About
I am an independent expert and advisor in marketing measurement, causal inference, and damages quantification, with a PhD in Decision Sciences (Computational Statistics) and more than ten years designing, validating, and governing causal measurement systems at enterprise scale.
My work sits at the intersection of causal inference, econometrics, and strategic decision enablement — the measurement architecture that organizations rely on to allocate investment, quantify impact, and defend analytical conclusions under scrutiny. I bring particular depth in marketing mix modeling, incrementality, attribution, A/B testing, and quasi-experimental methods, built from the ground up and made actionable for senior leadership across $500M+ in investment decisions.
A decade spent auditing whether third-party measurement methods truly capture incremental impact — combined with a Deloitte audit and SOX financial-controls background — is what I bring to independent validation, methodology review, and expert engagements. It is an evaluator's vantage point: assessing whether models and measurement are trustworthy, reproducible, and defensible.
Prior publications appear under Marie H. Roy and Marie-Hélène Roy.
Areas of Expertise
Causal Inference & Experimentation
A/B testing design and validity, quasi-experimental methods, difference-in-differences, geo-lift testing, propensity score methods, identification strategy under real-world constraints, causal vs. correlational claim evaluation.
Marketing Measurement & Attribution
Marketing Mix Modeling (MMM), multi-touch attribution (MTA), incrementality and lift measurement, media ROI and elasticity modeling, unified measurement architecture, investment optimization.
Statistical Modeling & Econometrics
Bayesian and hierarchical models, time-series forecasting, market-response and elasticity modeling, nonparametric methods, model specification and performance evaluation, predictive and causal modeling frameworks.
Model Governance & Validation
Model risk evaluation, methodology soundness review, bias and fairness assessment, audit oversight and reproducibility standards, drift monitoring, and KPI governance — assessed from an independent evaluator's seat, with working familiarity with emerging frameworks (NIST AI RMF, EU AI Act, ISO 42001).
Career Summary
Senior Data Scientist, Measurement Science (Contract)
Southern Glazer's Wine & Spirits
2025 – 2026
KPI governance, measurement architecture, and analytics leadership coverage for digital product analytics across $1.5B+ quarterly influenceable revenue.
Principal Data Scientist / Measurement Lead
HP Inc.
2020 – 2025
Led the design and execution of marketing measurement architecture integrating MMM, MTA, incrementality, and brand analytics across global business units, influencing $500M+ in investment decisions. Directed third-party model audits and established validation standards across 20+ deployed solutions.
Senior Data Scientist
Solsten (Berlin / Remote)
2019 – 2020
Psychometric modeling, bias detection in ML frameworks, and behavioral analytics for gaming and digital products.
Lead Data Scientist
Age of Learning
2018 – 2019
Educational data mining, engagement analytics, and predictive modeling for children's learning platforms.
Decision Sciences Researcher
GERAD / Tech3Lab, Université de Montréal & HEC Montréal
2014 – 2018
Nonparametric inference methodologies, ensemble learning frameworks, and neuro-marketing research.
Foundation Roles
Deloitte (Audit & Assurance) · Rio Tinto Alcan (SOX Financial Controls)
Audit discipline and financial controls experience that informs current model governance and compliance work.
Selected Publications
Roy, M.H. & Shapiro, S. (2024). Bridging the Gap of MMM and MTA in a Cookieless World. I-COM Global Summit, Malaga, Spain.
Roy, M.H. & Larocque, D. (2019). Prediction intervals for random forests. Statistical Methods in Medical Research, 29(1), 205–229.
Owen, V.E., Roy, M.H., Thai, K.P., Burnett, V., Jacobs, D., & Keylor, E. (2019). Detecting wheel spinning and productive persistence in educational games. Educational Data Mining Conference.
Roy, M.H. (2018). Adapting ensemble predictive modeling for educational video games. IDEAS SoCal AI & Data Science Conference, Los Angeles.
Roy, M.H., Larocque, D., & Dupuis, D. (2015). Robust variable selection with a multiple step bootstrap procedure. Joint Statistical Meeting, Seattle.
Education & Certifications
Ph.D., Decision Sciences — Computational Statistics
Université de Montréal, 2018
Dissertation: Three Essays on Nonparametric Prediction Intervals and Robust Variable Selection
NSERC Full Doctoral Scholarship (Natural Sciences and Engineering Research Council of Canada)
M.Sc., Statistics & Data Mining (Business Intelligence, Decision Sciences)
HEC Montréal, 2011
With Great Distinction (4.09 GPA)
B.B.A., Financial Markets & Economics
HEC Montréal, 2008
AIGP — AI Governance Professional (In Progress)
International Association of Privacy Professionals (IAPP)
Advisory & Expert Engagements
I work independently in two related capacities. Both rest on the same foundation: a decade auditing whether marketing-measurement methods truly capture incremental impact, grounded in consequential enterprise application rather than theory. As an independent party with no vendor stake, my role is to evaluate — not to sell a method or a tool.
Independent Measurement Advisory
For marketing and finance leaders who need a vendor-neutral assessment of whether their measurement is sound and their numbers are defensible:
- Independent method validation — whether an MMM, attribution, or incrementality method actually measures incremental impact, or only appears to.
- Measurement audit & assurance — independent review of a measurement stack for soundness, reproducibility, and defensibility.
- Vendor selection & methodology review — structured, vendor-neutral evaluation of incrementality and measurement providers.
- Measurement standards & neutral evaluation — defining what "incrementality" or "lift" means in practice, and serving as a neutral on measurement disagreements.
Expert Witness & Litigation Support
Expert consultation, report preparation, and testimony in disputes turning on marketing measurement, causal inference, statistical methodology, and damages:
- Marketing ROI, attribution, and incrementality-methodology disputes
- False advertising and performance-claim substantiation (Lanham Act, FTC, state consumer-protection)
- AdTech and measurement-vendor disputes; ad fraud and invalid-traffic assessment
- Commercial damages and lost profits involving marketing or sales data
- Expert rebuttal and methodology review of opposing statistical and causal models
For independent validation, vendor-neutral evaluation, or expert engagements, please contact me directly at mariehoffmann.ds@gmail.com.
Contact
Dallas–Fort Worth, TX