Staff Applied Analytics Analyst

LinkedIn•Sunnyvale, CA

46d•Hybrid

About The Position

This role will be based in Sunnyvale, Mountain View or San Francisco, CA. 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. We are looking for a Staff Applied Analytics Analyst (IC4) to help us reach our AI/ML and agentic ambitions in our People Analytics function within Global Talent Organization. You will be a driving force in advancing our applied analytics capabilities, leveraging cutting-edge AI/ML technologies and agentic frameworks to deliver actionable insights and scalable solutions to help us democratize workforce insights and people intelligence. You'll work cross-functionally to architect, implement, and deploy production-grade AI tools, while making complex analytics accessible to non-technical stakeholders. Your forward-thinking approach will help shape our AI strategy and keep our team at the forefront of innovation. To be successful in this role you need to be highly analytical, have strong intellectual curiosity, and be comfortable responding to and structuring ambiguous requests. Your stakeholders will not always know what is possible, so it is important that you ask effective questions to get clarity in addition to managing expectations on what you can deliver. You must be able to prioritize multiple tasks in a fast-paced environment. Exceptional attention to detail, strong analytical and development skills, proactive communication skills, and an ability to meet tight deadlines will be critical for success in this role. You'll be part of a team that values innovation, collaboration, and continuous learning. Your work will directly impact our People Analytics strategy and help shape the future of democratization of people intelligence across the organization.

Requirements

BA/BS in Computer Science, Data Science, Information Technology, Applied Statistics, Engineering, or related field.
7+ years of experience in data analytics, applied data science, or a similar role.
Demonstrated experience in deploying production-grade AI/ML models.
Experience with SQL, Python, and R coding.
Experience with data visualization tools (Power BI, Visier, etc.).

Nice To Haves

Experience with agentic frameworks (LangGraph, LangChain, Autogen, CrewAI).
Familiarity with enterprise AI tools (Visier Vee, Copilot Studio, Glean Agents, ChatGPT Enterprise).
Familiarity with LLM fine-tuning and hands-on experience with prompt engineering.
3+ years of experience working with both non-technical stakeholders and engineering teams.
Strong communication skills, with the ability to explain technical concepts to non-technical audiences.
Forward-thinking mindset with a passion for staying ahead of AI industry trends.

Responsibilities

Design, implement and refine production of AI/ML solutions, leveraging enterprise platforms and tools to ensure scalability, reliability, and performance.
Oversee solution lifecycle management, including testing for production releases, troubleshoot issues, monitoring performance metrics, and ensuring compliance with deployment standards.
Evaluate, build, and orchestrate AI tools and agent flows using platforms such as Visier Vee, Copilot Studio, Glean Agents, and other enterprise AI tools.
Leverage and experiment with leading AI solutions (ChatGPT Enterprise, Claude, Gemini, etc.) to enhance analytics delivery.
Translate stakeholder requirements to analysis plans, conduct complex data analysis, develop actionable insights, and communicate findings to stakeholders using advanced visualization tools (Power BI, Visier, etc.).
Document methodologies, findings, and insights to ensure clarity, reproducibility, and knowledge sharing.
Collaborate with senior leaders, cross-functional teams, and other stakeholders to align analytics solutions with evolving business needs and support implementation of new solutions.
Translate technical concepts for non-technical users, fostering understanding and adoption of applied analytics solutions.
Ensure data and project work comply with governance policies and best practices.
Identify opportunities to scale manual reports and analytics solutions, prioritizing based on impact and feasibility.
Stay abreast of emerging AI trends and proactively recommend solutions and perspectives to address complex business challenges.
Ensure diversity, inclusion, and belonging are embedded in the design of our analytics initiatives.