Staff Applied Scientist

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. 

LinkedIn’s Data Science and Applied Science teams use data, experimentation, causal inference, machine learning, and AI to solve important product and business problems. With more than 1 billion members globally and products that span both consumer and enterprise use cases, LinkedIn offers scientists the opportunity to work on problems that directly shape member experience, customer value, growth, and monetization. 

We are looking for a strong individual contributor who can bring rigorous science to practical problems. In this role, you will work across areas such as experimentation, causal inference, prediction, measurement, optimization, personalization, and large-scale machine learning. You will be expected to go deep technically, build methods and models that fit real product needs, and turn promising ideas into tools, platforms, and systems that can be used at scale. 

The ideal candidate combines technical depth with strong product and business judgment. You should be comfortable developing methods from the ground up, adapting existing techniques to new problems, and working closely with cross-functional partners to make better decisions and deliver measurable impact. The work may span areas such as auctions, matching, market design, personalization, AI-powered product experiences, and other high-impact systems across LinkedIn. 

Responsibilities

• Independently identify and frame complex, ambiguous data science problems, surfacing high-impact opportunities for product and solution improvement. 
• Lead advanced analyses, experimentation, machine learning, and causal inference efforts to inform product strategy and data-driven decisions. 
• Leverage AI tools in day-to-day workflows to increase productivity 
• Design and refine modeling approaches by evaluating technical tradeoffs and developing scalable, replicable solutions with measurable business and product impact. 
• Research and evaluate historical approaches, internal repositories, external literature, and novel methods to determine the best path forward when standard solutions are insufficient. 
• Establish or improve methodological frameworks, review peer work, and uphold high standards for scientific rigor, reproducibility, fairness, and analytical integrity. 
• Develop and implement advanced data science methodologies and ML models that are accurate, unbiased, robust, and aligned with scientific best practices. 
• Build and improve production-ready data science solutions while accounting for latency, cost, infrastructure, reliability, and maintainability constraints. 
• Partner cross-functionally with Product, Engineering, AI, and leadership to translate strategic priorities into clear data science roadmaps and deliverables. 
• Influence stakeholders through clear insights, recommendations, and advocacy for AI/ML methodologies and data-driven innovation.

Department: Engineering
Experience Level: Mid-Senior Level