Data Scientist - Product

Data Scientist – Product

Requires a Master’s degree in Computer Science, Engineering, Information Systems, Analytics, Mathematics, Physics, Applied Sciences, or a related field and six months of experience in the job offered or in a computer-related role.
Requires six months of experience in the following:
1. Performing quantitative analysis including data mining on highly complex data sets.
2. Data querying language: SQL.
3. Scripting language: Python.
4. Statistical or mathematical software including one of the following: R, Python, SAS, or Matlab.
5. Applied statistics or experimentation, such as correlation analysis, A/B testing, in an industry setting.
6. Complex machine learning techniques such as neutral network and XGboost.
7. ETL (Extract, Transform, Load) processes.
8. Relational databases.
9. Large-scale data processing infrastructures using distributed systems.
10. Quantitative analysis techniques, including one of the following: clustering, regression, pattern recognition, or descriptive and inferential statistics.
11. Version control for reproducibility, such as Git.

Collect, organize, interpret, and summarize statistical data in order to contribute to the design and development of Meta products.
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 both our consumer and business products.
Partner with Product and Engineering teams to solve problems and identify trends and opportunities.
Inform, influence, support, and execute our product decisions and product launches.
May be assigned projects in various areas including, but not limited to, product operations, exploratory analysis, product influence, and data infrastructure.
Work on problems of diverse scope where analysis of data requires evaluation of identifiable factors.
Demonstrate good judgment in selecting methods and techniques for obtaining solutions.
Improve ranking retrieval stages to cover more users and build more accurate timing models.
Improve Unified Value Model with long term value, short term value, and user frustration predictors.

No preferred qualifications provided.