About this role
Full Time Data Scientist in machine learning at Meta in Menlo Park, CA. Apply directly through the link below.
At a glance
- Work mode
- Office
- Employment
- Full Time
- Location
- Menlo Park, CA
Core stack
- Machine Learning
- Cross-functional
- A/B Testing
- Distributed
- Forecasting
- Python
- SQL
- ETL
Quick answers
What are the qualifications?
Bachelor’s degree (or foreign equivalent) in Mathematics, Statistics or a related field
What skills are required?
Machine Learning, Cross-functional, A/B Testing, Distributed, Forecasting, Python, SQL, ETL.
Meta is hiring for this role. Visit career page
Menlo Park, United States
Meta Platforms, Inc. (Meta), formerly known as Facebook Inc., builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps and services like Messenger, Instagram, and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. To apply, click “Apply to Job” online on this web page.
Responsibilities
Work with large and complex data sets to solve a wide array of challenging problems using different analytical and statistical approaches.
* Apply technical expertise with quantitative analysis, experimentation, data mining, and the presentation of data to develop strategies for our products that serve billions of people and hundreds of millions of businesses.
* Identify and measure success of product efforts through goal setting, forecasting, and monitoring of key product metrics to understand trends.
* Define, understand, and test opportunities and levers to improve the product, and drive roadmaps through your insights and recommendations.
* Partner with Product, Engineering, and cross-functional teams to inform, influence, support, and execute product strategy and investment decisions.
Qualifications
Bachelor’s degree (or foreign equivalent) in Mathematics, Statistics or a related field
* Requires completion of one graduate-level course, one research project, or one internship involving the following:
* Performing quantitative analysis including data mining on highly complex data sets
* Scripting language: Python
* Data querying using SQL, scripting using Python, and statistical processing using R or similar mathematical software
* Statistical or mathematical software including one of the following: R, SAS, or Matlab
* Applied statistics or experimentation, such as A/B testing, in an industry setting
* Machine learning techniques
* ETL (Extract, Transform, Load) processes
* Relational databases
* Large-scale data processing infrastructures using distributed systems
* and Quantitative analysis techniques, including one of the following: clustering, regression, pattern recognition, or descriptive and inferential statistics
Responsibilities
Work with large and complex data sets to solve a wide array of challenging problems using different analytical and statistical approaches.
* Apply technical expertise with quantitative analysis, experimentation, data mining, and the presentation of data to develop strategies for our products that serve billions of people and hundreds of millions of businesses.
* Identify and measure success of product efforts through goal setting, forecasting, and monitoring of key product metrics to understand trends.
* Define, understand, and test opportunities and levers to improve the product, and drive roadmaps through your insights and recommendations.
* Partner with Product, Engineering, and cross-functional teams to inform, influence, support, and execute product strategy and investment decisions.
Qualifications
Bachelor’s degree (or foreign equivalent) in Mathematics, Statistics or a related field
* Requires completion of one graduate-level course, one research project, or one internship involving the following:
* Performing quantitative analysis including data mining on highly complex data sets
* Scripting language: Python
* Data querying using SQL, scripting using Python, and statistical processing using R or similar mathematical software
* Statistical or mathematical software including one of the following: R, SAS, or Matlab
* Applied statistics or experimentation, such as A/B testing, in an industry setting
* Machine learning techniques
* ETL (Extract, Transform, Load) processes
* Relational databases
* Large-scale data processing infrastructures using distributed systems
* and Quantitative analysis techniques, including one of the following: clustering, regression, pattern recognition, or descriptive and inferential statistics