• Client is Meta • 7-8 years of experience as a data scientist • Lead experience • FULLY REMOTE AND LONG TERM CONTRACT Must Haves: Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Engineering, or a related field. 7+ years of experience in data science, analytics, or a related field. 2+ years of experience in a leadership or mentorship role. Proficiency in SQL and at least one programming language (Python or R preferred). Expertise in data visualization tools (e.g., Tableau, Power BI, or internal equivalents). Strong background in data modeling, ETL processes, and data warehousing. Familiarity with cloud platforms and big data technologies (e.g., Hive, Presto, Spark). Day to Day: Insight Global is seeking an experienced and strategic Lead Data Scientist to join the Edge Network Services (ENS) division. This role will lead the M360 Analytics project, a critical initiative aimed at enhancing operational efficiency through data-driven decision-making, robust metrics development, and automation of key financial tracking systems. As a senior member of the ENS Analytics team, you will drive the design of scalable data solutions, oversee the development of L1/L2/L3 metrics, and lead the automation of Opex and Capex tracking. You will also mentor junior data scientists, collaborate with cross-functional stakeholders, and ensure the delivery of high-impact insights through dashboards, reports, and self-service analytics tools. Key Responsibilities: Lead the design and implementation of scalable data pipelines and analytics infrastructure. Define and govern L1/L2/L3 operational metrics to monitor and optimize ENS performance. Oversee the automation of Opex and Capex tracking systems to support financial planning. Mentor and guide junior data scientists and analysts on best practices and project execution. Collaborate with engineering, finance, and operations teams to align data strategies with business goals. Develop and maintain dashboards and self-service tools for real-time data access and decision-making. Ensure data quality, consistency, and governance across all analytics outputs. Conduct advanced statistical analysis and modeling to uncover insights and drive strategic decisions. Present findings and recommendations to senior leadership and stakeholders.