Senior Data Scientist, Strategy & Insights

LinkedIn•Sunnyvale, CA

1d•$125,000 - $205,000•Hybrid

About The Position

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 a Senior data scientist to own ambiguous, high-impact problems end-to-end. You’ll use your strong statistical intuition and well-honed data science tool kit to answer open-ended problems like defining long-term retention or marketplace value. You’ll define the metrics that guide product strategy, dive deep into marketplaces and evaluate AI-native features as they ship. You'll work closely with engineers and PMs to translate findings into roadmap decisions.

Requirements

Bachelor's Degree in a quantitative discipline: Computer Science, Statistics, Operations Research, Informatics, Engineering, Applied Mathematics, Economics, etc.
3+ years of relevant industry or relevant academia experience working with large amounts of data
Experience influencing strategy through data-centric presentations.
Experience in SQL.
Background in at least one programming language (e.g., R, Python, Scala).
Experience in applied statistics and statistical modeling in at least one statistical software package.
Experience telling stories with data and visualization tools.
Experience running platform experiments and techniques like A/B testing.

Nice To Haves

Master's Degree or Doctorate in Applied Mathematics, Computer Science, Data Science, Economics, Engineering, Informatics, Statistics, or related field.
Experience with SQL & Python, experimentation (design, analysis, variance reduction),
Experience with causal inference methods;
Product intuition for two-sided marketplaces
Clear written communication
Ability to understand how to measure AI-powered product experiences.

Responsibilities

Designs and 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.
Evaluates A/B and causal tooling for discernable gaps and potentially partners with Applied Sciences teams to create A/B and causal test protocols and methods and analyze ramp performance to help optimize new and/or existing features or models.
Leverage AI tools in day-to-day workflows to increase productivity
Partners with internal customers to translate business problems into data-verifiable hypotheses.
Develops and employs models and causal analyses (e.g., panel analysis, A/B testing) to determine what drives metric relationships and create actionable insights within moderately complex product domains.
Independently diagnoses issues and identifies opportunities to drive improvements within AI/LLM systems.
Prototypes data solutions, and/or collaborates with relevant partners (e.g., Data Science teams, Engineering) to operationalize, build, and validate scalable tools/applications/solutions for use by internal customers to convert data into insights.
Embeds data science best practices and principles through all phases of development of customized and/or scalable data solutions, providing feedback to junior team members.
Communicates with relevant partners and internal customers on scoping data-driven investigations and experiments, contributing to recommendations for what best to prioritize to achieve intended strategy.
Develops and runs predictive models for estimating the impact of initiatives on identified KPIs.
Independently creates compelling, data-centric stories based on analytical findings to contribute to internal customers' decision-making.