Google Hiring Data Scientist | Fresher / Experienced

Qualifications

Minimum qualifications:

• Master’s degree or PhD in Statistics, Biostatistics, Operations Research, Physics, Economics, Applied Mathematics, or similar quantitative discipline, or equivalent practical experience.
• Relevant internship or work experience with data. Experience in quantitative methodologies with statistics and causal inference method.
• Experience with statistical software (e.g., R, Python, S-Plus, SAS, or similar).

Preferred qualifications:

• Experience with statistical data analysis such as linear models, multivariate analysis, stochastic models, sampling methods.
• Experience with machine learning on large datasets.
• Ability to draw conclusions from data and recommend actions.
• Ability to teach others and learn new techniques such as differential privacy.
• Ability to select the right statistical tools given a data analysis problem.

Job Description & Responsibilities:-

As a Data Scientist, you will evaluate and improve Google’s products. You will collaborate with a multi-disciplinary team of Engineers and Analysts on a wide range of problems, using statistical methods for the issues of measuring quality, improving consumer products, and understanding the behavior of end-users, advertisers, and publishers.

• Work with large complex data sets, solve difficult non-routine analysis problems, and apply advanced problem-solving methods as needed. Conduct analysis that includes data gathering and requirements specification, processing, analysis, ongoing deliverables and presentations.
• Make business recommendations (e.g. cost-benefit, forecasting, experiment analysis) with effective presentations of findings at multiple levels of stakeholders through visual displays of quantitative information.
• Research and develop analysis, forecast and optimize methods to improve the quality of Google’s user facing products such as ads quality, search quality, end-user behavioral modeling and live experiments.
• Help suggest, support and shape new data-driven and privacy-preserving advertising and marketing products in collaboration with engineering, product and customer-facing teams.
• Find ways to combine large-scale experimentation, statistical-econometric, machine learning and social-science methods to answer business questions at scale.