This role is based in either our Sunnyvale, San Francisco, New York, or Chicago offices.
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.
As part of the Product Operations organization, you will leverage one of the richest proprietary datasets in the world to lead high-impact initiatives that deepen intelligence across our members and customers and elevate product quality.
We are seeking a talented and driven technical leader who excels at delivering world-class AI-powered analytic solutions, actionable insights, and measurable business impact. You will design and implement data-driven initiatives that create both immediate value and long-term strategic advantage.
You bring strong technical acumen, product judgment, and business savvy, with applied expertise in modern AI tools and techniques. You combine analytical rigor with a growth mindset to generate scalable, data-driven learnings. You are comfortable navigating large, complex, and ambiguous data ecosystems and influencing cross-functional stakeholders through strong relationship-building and collaboration.
This is a hands-on “player-coach” leadership role. You will architect solutions, write production-grade code using AI tools, and mentor a team of 3–4 data scientists and analytics engineers.
You will own the end-to-end technical lifecycle of complex initiatives — from prototyping AI-driven concepts to deploying scalable, automated systems. Combining the analytical depth of a principal data scientist with executive-level storytelling, your primary goal is to architect and build agentic workflows, predictive models, and automated systems that fundamentally transform how operations teams operate.
Responsibilities
Architect & Build
Lead the hands-on design, development, and deployment of scalable data products, AI/ML models (e.g., member friction, customer impact, anomaly detection), and GenAI-powered agentic workflows.
Technical Strategy
Define the technical roadmap and architecture for the Product Operations Applied AI pillar, including key decisions on frameworks, tooling, and practices.
End-to-End Automation
Write high-quality, production-ready Python and SQL to build and maintain automated data pipelines, advanced analytics, and insight-delivery systems.
Applied AI Integration
Serve as the subject matter expert on applying modern AI, LLMs, and ML techniques (e.g., RAG, fine-tuning) to solve GTM business problems in partnership with Data Science and Engineering teams.
Technical Mentorship
Mentor and develop a team of data analysts and engineers, setting a high bar for technical rigor, code quality, and engineering best practices through a lead-by-example approach.
Executive Storytelling
Translate complex technical concepts and model outputs into clear, concise, and actionable narratives for senior GTM and Operations leadership.
Cross-Functional Partnership
Collaborate with Product, Engineering, and Data Science teams to operationalize and scale models from prototype to production, ensuring reliability and measurable business impact.