Staff Applied Scientist, Trust

LinkedIn; California (MO); Hybrid; USD 175,000 - 287,000; Full time

Benefits offered by this job

Hybrid work model

Health benefits

Stock options

Job summary

LinkedIn, the world’s largest professional network, seeks an outstanding data scientist to drive advanced ML, causal inference, and experimentation across core products. You will build scalable models, work with cross-functional teams, and shape data-driven decisions that impact over a billion members.

The role blends rigorous science with business judgment, developing methods from the ground up and delivering measurable product improvements at scale. Hybrid work from Sunnyvale, CA.

Qualifications

• Bachelor's degree in a quantitative discipline (Statistics, OR, CS, Economics, Engineering, etc).
• 5+ years of industry or relevant academia experience.
• Proficiency in at least one programming language (e.g., Python, R, Java, Scala/Spark).
• Experience in applied statistics and statistical modeling in Python or R.

Responsibilities

• Independently identify and frame complex data science problems with high-impact opportunities.
• Lead advanced analyses, ML, causal inference, and experimentation to inform strategy.
• Leverage AI tools to increase day-to-day productivity.
• Design scalable, replicable modeling approaches with measurable business impact.
• Evaluate approaches from literature and internal repos to choose best path forward.

Skills

ML/AI systems

Statistics

Programming languages

Education

Bachelor's degree in quantitative discipline

Job description

LinkedIn is the world's largest professional network, built to create economic opportunity for every member of the global workforce. Our products help people make powerful connections, discover exciting opportunities, build necessary skills, and gain valuable insights every day. We're also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture that's built on trust, care, inclusion, and fun - where everyone can succeed.

Join us to transform the way the world works.

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.

Team description

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.

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.

Location

This role will be based in Sunnyvale. 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.

Basic Qualifications

• 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)

Preferred Qualifications

• 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.

Suggested Skills

• ML or AI system development and deployment
• Statistics
• Programming Languages

You will Benefit from our Culture

We strongly believe in the well-being of our employees and their families. That is why we offer generous health and wellness programs and time away for employees of all levels. LinkedIn is committed to fair and equitable compensation practices.

You will Benefit from our Culture

We strongly believe in the well-being of our employees and their families. That is why we offer generous health and wellness programs and time away for employees of all levels. LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $175,000 - $287,000. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor. The total compensation package for this position may also include annual performance bonus, stock, benefits and/or other applicable incentive compensation plans.

For more information, visit https://careers.linkedin.com/benefits.

Equal Opportunity Statement

We seek candidates with a wide range of perspectives and backgrounds and we are proud to be an equal opportunity employer. LinkedIn considers qualified applicants without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other legally protected class.

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