Senior Data Scientist, CX Revenue & Causal ML

Coinbase, Inc.; Baton Rouge (LA); On-site; USD 180,000 - 212,000; Full time

Job summary

Coinbase, Inc. is seeking a Senior Data Scientist for the CX Consumer Analytics team. You will own revenue calibration models and experiments that connect support interactions to our asset accumulation flywheel.

Collaborate with CX Analytics Engineers, PMs, and Product teams to deliver defensible insights, improve retention, and enhance automation quality. Strong production-grade rigor is required.

Qualifications

• BA/BS in a quantitative field (e.g., Statistics, Mathematics, CS, Economics) with 5+ years of relevant experience or a PhD with 3+ years.
• Experience building revenue attribution or causal impact models in a consumer-facing or operational analytics context.
• Proficiency in A/B testing, causal inference, and ML applications with production-grade rigor.
• Experience designing and deploying LLM-based classification or NLP pipelines.
• Ability to translate complex model outputs into clear narratives for executives and product teams.

Responsibilities

• Own and evolve CX's revenue calibration models from support data.
• Design causal inference frameworks to measure CX program impact.
• Build LLM-powered classification pipelines for CX contact taxonomy and issue attribution.
• Collaborate with program managers and product teams on segmentation models and behavioral signals.
• Maintain statistical rigor for executive reporting and regulatory defensibility.

Skills

Revenue attribution

Causal inference

A/B testing

NLP pipelines

LLM classification

Stakeholder communication

Gen AI tools

Education

BA/BS in quantitative field

PhD in quantitative field

Tools

LibreChat

Gemini

Glean

Job description

Coinbase, Inc. is seeking a Senior Data Scientist for the CX Consumer Analytics team. You will own revenue calibration models and experiments that connect support interactions to our asset accumulation flywheel.

Collaborate with CX Analytics Engineers, PMs, and Product teams to deliver defensible insights, improve retention, and enhance automation quality. Strong production-grade rigor is required.