Data Scientist

Meta · Big Tech · Menlo Park, CA

Data Scientist role at Meta focused on quantitative analysis, data mining, and building models of user behavior to inform product decisions and power production systems. Requires a PhD and practical experience in experimental design, data analysis, and applying ML/AI techniques.

What you'd actually do

Utilize 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.
Responsible for defining key product metrics, forecasting and setting product team goals.
Design and evaluate experiments.

Skills

Required

PhD in Statistics, Mathematics, Physics, Computer Science, Sociology or related field
Quantitative analysis
Clustering
Regression techniques
Pattern recognition
Descriptive statistics
Inferential statistics
Experimental design
Hypothesis testing
SQL
Python
ETL processes
Data mining
Large-scale relational databases
Data risk management
Machine learning techniques
Artificial Intelligence

Nice to have

Guardrail metrics

What the JD emphasized

PhD degree (or foreign degree equivalent) in Statistics, Mathematics, Physics, Computer Science, Sociology or related field.
Quantitative analysis, including clustering and regression techniques, pattern recognition, descriptive and inferential statistics, and experimental design (including hypothesis testing)
Practical experience using querying (e.g. SQL) and scripting (e.g. Python) languages to orchestrate Extract, Transform, and Load (ETL) processes on large-scale relational databases, and performing data mining on highly-complex datasets for the aforementioned analyses
Practical experience in experimental design, setup and analysis, along with defining key performance indicator and guardrail metrics to enable product teams to run product tests and both track and measure progress against team goals
Performing structured audits of logged data to recommend product improvements and guide internal teams to prioritize highest impact work based on top business needs
Practical experience in data risk management, including the design of automated processes to rapidly identify and remediate key gaps that could prevent product teams from hitting their goals
Presenting qualitative and quantitative information to technical and non-technical audiences, including the application of machine learning techniques and Artificial Intelligence

Other signals

Building models of user behaviors for analysis or to power production systems
design and evaluate experiments
Quantitative analysis, data mining, and the presentation of data
Define key product metrics
Apply machine learning techniques and Artificial Intelligence

Read full job description

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

Utilize 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. Responsible for defining key product metrics, forecasting and setting product team goals. Design and evaluate experiments. Monitor key product metrics, understanding root causes of changes in metrics. Build and analyze dashboards and reports. Build key data sets to empower operational and exploratory analysis. Evaluate and define metrics. Perform exploratory analysis. Understand ecosystems, user behaviors, and long-term trends. Identify new levers to help move key metrics. Building models of user behaviors for analysis or to power production systems. Influence Product teams through presentation of data-based recommendations. Communicate state of business, experiment results to Product teams.

Qualifications

PhD degree (or foreign degree equivalent) in Statistics, Mathematics, Physics, Computer Science, Sociology or related field. Requires completion of a university-level course, research project, internship, or thesis in the following: Quantitative analysis, including clustering and regression techniques, pattern recognition, descriptive and inferential statistics, and experimental design (including hypothesis testing) Practical experience using querying (e.g. SQL) and scripting (e.g. Python) languages to orchestrate Extract, Transform, and Load (ETL) processes on large-scale relational databases, and performing data mining on highly-complex datasets for the aforementioned analyses Practical experience in experimental design, setup and analysis, along with defining key performance indicator and guardrail metrics to enable product teams to run product tests and both track and measure progress against team goals Performing structured audits of logged data to recommend product improvements and guide internal teams to prioritize highest impact work based on top business needs Practical experience in data risk management, including the design of automated processes to rapidly identify and remediate key gaps that could prevent product teams from hitting their goals and Presenting qualitative and quantitative information to technical and non-technical audiences, including the application of machine learning techniques and Artificial Intelligence