Senior Applied Scientist

This role will be based in Mountain View, 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. 

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: 

• Support the identification of product and data solution improvement opportunities through structured analysis and investigation. 

• Leverage AI tools in day-to-day workflows to increase productivity 

• Conduct analyses, experiments, and modeling work to evaluate product performance and uncover actionable insights. 

• Research prior work, documentation, and relevant literature to inform analytical approaches. 

• Participate in reviews of methodologies, tools, and outputs to improve scientific rigor and consistency. 

• Build, evaluate, and refine machine learning models or statistical approaches using established data science best practices. 

• Implement data science solutions that improve data extraction, interpretation, and decision-making under guidance from senior team members. 

• Apply standards for accuracy, fairness, robustness, and reproducibility in analyses and modeling work. 

• Collaborate with Engineering, AI, Product, and other partners to understand business goals and translate them into analytical tasks and ML models. 

• Communicate findings, recommendations, and model results clearly to stakeholders.