Google Jobs Hyderabad


Data Scientist

Job Description

Minimum qualifications:

  • Bachelor’s degree in Computer Science or equivalent practical experience.
  • 8 years of experience in data science, data analysis, and/or a related field.
  • Experience working with web technologies (e.g., HTML, CSS, JavaScript, and HTTP).
  • Experience in customer-facing, translating technical concepts and solutions to non-technical and executive audiences.

Preferred qualifications:

  • Master’s degree in Statistics, Computer Science, Engineering, Mathematics, or a related field.
  • 2 years of experience in coding using a general purpose programming language.
  • Experience in big data and cloud platforms to deploy large-scale data science solutions.
  • Experience in SQL, R, Python, Marketing Analytics, Software Development.
  • Understanding of media and the statistical algorithms typically used in marketing analytics.

About The Job

gTech’s Product and Tools Operations team (gPTO) leverages deep user, operational, and technical insights to innovate Google’s Ads products into customer experiences that are so intuitive (or automated) that they require no support at all.

gPTO partners closely with gTech’s Support, Professional Services, Product Management, and Engineering teams to innovate and simplify our Ads products and build the productivity tools ecosystem for gTech users.

Google creates products and services that make the world a better place, and gTech’s role is to help bring them to life. Our teams of trusted advisors support customers globally.

Our solutions are rooted in our technical skill, product expertise, and a thorough understanding of our customers’ complex needs.

Whether the answer is a bespoke solution to solve a unique problem, or a new tool that can scale across Google, everything we do aims to ensure our customers benefit from the full potential of Google products.

To learn more about gTech, check out our video .


  • Leverage critical thinking and problem statement definition, decomposition, and problem solving to ensure efforts are focused on delivering impactful and actionable outcomes.
  • Adopt and develop data engineering methodologies including, but not limited to 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.
  • Analyse and develop explanatory, predictive, and prescriptive models using appropriate mathematical methods like frequentist statistics, bayesian statistics, time-series analysis, supervised and unsupervised machine learning methods, natural language process (NLP), and semantic analysis.
  • Collaborate across cross-functional stakeholder teams, managing opportunities and challenges that improve processes and help stakeholders become more data savvy.
  • Develop and conduct experiments to validate findings from observational research. Communicate and visualise insights to multiple levels of stakeholders with clarity, informed decision-making.

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