Meta is seeking a skilled and motivated Data Scientist to join our Infrastructure Data Centers team. As a lead data scientist, you will partner with stakeholders, program managers, and other data science functions to translate Meta’s Infrastructure Data Centers’ data into value. You will have the opportunity to work on a wide range of data science projects, such as developing an analytics program to drive operational efficiency, conducting strategic analysis to facilitate decision making, measuring Machine Learning models and automation to increase predictive accuracy and reduce manual effort, and working with software developers to build analytical solutions. The ideal candidate will have experience defining scope and driving outcomes in environments with evolving requirements. Additionally, you will have a proven track record of developing and delivering analytics programs that drove measurable business impact in developing similar analytics programs and machine learning solutions. This position is part of the Infrastructure Data Centers organization.
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, 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
Qualifications
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience 6+ years of experience (2 years+ of experience post Ph.D.) with advanced SQL in big data environments (e.g., Hive, Presto, Spark) and data modeling 6+ years of experience (2 years+ of experience post Ph.D.) managing and analyzing large-scale data using Python, R, or similar languages 6+ years of experience (2 years+ of experience post Ph.D.) of working with visualization tools such as Tableau, PowerBI, or similar 6+ years of experience (2 years+ of experience 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 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