Freelancer profile translated to English.

Description

Data scientist, I design models and analyses on real-world data: sensors, time series, behavioral and business data.

I cover the entire chain: needs framing, exploration and cleaning, feature construction, modeling, interpretation, and reporting. I focus as much on statistical robustness as on what the client can actually do with it.

What I deliver:

— Supervised predictive models (regression, classification, gradient boosting) with interpretability analysis

— Time series analysis and forecasting

— Data preparation and variable selection pipelines on noisy or incomplete data

— Exploratory analyses, segmentation, funnel analysis

— Dashboards and automated reporting

— Comprehensive report: methodology employed and critical analysis of results, as detailed as you wish

Past projects (some deliverables can be viewed on my Github):

- Variable selection and regression pipeline on high-sparsity questionnaire data (research freelance);

- Modeling of insurance claim costs on 166,000 lines (XGBoost, Poisson/Gamma losses, SHAP, non-random missing data);

- BI and CRM analysis on Excel for a real estate scale-up;

- Electoral modeling and voter segmentation for a political campaign;

- Reporting and management of industrial data at an automotive supplier.

- Weight prediction on US health survey data (custom scikit-learn transformers to neutralize non-response codes, benchmark of ten models in cross-validation);

- Retail sales forecasting by SARIMAX and exponential smoothing;

- Classification of satellite spectra for CNES (multi-input, multi-output CNN).

Education:

Dual degree Grande École EDHEC — Management and MSc Data Science & AI, after a scientific preparatory class.

Bilingual French/English, based in Haute-Savoie, available remotely.

Languages

French
Native or bilingual
English
Native or bilingual
Spanish
Basic

Workplace preferences

Remote only

Primarily works remotely

Lucya
Data Scientist — Applied Research
EDUCATION AND E-LEARNING
March 2026 - July 2026 (4 months)
Paris, France
Applied research on Self-Regulated Learning, in partnership with an academic laboratory and a private sponsor.

From a behavioral questionnaire with over 300 items and very partial completion (~15%), I built a complete variable selection and dimension reduction chain: factor analysis, stability selection, then cross-validated regularized regression to isolate the most robust predictors of academic performance.

Deliverable: a 25-item measurement instrument, empirically calibrated, achieving a correlation of 0.47 with academic performance on a validation sample.

Stack: Python (pandas, scikit-learn, scipy, statsmodels), regularized regression, unsupervised clustering, confirmatory factor analysis, missing data handling
Python Scikit-learn Machine Learning Statistics Research
Forvia
Associate Digital Project Manager & Data Analyst — Process Mining & Reporting
AUTOMOBILE
January 2024 - July 2024 (6 months)
Paris, France
Mission within the digital department, I acted as the liaison between Data Engineers and Project Owners for the deployment of data and process mining use cases.

Six use cases delivered to five business teams (sales, purchasing, HR, controlling, treasury) on Palantir Foundry and Celonis, resulting in a reduction of manual reporting.

Construction of a cash flow forecasting model and resolution of missing data issues between systems; homogenization of tools and projects across internationally distributed teams.

Stack: Python, PQL, Palantir Foundry, Celonis.
Data Mining Python Palantir Foundry Process Mining SQL
Political Party
Data Analyst — Electoral Modeling
PUBLIC SECTOR
July 2023 - December 2023 (5 months)
Auckland, New Zealand
Legislative election campaign. Modeling of the Auckland electorate, construction of field targeting, refinement of the party's strategy.

I developed a model to identify undecided voters ('swing voters') to prioritize door-to-door areas based on their concentration of potentially convertible voters, used daily until election day.

The segmentation produced directly guided the coordination of field teams via Qomon. The party won the vote in the constituency.
Customer Segmentation and Analysis Data Modeling Microsoft Excel

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Scientific Preparatory Class (PCSI-PC)
Lycée Lakanal
2021
Deux ans de formation scientifique intensive (mathématiques, physique, chimie, sciences de l'ingénieur) avec entrée sur concours. Base de rigueur méthodologique et d'analyse quantitative.
Master in Data Science & Artificial Intelligence
EDHEC
2026
Programme : machine learning, deep learning, data mining, traitement automatique du langage, séries temporelles, modèles statistiques, méthodologie de recherche, gestion et architecture de données, data visualisation, web analytics, projets data appliqués au business, challenge public (en collaboration avec l'ENS & Crédit Agricole), mémoire. Outils : Python, SQL, Tableau, Looker Studio, GA4 (Google Analytics). Moyenne : 16.5/20

Alizée A.

Data Scientist, Data Analyst, AI Scientist

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