Data Scientist

About the role

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 team turns member and customer behavior into product decisions across one of the world's largest professional networks. We design experiments, investigate metric movements, and partner with PM and Engineering to ship features that create real economic opportunity for members, including the next generation of AI-powered experiences.

We're hiring an early-career data scientist to join a product team and grow under the mentorship of more senior Data Science ICs. You'll be responsible for writing SQL and Python to investigate how members use the product, read and interpret A/B test results, build dashboards and analysis docs that inform real decisions, and learn the craft of experimentation on data at LinkedIn's scale.

You'll deep dive into the tracking and algorithms of our AI stacks and learn how to use agentic data science to understand these systems and create insights that drive impact. As you grow, you'll take on more ambiguous problems and shape the metrics our team uses.

Responsibilities

• Performs data-driven experiments and causal analyses to test and validate new product ideas or go-to-market strategies, develop ecosystem understanding, and monitor current products or systems.
• Performs analyses to support the definition of metrics that leads to data-driven target setting and decision-making.
• Leverage AI tools in day-to-day workflows to increase productivity
• Develops and employs models and causal analyses (e.g., panel analysis, A/B testing) to help determine what drives metric relationships and create actionable insights within basic product domains.
• Collaborates with relevant partners on defining metrics and frameworks for evaluating AI/LLM systems and agentic product performance.
• Supports AI system roadmaps through assessing model performance.
• Prototypes data solutions, and/or collaborates with relevant partners (e.g., Data Science teams, AI, Engineering) to operationalize, build, and validate scalable tools/applications/solutions for use by internal customers to convert data into insights at scale, with guidance.
• Embeds data science best practices and principles through all phases of development of customized and/or scalable data solutions.
• Provides consultation to partners on basic data questions to enable data democratization.
• Understands and identifies patterns of user behavior leveraging data mining techniques and provides insights and data-informed recommendations to internal partners.
• Creates compelling, data-centric stories based on analytical findings to support internal customers' decision-making.

Basic Qualifications

• Bachelor's Degree in in Applied Mathematics, Computer Science, Data Science, Economics, Engineering, Informatics, Statistics, or related field
• 1+ year of experience in data science (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.
• Background in at least one programming language (eg. R, Python, Java, Ruby, Scala/Spark or Perl)

Preferred Qualifications

• Master's Degree or Doctorate in Applied Mathematics, Computer Science, Data Science, Economics, Engineering, Informatics, Statistics, or 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

Suggested Skills

• Data Science
• Data Storytelling
• Statistics
• Strategic Thinking

Pay and benefits

LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $101,000 to 167,000. 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.