Meta is seeking a lead data scientist to translate Meta's Infrastructure Data Centers' data into value, driving operational efficiency, facilitating decision making, and increasing predictive accuracy through machine learning and automation.
Requirements
- advanced SQL in big data environments (e.g., Hive, Presto, Spark) and data modeling
- managing and analyzing large-scale data using Python, R, or similar languages
- working with visualization tools such as Tableau, PowerBI, or similar
- analyzing and interpreting data, developing metrics, drawing conclusions, recommending actions, and reporting results across stakeholders
- enhancing data collection procedures, data processing, cleansing, and verifying the integrity of data used for analysis
- 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
Responsibilities
- 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 a seamless progress
- Recommend process improvements based on operational data and user behavior to boost business performance
- Mentor team members on data science best practices
Other
- Collaborate with cross-functional data and business teams to define problems, analyze impacts, identify opportunities, and develop solutions that improve decision-making and efficiency
- Educate and influence stakeholders to enhance operational efficiency with empathy
- Proven track record of managing and leading cross-functional projects and teams
- Technical knowledge of data center operations
- Communication and storytelling skills to influence all organizational levels (engineers, executives and cross functional teams) to drive business decisions
