Operations Analytics & Insights, Tech Lead

LinkedIn is the world's largest professional network, built to create economic opportunity for every member of the global workforce. Our products help people make powerful connections, discover exciting opportunities, build necessary skills, and gain valuable insights every day. We're also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture that's built on trust, care, inclusion, and fun – where everyone can succeed.

Join us to transform the way the world works.

At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team. 

The Strategy, Planning & Operations (SPO) team is a fast-moving group essential for setting the direction of LinkedIn’s Member & Customer Support, resolving complex cross-functional challenges, and ensuring a world-class customer experience.   

We are looking for Ops Analytics & Insights – Tech Lead, who can be a steward of global operational analytics and performance metrics across LinkedIn Global Support and Product Operations. You will be responsible for the end-to-end lifecycle of our most complex projects, from prototyping new AI-driven concepts to deploying scalable, automated systems. You will learn the business inside-out, and lead engagements with business leaders, Strategic Finance, Product, Engineering, and Operations teams to identify opportunities, design, and deliver results. 

The ideal candidate will combine technical & analytical strengths with the strategic storytelling needed to influence GTM leadership, as well as bring deep expertise in translating complex business requirements into clear, actionable insights. Our solutions aim to provide the right information at the right time to enable the best outcomes – for our business, and our billion+ members and customers.  

Responsibilities: 

• Develop scalable data products and AI/ML models, integrating modern AI techniques to solve business problems across LinkedIn’s Global Support organization 
• Work with upstream technology partners to operationalize and scale models from prototype to production, ensuring reliability and business impact 
• Identify operational opportunities, gaps, and trends through the work to improve scalability, efficiency, and performance 
• Make business recommendations & translate highly complex technical concepts and model outputs into clear, concise, and actionable narratives for senior GTM and Operations leadership   
• Lead analytical projects and grow a thriving analytics team function 

Basic Qualifications 

• 8+ years of experience working with data systems or tools in a business setting or equivalent experience. 
• Experience in SQL and experience with large-scale data warehouses (e.g., Presto, Trino, Spark SQL).  
• Experience architecting, building, and deploying machine learning models and/or automated data solutions into a production environment.  
• BA/BS degree in a quantitative field (e.g., Computer Science, Statistics, Operations Research, Engineering) or equivalent practical experience. 

Preferred Qualifications 

• Experience with technologies and frameworks (e.g., Claude Code, Cursor, etc.) 
• Experience with modern data stack and automation tools (e.g., Airflow, Databricks, dbt) 
• Deep understanding of Support financial and operational metrics (e.g., resolution time, SLA, customer health, etc.)  
• A self-directed, intellectually curious mindset with a proven ability to lead ambiguous, complex technical projects from 0 to 1 
• A passion for AI coupled with a strong, opinionated perspective on how to strategically apply AI tools and capabilities to drive business decisions in a fast-moving environment 

Suggested Skills 

• AI fluency  
• Data analysis  
• Data visualization  
• Data storytelling   

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