google Cloud Supply Chain and Operations.
  • Conduct investigative work, design model approaches, and perform exploratory data analysis.
  • Plan and execute project work, select appropriate methods, and advise on improving data infrastructure.
  • Identify issues with scope, data or approaches, escalate concerns, and present insights to stakeholders.

Key skills/competency

  • Machine Learning
  • Statistical Modeling
  • Data Analysis
  • Analytics
  • Python
  • R
  • SQL
  • Cloud Computing
  • Business Judgment
  • Exploratory Analysis
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How to Get Hired at Google

🎯 Tips for Getting Hired

  • Research Google's culture: Study their mission, values, and news.
  • Customize your resume: Highlight analytics and ML experience.
  • Prepare technical examples: Showcase Python, R, SQL projects.
  • Practice interviews: Focus on problem-solving and data analysis scenarios.

📝 Interview Preparation Advice

Technical Preparation

Review Python, R, SQL coding challenges.
Practice building ML models for forecasts.
Study statistics and anomaly detection techniques.
Refresh database querying and data analysis methods.

Behavioral Questions

Describe a challenging data project.
Explain a time you solved a complex problem.
Share an experience improving data processes.
Discuss teamwork in high-pressure projects.

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