Sr. Measurement Scientist, Solution Engineering

Live Ramp is hiring a Sr. Solution Engineer, Measurement to join our pre‑sales team and serve as the measurement subject‑matter expert for customers.

• Measurement Advisory & Consultative Sales:
  Serve as Live Ramp’s pre‑sales measurement authority, guiding customers through measurement methodologies—incrementality / lift testing, multi‑touch attribution, media mix modeling, causal AI, and hybrid frameworks—and recommending the right approach for specific business objectives and data maturity.
• Lead technical discovery and white‑boarding sessions with senior measurement stakeholders (VPs of Analytics, Heads of Measurement, Data Science Leads, agency measurement teams) to diagnose challenges and design Live Ramp‑powered solutions.
• Translate complex measurement concepts—intent‑to‑treat analysis, propensity scoring, difference‑in‑differences, synthetic control, test/control design—into narratives that resonate with both technical and C‑level audiences.
• Partner with Sales as the technical and analytical authority in competitive measurement deals, building credibility and trust that wins business.
• Solution Design & Data Architecture:
  Design end‑to‑end measurement solutions leveraging Live Ramp’s Cross‑Screen Measurement product, identity graph (RampID), and clean‑room technology across customer verticals. Advise customers on data infrastructure and data science requirements to execute measurement at scale: clean‑room configuration, identity resolution workflows, first‑party data onboarding, log‑level data access, and analytical environment setup.
• Provide technical guidance on implementation trade‑offs:
  Python vs. SQL for analytical workloads, in‑platform computation vs. data export, deterministic vs. modeled measurement approaches, and privacy‑safe data collaboration workflows. Produce detailed technical scoping documents, reference architectures, and implementation plans that bridge the pre‑sales promise to post‑sales delivery, ensuring seamless handoffs to Customer Success, Technical Solutions and Data Science teams.
• Prototyping, Demos & Innovation:
  Build working prototypes, proof‑of‑concepts, and demo environments that bring Live Ramp’s measurement capabilities to life. Demonstrate causal inference workflows, incrementality analyses, and cross‑screen attribution models within our clean‑room and identity infrastructure. Experiment with emerging measurement techniques, e.g., causal AI, Bayesian media mix modeling, synthetic control methods, double machine learning, and translate them into repeatable, demonstrable solutions on Live Ramp’s platform.
  
  Help create reusable technical assets (code notebooks, solution blueprints, demo scripts, analytical templates) that scale the team’s ability to address measurement use cases across the sales organization. Feed product roadmap priorities from the field, collaborating with Product and Engineering to ensure our measurement products evolve in line with what customers and the market actually need.
• Thought Leadership & Ecosystem Engagement:
  Represent Live Ramp as a measurement thought leader at industry events, conferences, and customer workshops, articulating our measurement philosophy and technical differentiation. Stay current on industry developments in cross‑screen measurement, privacy‑safe analytics, causal inference, identity, and the evolving competitive landscape, and bring those insights back to the team. Build relationships across the measurement ecosystem—agencies, measurement vendors, data partners, industry bodies—to strengthen Live Ramp’s positioning as a measurement infrastructure leader.

• 8+ years of progressive experience in advertising measurement, marketing analytics, or a related quantitative discipline, including meaningful time at a specialist measurement or analytics firm or in an advanced measurement function at a major platform, agency, or brand.
• Deep domain expertise in measurement methodologies: incrementality / lift testing, multi‑touch attribution, media mix modeling, and causal inference techniques (intent‑to‑treat, propensity scoring, difference‑in‑differences, synthetic control, Granger causality).
• Hands‑on…