About
I am a principal-level data scientist and measurement strategist with a PhD in Decision Sciences (Computational Statistics) and more than ten years of applied experience designing, validating, and governing marketing measurement systems at enterprise scale.
My work sits at the intersection of causal inference, econometrics, and strategic decision enablement — building the measurement architecture that organizations rely on to allocate investment, evaluate impact, and defend analytical conclusions under scrutiny. I specialize in the full measurement stack: problem framing, identification strategy, model validation, KPI governance, and executive translation.
I am currently focused on AI/ML governance and model risk management — applying deep statistical and methodological expertise to the strategic oversight layer that determines whether models are trustworthy, auditable, and defensible.
Prior publications appear under Marie H. Roy and Marie-Hélène Roy.
Areas of Expertise
Marketing Measurement & Attribution
Marketing Mix Modeling (MMM), multi-touch attribution (MTA), incrementality and lift measurement, media ROI and elasticity modeling, geo-lift and difference-in-differences experimental design, unified measurement architecture.
Causal Inference & Experimentation
A/B testing design and validity, quasi-experimental methods, difference-in-differences, propensity score methods, identification strategy under real-world constraints, causal vs. correlational claim evaluation.
AI/ML Governance & Model Risk
Model risk evaluation and validation, bias detection and fairness assessment, audit oversight and reproducibility standards, drift monitoring, regulatory alignment (NIST AI RMF, EU AI Act, ISO 42001), executive risk communication.
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.
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 Witness
I am available for expert consultation, report preparation, and testimony in matters involving marketing measurement, causal inference, statistical modeling, and model governance. My opinions are grounded in a decade of consequential, real-world application at enterprise scale — not theoretical exercises.
Practice areas include: marketing ROI and attribution disputes, advertising effectiveness and media contract disputes, ad fraud and invalid traffic assessment, damages quantification involving marketing or sales data, statistical discrimination claims, data quality and integrity disputes, and intellectual property involving proprietary measurement methodologies.
For engagement inquiries, please contact me directly at mariehoffmann.ds@gmail.com.
Contact
Dallas–Fort Worth, TX