Data Scientist

About Meta

Meta builds technologies that help people connect, find communities, and grow businesses. Starting with Facebook in 2004, Meta has expanded with apps like Messenger, Instagram, and WhatsApp, empowering billions globally. Now, Meta is exploring augmented and virtual reality to build the next evolution of social technology, aiming to go beyond current digital connection capabilities and physical constraints.

About the Position

Introduction

Meta is looking for a skilled and motivated Data Scientist to join their Infrastructure Data Centers team. This role involves partnering with stakeholders and other data science functions to extract value from Meta's Infrastructure Data Centers' data. The ideal candidate will work on diverse data science projects, focusing on operational efficiency, strategic analysis, ML model measurement, automation, and building analytical solutions with software developers.

Responsibilities

Collaborate with cross-functional data and business teams to define problems, analyze impacts, identify opportunities, and develop solutions that improve decision-making and efficiency.
Translate business challenges into data-driven problems and design appropriate data science solutions, including metrics and analytics, business intelligence, and experimentation.
Identify operational gaps, build analytical models to derive insights, and support decision-making across organizational leadership.
Design and implement statistical models such as hypothesis testing, forecasting, statistical process control, and simulation to influence critical business decisions and Data Center operations.
Utilize expertise in statistics, machine learning, optimization, and automation to develop analytics solutions.
Work closely with stakeholders, data engineers, and program/product managers to ensure seamless progress.
Educate and influence stakeholders to enhance operational efficiency with empathy.
Recommend process improvements based on operational data and user behavior to boost business performance.
Mentor team members on data science best practices.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience.
6+ years of experience (or 2+ years post-Ph.D.) with advanced SQL in big data environments (e.g., Hive, Presto, Spark) and data modeling.
6+ years of experience (or 2+ years post-Ph.D.) managing and analyzing large-scale data using Python, R, or similar languages.
6+ years of experience (or 2+ years post-Ph.D.) working with visualization tools such as Tableau, PowerBI, or similar.
6+ years of experience (or 2+ years post-Ph.D.) analyzing and interpreting data, developing metrics, drawing conclusions, recommending actions, and reporting results across stakeholders.
Experience in enhancing data collection procedures, data processing, cleansing, and verifying the integrity of data used for analysis.
Proven track record of managing and leading cross-functional projects and teams.
Solid understanding of machine learning techniques and algorithms.
Hands-on programming experience in one or more of: AI/ML, LLM, NLP, Statistical modeling.
Proficient in statistical analysis and experimental design.

Nice to Have

Knowledge of simulation and optimization techniques.
Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements).
Technical knowledge of data center operations.
Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews).
Great communication and storytelling skills to influence all organizational levels (engineers, executives, and cross-functional teams) to drive business decisions.
Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies.
Master's degree in Computer Science, Engineering, Mathematics, Statistics, Operations Research, or a related analytical field.