Alice Lépissier, PhD



Data scientist and computational social scientist with 10+ years of experience in global climate policy and international development. Deep domain expertise in sustainable investment, climate finance, carbon markets, and illicit finance demonstrated by peer-reviewed publications in top scientific journals. Skilled in bridging research and policy interface by combining quantitative research with pragmatic policy proposals, and experienced in high-level political advocacy (UN, NGOs). Specialty is communicating complex concepts in a clear and impactful way to different stakeholders (policy-makers, media, students).

Data Scientist & Climate Policy Expert

  • Currently Postdoctoral Fellow at Brown University
  • Previously Center for Global Development, United Nations Economic Commission for Africa, Tax Justice Network, ICLEI Africa
  • 12 years of experience
  • PhD Environmental Science and Management from University of California, Santa Barbara
  • Master's degrees Statistics, Economics and Public Policy, and Economic History
  • Alumna University of California, London School of Economics, Sciences Po Paris, École Polytechnique (X), University College London
  • PhD-level trained in statistical data science and machine learning
  • Cutting-edge in open and reproducible science
  • Interactive dashboards and tools
  • An eye for design and polished presentations
  • Policy proposals in international organizations, think tanks, NGOs
  • Expertise in climate finance, carbon markets, sustainable investments, illicit finance, international trade
  • Communicating complex concepts to lay audiences



Public Interventions

Open-Source Repositories




Tech Stack

I love programming and I am always looking to level my skills up. As a result of my keen interest in coding, in addition to the concrete projects that I have delivered for clients and employers over the years, I have acquired a substantial technology stack. I work with the full suite of scientific computing software, including the major Python libraries (Numpy, Pandas, scikit-learn).

Scientific Computing

R 100%
Python 95%
scikit-learn 75%
Jupyter 100%
Stata 100%

Database Management

SQL 70%
MongoDB 90%
MySQL 55%

Scientific Reproducibility

Git 100%
Docker 100%
Binder 100%

General Purpose Programming

Bash shell 80%
Linux 80%
Javascript 70%


HTML/CSS/Sass 100%
TeX 100%
R Markdown 100%
Node.js 85%
Jekyll 100%
Bootstrap 90%

Cloud Computing / CI

Google Cloud Platform 80%
Digital Ocean 70%
GitHub actions 60%
cron 50%


I have developed my quantitative and statistical reasoning skills over 7 years of formal training in graduate school in statistics and econometrics. My Master's degrees were all highly quantitative and my PhD qualifying examinations were in statistics (GPA: 4.0).


  • Regression analysis
  • Hypothesis testing
  • Time series forecasting
  • Model selection
  • Network analysis

Causal inference

  • A/B testing
  • Regression discontinuity design
  • Difference-in-difference
  • Instrumental variables
  • Panel data
  • Synthetic control
  • Interrupted time series
  • Matching and propensity score weighting

Machine learning

  • Classification: logistic regression, naive Bayes, support vector machines, K-nearest neighbor
  • Tree-based methods: decision tree, random forest, XGBoost
  • Feature engineering
  • Dimension reduction: principal components analysis, factor analysis
  • Regularization
  • Hyperparameter tuning
  • Model evaluation and loss metrics
  • Clustering: K-means, hierarchical, DBSCAN, spectral
  • Deep learning: artificial neural networks, generative adversarial networks, natural language processing
  • Causal learning

Computational methods

  • Constrained optimization
  • Expectation–maximization algorithm
  • Genetic algorithm
  • Sampling algorithms
  • Inter- and intrapolation
  • Numerical integration


Scroll down to download my one-page resume or my academic CV.


PhD in Environmental Science & Management

2016 - 2021

Bren School, University of California Santa Barbara, CA

  • Grade: 4.0. Qualifying exams: statistics and comparative politics.

MA Statistics

2017 - 2020

University of California Santa Barbara, CA

  • Grade: 4.0.

MA Economic History

2010 - 2011

London School of Economics, London, UK

  • Obtained with Merit.

MSc Economics & Public Policy

2009 - 2010

Sciences Po Paris & École Polytechnique, France

BA (Hons) European Social & Political Studies

2005 - 2009

University College London, UK

  • Obtained with First Class Honors.

Professional Experience

Lead Machine Learning Engineer

03/23 - 06/23


  • Selected as part of a global collaboration of 20 engineers using AI for good.
  • Working with partner company Agreed Earth to develop an AI system to remotely monitor nitrogen-flow in farms in real-time.

Postdoctoral Researcher

2021 - 2023

Watson Institute for International and Public Affairs, Brown University

  • Affiliations with the Climate Solutions Lab and the Rhodes Center for International Economics and Finance.
  • Working on how to catalyze private sector investment in the green transition.

Doctoral Researcher

2016 - 2021

Bren School of Environmental Science and Management, University of California Santa Barbara, CA

  • Applying innovative quantitative methods from econometrics and statistics to solve pressing environmental problems.
  • Dissertation: "A methodological toolkit to understand complex policy problems: applications to climate change and illicit finance".

Instructor / Teaching Assistant

6 classes throughout 2017 - 2020

University of California Santa Barbara, CA

  • Classes include "Introduction to Research Methods" (undergraduate) and "Business & the Environment" (graduate).
  • Used innovative teaching strategies to introduce students to statistics, computer programming, and other technical concepts.
  • Consistently received rave reviews.


2015 - present

Various clients

  • Clients include the United Nations Economic Commission for Africa, Tax Justice Network, and Global Commission on Business and Sustainable Development.

Research Associate

2014 - 2015

Center for Global Development in Europe, London

  • Bridging research and policy interface by combining rigorous research with practical policy proposals and designing intuitive tools to aid decision-making.
  • Developed the SkyShares climate simulation tool for policy-makers, featured in The Guardian.

Research Assistant

2012 - 2014

Center for Global Development in Europe, London

  • Quantitative research on climate change and illicit finance.

Climate Change Internships

Summer 2009

LaquaR Consultants CC & ICLEI Africa Secretariat, Cape Town, South Africa

  • Research on climate change adaptation.


Below is a selection of my data science projects. Please refer to my CV for a full list of publications.

  • All
  • Climate Change
  • Illicit Finance
  • Algorithms
  • Software


Climate Change

Opposition to Carbon Taxes

Climate Change

Impact Evaluation of Climate Policy

Climate Change

Automatic Scraping of COVID Data


Genetic Algorithm for Image Reconstruction


Interpolation with Bézier Splines


Modeling International Flows of Dirty Money

Illicit Finance

Financial Secrecy and Illicit Financial Flows

Illicit Finance

Machine Learning to Impute Missing Trade Data

Illicit Finance

Docker Container for Jupyter Book


5-minute Git Tutorial


Binder-compatible Docker Image with RStudio and Jupyter


Template for Bilingual Wedding Website


Unsupervised Machine Learning with Illicit Trade

Illicit Finance

Trusted By

Brown University

Postdoctoral Fellow


Lead Machine Learning Engineer

United Nations Economic Commission for Africa


University of California Santa Barbara


Center for Global Development

Research Associate

Tax Justice Network


Business & Sustainable Development Commission



Snail Mail

111 Thayer Street, Box 1970

Brown University

Providence, RI 02912