Senior Data Scientist, Research, App Ecosystem and Trust

Google Google · Big Tech · Singapore

This role focuses on applying advanced quantitative methods and machine learning to protect Android users from abusive applications. The data scientist will conduct end-to-end analysis, develop and evaluate ML models, and collaborate with product and engineering teams to improve safety systems. The work involves handling large datasets in an adversarial environment, aiming to detect and prevent various forms of abuse.

What you'd actually do

  1. Work with large datasets and solve difficult, non-routine analysis problems, applying advanced quantitative methods as needed.
  2. Conduct end-to-end analysis that includes data gathering and requirements specification, processing, analysis, model development and evaluation, as well as written and oral delivery of results to business partners.
  3. Collaborate closely with stakeholders in Product Management, Engineering, and Operations teams to define relevant questions, objectives, and metrics; identify and implement quantitative methods to answer those questions.
  4. Develop, own, and evolve methodologies and frameworks, providing source-of-truth measures for the organization.
  5. Establish a comprehensive understanding of the production systems, advocate for changes where needed for product development, and build data-driven solutions to facilitate efficiency improvements.

Skills

Required

  • Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
  • 5 years of work experience using analytics to solve product or business problems, including coding (e.g., Python, R, SQL), querying databases, or statistical analysis, or 3 years of work experience with a PhD.

Nice to have

  • PhD in Statistics, Mathematics, Data Science, Economics, or a related quantitative field.
  • 4 years of experience, including expertise with statistical data analysis on real-world data such as ML modeling, experimentation, sampling methods, and causal inference methods.
  • Experience with machine learning on large datasets.
  • Experience articulating and translating business questions and using statistical techniques to arrive at an answer using available data.
  • Ability to demonstrate leadership and self-direction, with a willingness to both teach others and learn new techniques.
  • Ability to demonstrate skills in selecting the right methodology given a data analysis problem.

What the JD emphasized

  • improving and evaluating machine learning models
  • detect bad actors who try in turn to evade detection
  • adversarial scenario

Other signals

  • improving and evaluating machine learning models
  • detect bad actors who try in turn to evade detection
  • preventing abuse is fascinating technically and it features an adversarial scenario