Aslane Mortreau

Name: Aslane MORTREAU

Profile: Freelance Data & AI Specialist | Pharma/Cosmetics R&D
Email: aslane@mortreau.net
Phone: +33 6 27 66 05 07

About me

I'm Aslane Mortreau, a freelance data scientist specializing in the application of statistics, data, and AI to R&D challenges in the pharmaceutical and cosmetic industries.

I support R&D teams in analyzing complex data (longitudinal, efficacy trials, survival) and in structuring their data workflows, to deliver robust, interpretable, and decision-ready results.

Beyond analysis, I design custom analytical tools that automate statistical methods, improve reproducibility, and make advanced analyses accessible to non-expert teams.

My goal: save time for R&D, without compromising on scientific rigor.

Skills

  • Python - Advanced (Data science, automation, APIs, PyTorch, pandas)
  • R - Advanced (Biostatistics, mixed models, Shiny apps, ggplot2)
  • SAS - Intermediate (Base, Macro)
  • SQL - Intermediate (PostgreSQL, BigQuery)
  • Statistical Methods - Mixed-effects models, ANOVA, Survival analysis, CDISC, Cox regression
  • Data Engineering - Dagster, dbt, Docker, Data Vault, GCP
  • Tools - Git, Airflow, Grafana
  • Languages - French (native), English (fluent)

Resume

Education

Master of Biomedical Engineering/Health & Image Processing/AI

EPISEN, Creteil, France

Coursework: Bioinformatics, Data Science, Fluid Mechanics, Genomics, Genetics, Health Economics, Image Processing, Medical Imaging, Networks, OOP, Pharmacology, Physiology, Proteomics, Signal Processing: Bioinformatics, Data Science, Fluid Mechanics, Genomics, Genetics, Health Economics, Image Processing, Medical Imaging, Networks, OOP, Pharmacology, Physiology, Proteomics, Signal Processing

Summer Program

University of Michigan, Dearborn, Michigan

Coursework: Algorithms, Data Science/NLP, Web Developpement

Master of Computer Science & Data Science

ESIEA, Paris, France

Coursework: Data Science, Hardware, Networks, OOP, Signal Processing, Statistics

Bachelor of Science : Statistical Engineering

University of Nantes, Nantes, France

Coursework: Algebra, Calculus, Group Theory, Markov Chain, Probability, Python, Statistics

Professional Experience

Freelance Data Scientist

November 2025 — January 2026

L'Oréal Research & Innovation

  • Conducted a full scientific and technical feasibility study for a strategic R&I initiative.
  • Analyzed constraints, potential workflows, operational requirements, and scientific viability across research, data, and engineering dimensions.
  • Identified risks, bottlenecks, data requirements, and dependencies for future implementation phases.
  • Delivered structured recommendations to support go/no-go decision-making at the innovation leadership level.
  • Skills: Scientific analysis, feasibility assessment, technical evaluation, R&D workflows.

Freelance R Shiny Developer

November 2025 - Present

Al-Gebrax

  • Conduct statistical analyses for pharmaceutical and CMC studies, including stability, assay performance, and bioanalytical data workflows.
  • Develop R Shiny applications to automate standardised analyses, generate PDF/Word statistical reports, and improve traceability.
  • Implement reproducible pipelines, improve data quality monitoring, and support regulatory-driven analytics.
  • Tools: R (Shiny, tidyverse, lme4), Docker, PK/CMC workflows.

Freelance Data Science & Bioinformatics Consultant

November 2025 - Present

Gencovery

  • Develop Reflex bricks for life science workflows (PK-NCA, CDISC validation, molecular embeddings).
  • Build demo applications and technical documentation to support client onboarding and showcase advanced analytical capabilities.
  • Design generic pipelines for omics data exploration, clustering, differential expression, and interactive dashboards.
  • Technologies: Python, Reflex, RDKit, Docker

Freelance DevOps & Data Engineer

July 2025 - November 2025

LeetCall AI

  • Developed and maintained the backend infrastructure of an AI-powered outbound dialer platform, including call orchestration, real-time communication, and automation workflows.
  • Designed and deployed distributed microservices using FastAPI, Docker, Supabase, RabbitMQ, and PostgreSQL.
  • Implemented data pipelines and event-driven architectures to support call logs, summaries, lead qualification, and CRM synchronization.
  • Optimized CI/CD pipelines, container orchestration, monitoring, and system reliability to ensure high availability.
  • Collaborated with the AI/LLM team to integrate real-time call transcription, summarization, and automated decision workflows.
  • Technologies: Python, FastAPI, Docker Compose, RabbitMQ, Supabase, PostgreSQL, WebRTC (LiveKit), CI/CD pipelines.

Freelance Automation Specialist

October 2024 - May 2025

Oltega

  • Developed comprehensive automation solutions across various business functions including administrative processes, CRM management, and workflow optimization.
  • Created custom Python scripts and integrations to streamline data processing and business operations.
  • Implemented automation workflows using Make, Zapier, Monday.com, and HubSpot to reduce manual tasks and improve efficiency.
  • Designed and deployed automated systems that enhanced productivity and reduced operational overhead for client teams.
  • Collaborated with cross-functional teams to identify automation opportunities and deliver tailored solutions.
  • Tools and technologies: Python, Make, Zapier, Monday.com, HubSpot, API integrations

Data Research Engineer

August 2023 - September 2025

LVMH Recherche

  • Led statistical analyses of in vivo cosmetic efficacy studies, including longitudinal modeling (linear mixed-effects models), time-to-event analysis (Kaplan-Meier, Cox regression), and post-hoc comparisons via estimated marginal means.
  • Collaborated with clinical and regulatory teams to write and validate Statistical Analysis Plans (SAPs), handle missing data (MCAR/MAR), and ensure methodological alignment with claim validation and internal regulatory standards.
  • Developed modular and reusable R Shiny applications for non-statisticians to conduct automated analyses and visualize results.
  • Designed and implemented end-to-end automated statistical workflows (from raw clinical data to formatted tables/figures), reducing analysis turnaround time by >50%.
  • Actively contributed to cross-functional innovation projects, including the development of an AI-driven molecular substitution engine using graph embeddings (metapath2vec) for sustainable formulation strategies.
  • Tools and methods: R (lme4, survival, emmeans, Shiny), Python, Shiny, Docker, Google Cloud Platform, Git

Analytics Engineer

September 2022 - August 2023

dFakto

  • Built and maintained data pipelines to support decision-making for public sector and enterprise clients.
  • Worked on data modeling, transformation, and integration using modern data stack tools (e.g., SQL, dbt).
  • Contributed to dashboard development and data quality assurance processes.
  • Collaborated with multidisciplinary teams to ensure reliable and actionable insights.

Junior Data Manager

September 2018 - January 2019

LMP

  • Responsible for inserting all new data into the main production database.
  • Develops tools for data collection, cleaning, and automated insertion to ensure data quality, completeness, and freshness.
  • Works within the Data Engineering team to maintain a zero-defect database for clients.

Portfolio

  • All
  • Data Engineering
  • Statistics
TrialLytics – statistical platform for clinical trial analysis (ANOVA, mixed models, survival analysis)

TrialLytics

Triallytics is a statistical platform designed to automate clinical trial analyses, including ANOVA, mixed models, and survival analysis, to streamline the research process.

Random Walk Pipeline – data streaming and visualization with Docker

Random Walk Pipeline

Random Walk Pipeline is an end-to-end data streaming and visualization pipeline using Dockerized services to simulate, process, and analyze random walk data.

Epidemio – interactive epidemiological data and survival analysis app

Epidemio

Epidemio is an interactive web application designed for visualizing epidemiological data and conducting survival analysis

Cosmetic Claim Analysis – statistical analysis for cosmetic efficacy claims

Cosmetic Claim Analysis

Epidemio is an interactive web application designed for visualizing epidemiological data and conducting survival analysis

CDISC Pipeline – Dagster data pipeline for CDISC clinical data

CDISC Pipeline

CDISC Validator – Streamlit app for SDTM and ADaM validation

CDISC Validator

An interactive Streamlit application that validates SDTM and ADaM datasets using the official CDISC CORE engine, offering a simple, visual, and efficient way to ensure regulatory compliance.

Testimonials

I had the pleasure of working with Aslane on a Data Scientist assignment for a particularly challenging feasibility study for a business unit. Aslane quickly built strong expertise on specific and complex business topics, grasped the business stakes, and translated them into relevant, structured, and actionable data analyses. Beyond his solid technical skills, I particularly valued his autonomy, his ability to propose solutions, his analytical rigor, and his capacity to communicate effectively with senior stakeholders. Aslane was a real asset in securing the project's feasibility under tight deadlines. I recommend him without hesitation for any Data Science / AI mission calling for technical expertise, business understanding, and strong adaptability.

Océane DOUBLET

Data and AI Department Head at Enovalife

I had the pleasure of working with Aslane and I highly recommend him. He is a colleague who is at once very competent, intelligent, and pleasant, and collaboration with him is smooth and effective. He builds expertise quickly in his areas, works well in a team, and his contributions are always relevant and well thought out. A real added value in his field.

Laurie Montjoly

Toxicology and Ecotoxicology Scientist | Data and AI (NAMs, Read-across, QSAR)

I recommend Aslane for data engineering and R-based data analysis missions! He masters data preparation, transformation, and structuring. Thanks to him, we developed R Shiny analyses and applications integrated into cloud environments (AWS/GCP), connected to data pipelines and storage. His deliverables are robust, clear, and directly usable by business teams!

Sofiane Djerbi

Senior DevOps Engineer

Contact

Call Me

+33 6 27 66 05 07

Email Me

aslane@mortreau.net