Chief Research Scientist, Revenue Intelligence
Listed on 2026-10-28
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Research/Development
Research Analyst, Data Scientist, Research Scientist
The Principal Research Scientist owns the empirical integrity of QNT/L's research system. They design and strengthen the methodologies behind QNT/L Research, test the firm's commercial hypotheses against evidence, establish quantitative standards, and ensure that published claims can withstand scrutiny from executives, investors, researchers, and sophisticated institutional buyers.
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What you'll do
The mandate.01
Design rigorous quantitative and mixed-method research programs around B2B commercial systems, organizational performance, revenue operations, buyer behavior, sales productivity, leadership, and AI
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Develop research methodologies, sampling frameworks, survey instruments, longitudinal studies, benchmark construction, statistical analyses, and validation protocols
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Review QNT/L frameworks and hypotheses for methodological soundness
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Distinguish clearly between observed evidence, inference, correlation, causation, interpretation, and projection
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Own or materially influence QNT/L's research standards, claims validation, citation discipline, provenance, and reproducibility
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Build datasets capable of testing constructs such as Motion Integrity, Signal Debt, Pipeline Fiction, and Executive Continuity
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Establish validation frameworks for the Revenue Architecture Index before any predictive claims are made
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Analyze data produced through Revenue Architecture Diagnostics while maintaining client confidentiality and aggregation standards
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Identify when QNT/L's evidence does not support its thesis, with explicit authority to say so
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Translate sophisticated empirical work into conclusions that CEOs, boards, PE operating partners, and investors can understand, without compromising methodological integrity
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Contribute to major QNT/L research publications and methodology papers
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Help determine which questions are worthy of becoming formal QNT/L research programs
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What you bring
The evidence.10+ years in applied research, economics, organizational science, quantitative business research, behavioral science, industrial-organizational psychology, management science, or a closely related discipline
Demonstrated experience designing and executing original empirical research, not merely synthesizing third-party research
Strong command of statistical inference, research design, survey methodology, and sampling
Fluency in regression and multivariate analysis, longitudinal analysis, and causal inference principles
Depth in measurement and construct validity, experimental and quasi-experimental design, data quality and bias, and reproducible research
Published research, substantial institutional research reports, or comparable evidence of serious methodological work
Experience working with large or messy real-world datasets
Ability to challenge senior executives intellectually without becoming academic for academia's sake
Comfort saying, "The evidence does not support that conclusion"
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Preferred background
Who we notice.No single pedigree is required, and screening for one would eliminate exceptional people. These backgrounds earn immediate attention:
Former Gartner, Forrester, or Sirius Decisions senior researcher or analyst
Research leadership from Gallup, Korn Ferry, Mercer, or a serious economics or workforce research institution
Applied researcher from McKinsey Global Institute, Bain, BCG, or another genuinely empirical research organization
PhD-level researcher in economics, organizational behavior, industrial-organizational psychology, management science, statistics, behavioral science, or a related quantitative field
The strongest profile is a hybrid:
PhD-level methodological sophistication, 10 to 15 years of applied commercial research, and enough business experience to keep QNT/L from becoming an academic journal
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Why QNT/L
You will not produce content that dresses up conclusions the data cannot carry. Your mandate is the opposite: hold QNT/L's thinking to a standard that survives executives, investors, and sophisticated institutional buyers. You will have explicit authority to say the evidence does not support the thesis, and that authority is the point. This is research that decides what the firm is willing to claim.
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