Data Scientist

LinkedIn•Mountain View, CA

20h•Hybrid

About The Position

LinkedIn’s Data Science team uses member and customer behavior to inform product decisions across its professional network. The team designs experiments, investigates changes in metrics, and partners with Product Management and Engineering to develop features, including AI-powered experiences. This is an early-career role with mentorship from more senior Data Science individual contributors. The Data Scientist investigates product usage with SQL and Python, interprets A/B test results, and creates dashboards and analysis documents to inform decisions. The role also involves examining the tracking and algorithms of AI systems and applying agentic data science to generate insights. With experience, the role takes on more ambiguous problems and helps shape team metrics. The position performs experiments and causal analyses, supports metric definition and data-driven decision-making, and develops models and analyses to explain metric relationships and generate actionable insights. It also works with partners to evaluate AI/LLM systems and agentic product performance, assess model performance, prototype and validate scalable data solutions, apply data science best practices, answer basic data questions, identify patterns in user behavior, and communicate findings through compelling data-centric stories.

Requirements

Bachelor’s degree in Applied Mathematics, Computer Science, Data Science, Economics, Engineering, Informatics, Statistics, or a related field.
At least 1 year of experience in data science, such as managing structured and unstructured data, applying statistical techniques, and reporting results, or equivalent experience.
Background in at least one programming language, such as R, Python, Java, Ruby, Scala/Spark, or Perl.

Nice To Haves

Master’s degree or doctorate in Applied Mathematics, Computer Science, Data Science, Economics, Engineering, Informatics, Statistics, or a related field.
Experience with experimentation platforms, causal inference, product domain expertise, or evaluating AI/LLM-powered features.
Written communication and structured thinking abilities.
Curiosity about how products work and why members behave the way they do.

Responsibilities

Perform data-driven experiments and causal analyses to test and validate product ideas or go-to-market strategies, develop ecosystem understanding, and monitor products or systems.
Analyze data to support metric definitions, data-driven target setting, and decision-making.
Leverage AI tools in day-to-day workflows to increase productivity.
Develop models and causal analyses, such as panel analysis and A/B testing, to investigate metric relationships and create actionable insights within basic product domains.
Collaborate with partners to define metrics and frameworks for evaluating AI/LLM systems and agentic product performance.
Support AI system roadmaps by assessing model performance.
Prototype data solutions and, with partners such as Data Science, AI, and Engineering teams, operationalize, build, and validate scalable tools, applications, or solutions for internal customers, with guidance.
Apply data science best practices and principles throughout development of customized or scalable data solutions.
Consult with partners on basic data questions to enable data democratization.
Use data mining techniques to identify user behavior patterns and provide insights and data-informed recommendations to internal partners.
Create data-centric stories based on analytical findings to support internal customers’ decision-making.
Use SQL and Python to investigate product usage, interpret A/B test results, and create dashboards and analysis documents.
Investigate the tracking and algorithms of AI systems and use agentic data science to develop insights.