Data Scientist

LinkedIn•Mountain View, CA

1d•$101,000 - $167,000•Hybrid

About The Position

LinkedIn is hiring an early-career Data Scientist to join a product team and grow with mentorship from senior Data Science individual contributors. The role uses member and customer behavior data to inform product decisions across LinkedIn’s professional network, including AI-powered experiences. The Data Scientist writes SQL and Python to investigate product usage, interprets A/B test results, and builds dashboards and analysis documents that support decisions. The role also involves investigating the tracking and algorithms of AI systems and using agentic data science to generate insights. As experience grows, the Data Scientist will take on more ambiguous problems and help shape team metrics.

Requirements

Bachelor's degree in Applied Mathematics, Computer Science, Data Science, Economics, Engineering, Informatics, Statistics, or a related field.
At least 1 year of data science experience, 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.
Data science, data storytelling, statistics, and strategic thinking skills.

Responsibilities

Conduct data-driven experiments and causal analyses to test product ideas and go-to-market strategies, develop ecosystem understanding, and monitor products or systems.
Analyze data to support metric definition, target setting, and data-driven 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 identify drivers of metric relationships and produce actionable insights in basic product domains.
Collaborate with partners to define metrics and frameworks for evaluating AI/LLM systems and agentic product performance.
Assess model performance to support AI system roadmaps.
Prototype data solutions and, with guidance, collaborate with Data Science, AI, and Engineering partners to operationalize, build, and validate scalable tools, applications, or solutions that convert data into insights.
Apply data science best practices throughout the 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 compelling, data-centric narratives from analytical findings to support internal customers' decisions.