Staff Applied Scientist, Trust

LinkedIn•Mountain View, CA

4d•Hybrid

About The Position

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. The Trust Applied Science team sits at the intersection of rigorous measurement and cutting-edge AI, developing quantitative methods—including agentic and LLM-based models—to make LinkedIn a safer, more trusted platform. We tackle complex problems like measuring the prevalence of abuse and automated activity, evaluating the quality of our enforcement systems, and building new ways to understand trust at scale. Our work gives teams across LinkedIn the insights and tools they need to identify emerging risks, improve enforcement, and protect more than 1 billion members worldwide.

Requirements

Bachelor's Degree in a quantitative discipline: Statistics, Operations Research, Computer Science, Informatics, Engineering, Applied Mathematics, Economics, etc.
5+ years of industry or relevant academia experience
Background in at least one programming language (eg. R, Python, Java, Ruby, Scala/Spark or Perl)
Experience in applied statistics and statistical modeling in at least one statistical software package, (eg. R, Python)

Nice To Haves

7+ years of industry or relevant academia experience
Doctorate in Statistics, Biostatistics, Applied Mathematics, Engineering, Operations Research, Economics, Informatics, Computer Science, Data Science or related field.
ML or AI system development and deployment
Statistics
Programming Languages

Responsibilities

Independently identify and frame complex, ambiguous data science problems, surfacing high-impact opportunities for product and solution improvement.
Lead advanced analyses, machine learning, causal inference, and experimentation 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, LLM/agentic systems 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.