Senior Behavioral Scientist

LinkedIn • San Francisco, CA

3d • Hybrid

About The Position

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. This role can be based in either our Sunnyvale, San Francisco, Bellevue, New York, Chicago, Atlanta, Omaha, or Washington D.C. offices. 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 Go-To-Market Enablement (GTME) organization is on a mission to deliver data-driven insights and AI-powered tools that help our global sales teams excel. We are seeking a Senior Behavioral Scientist to join our Insights & Analytics team supporting LinkedIn’s Global Business Organization (GBO). This is a unique opportunity to lead rigorous, interpretable behavioral measurement—connecting what sellers do (in conversations, workflows, and coaching moments) to outcomes like pipeline progression, win rate, cycle time, and renewal/upsell. You’ll apply your expertise in quantitative social science to define high-quality behavioral constructs, build defensible models, and translate findings into practical enablement and coaching decisions. You’ll collaborate with cross-functional partners across GTME and the business (e.g., Sales Performance Consultants, Sales Operations, and Engineering) to turn business questions into clear measurement plans and decision-ready insights. If you’re excited by applied research at scale—and want your work to shape how teams coach, enable, and prioritize investment—we’d love to have you help raise the standard for enablement impact at LinkedIn.

Requirements

• MS or PhD in a quantitative field (e.g., economics, statistics, psychometrics, political science, marketing science), or equivalent practical experience
• 5+ years of applied quantitative experience, including work with observational/longitudinal data; doctoral research can count toward experience.
• Experience with causal inference
• Experience communicating with non-technical stakeholders
• Experience in SQL and Python for data extraction and analysis.

Nice To Haves

• Strong preference for experience in marketing/media analytics (e.g., campaign measurement, funnel performance, demand gen), particularly in B2B contexts.
• Experience analyzing sales performance or commercial outcomes (pipeline, stage conversion, renewals/upsell, cycle time).
• Familiarity with conversation / call behavior data and translating it into reliable constructs and actionable coaching insights.
• Hands-on experience with causal inference methods (e.g., DoubleML, DiD, matching, event studies, sensitivity analyses) and/or experimentation design.

Responsibilities

• Discover emerging seller behaviors: Mine unstructured data (e.g., call transcripts) to identify novel, high-impact behaviors that distinguish top performers but aren’t yet captured in existing frameworks.
• Define behavioral measures that hold up under scrutiny: Translate high-volume behavioral signals (e.g., conversation behaviors, workflow signals, coaching interactions) into stable, interpretable constructs with clear definitions and documentation.
• Estimate behavior–outcome relationships with decision-grade rigor: Use appropriate longitudinal and hierarchical approaches, thoughtful controls, and clear assumptions to separate signal from noise and avoid common observational pitfalls.
• Evaluate enablement impact credibly: Design measurement strategies for programs and interventions, using experiments when feasible and quasi-experimental approaches when not—paired with robustness checks and sensitivity thinking.
• Communicate uncertainty clearly: Translate results into plain-English takeaways that include effect sizes, confidence/uncertainty, and the practical implications for coaching and enablement decisions.
• Set shared “claims standards” for Enablement: Establish reusable templates, analysis checklists, and guardrails.
• Multiply impact through mentorship: Coach the team and partners on causal inference methods, interpretation, and study design to raise the collective technical bar.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level