Job Summary

$9,549 - $19,098 SGD / Monthly EST

Job Type


Years of Experience
At least 5 years

Tech Stacks

Job Description

We’re looking for Data Scientists to work on our business products to help shape the future of what we build at Facebook. You will support the two hundred million businesses, including ten million advertisers, who depend on us for their livelihoods. You will create the tools that power Facebook's core revenue streams, including new business models, that are used by businesses, big and small, all around the world. You will enjoy working with one of the richest data sets in the world, cutting edge technology, and the ability to see your insights turned into real products on a regular basis. You should have a background in a quantitative or technical field, experience working with large data sets, and experience in data-driven decision making. You are focused on results, a self-starter, and have demonstrated success in using analytics to drive the understanding, growth, and success of a product.


  • Apply your expertise in quantitative analysis, data mining, and the presentation of data to see beyond the numbers and understand how our users interact with our business products. Users include all forms of businesses, salespeople, developers, and more
  • Partner with Product and Engineering teams to solve problems and identify trends and opportunities
  • Inform, influence, support, and execute our product strategy, decisions, and launches
  • The Data Scientist Analytics role has work across the following four areas:
  • Product Operations: Forecasting and setting product team goals. Designing and evaluating experiments and/or causal inference studies. Monitoring key product metrics, understanding root causes of changes in metrics. Building and analyzing dashboards and reports. Building key data sets to empower operational and exploratory analysis, evaluating and defining metrics
  • Exploratory Analysis: Proposing what to build in the next roadmap. Understanding ecosystems, user behaviors, and long-term trends. Identifying new levers to help move key metrics. Building models of user behaviors for analysis or to power production systems
  • Product Leadership: Influencing product teams through presentation of data-based recommendations. Communicating state of business, experiment results, etc. to product teams. Spreading best practices to analytics and product teams, Leading cross-functionally on defining and executing on analyses, including across other data scientists, data engineers, software engineers, user researchers, and others
Minimum Qualification

  • 3+ years of experience with exploratory analysis and market sizing to inform development of new product strategy
  • 3+ years of experience communicating the results of analyses and aligning cross-functional teams to influence the strategy
  • Bachelors/Masters Degree with 5+ years (or PhD with 3+ years) of experience in the following areas:
  • Experience with applied statistics, including causal inference and experimentation (i.e. A/B testing) in an industry setting, especially for complex multi-sided ecosystems and/or low sample size environments
  • Experience doing complex quantitative analysis and working with distributed (i.e. Hive, Hadoop or similar databases) or highly complex dataset
  • Experience with data querying languages (e.g. SQL), scripting languages (e.g. Python), or statistical/mathematical software (e.g. R, SAS, MATLAB)
  • 3+ years of experience providing analytical support in 1+ of these backgrounds: B2B/Enterprise companies, Advertising Technology/Digital Advertising, Financial Technology, Platforms/Marketplaces, Front-end tools for business customers, Quantitative Consulting
  • 3+ years of experience in one or more of these types of analytics: Back-end quality/Performance & Reliability, Risk & Integrity, Infrastructure system design, Marketing Analytics
Preferred Qualification

  • 2+ years of leadership experience in supporting a technical team
  • Applied experience of machine learning algorithms (e.g. issue detection, recommendation systems, search ranking, flow optimisation)

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