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LinkedIn's strong brand and Bangalore metro increase candidate interest despite seniority and domain specificity.
Core ML and data engineering skills transfer well, but support operations domain knowledge moderately matters.
Requires 8+ years, SQL, large-scale data warehouse and production ML deployment experience, enforcing strict technical filters.
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Own end-to-end lifecycle of complex operational analytics projects including prototyping AI-driven concepts and scaling automated systems for LinkedIn Global Support.
Develop scalable data products and AI/ML models, operationalize and scale them from prototype to production ensuring reliability and business impact.
Lead analytical projects and manage analytics team to identify operational opportunities, improve scalability, efficiency, and performance, and provide actionable insights to senior GTM and Operations leadership.
8+ years experience working with data systems/tools in a business setting or equivalent experience.
Proficiency in SQL and experience with large-scale data warehouses (e.g., Presto, Trino, Spark SQL).
Experience architecting, building, and deploying machine learning models or automated data solutions into production.
BA/BS degree in a quantitative field (e.g., Computer Science, Statistics, Operations Research, Engineering) or equivalent practical experience.
Combines strong technical and analytical skills with strategic storytelling to influence senior business and operational leadership.
Experience leading complex, ambiguous technical projects from inception to deployment, particularly with AI tools applied to business decisions.
Deep understanding of support financial and operational metrics, and experience partnering cross-functionally with teams such as Finance, Product, Engineering, and Operations.