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Capucine Tournemine

@capucinetournemine

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Data Scientist turning operational data into predictive insights and automation.

Mexico

Message

What I'm looking for

I’m looking for a Data Scientist role where I can build predictive models and automate operational workflows, using an end-to-end ML stack (data to deployment) in a real business context—especially where aviation-style operations benefit from timely insights.

I’m a Data Scientist with a strong engineering foundation in the aviation industry, combining hands-on experience in data analysis, predictive modeling, and process automation with a Data Science & AI bootcamp at Le Wagon covering the full ML stack—from data engineering to deep learning and MLOps.

At Transavia (Air France-KLM Group), I automated data pipelines and reporting using Power Automate and Power BI (TRAX), including email parsing of contractor Excel reports into a live dashboard for weekly performance reviews. I built predictive maintenance KPI dashboards with automated alerts for ~10 daily users, helping anticipate parts orders and prevent AOG.

I also led digital tools deployment (PowerApps, Power Automate, external vendors), coordinating with IT and managing a 2-person project team. I delivered three application deployments focused on fault detection & analysis, real-time between-flight intervention tracking (~50 ops/day), and purchase request management across a multi-million-dollar budget scope, while delivering training sessions to technical and operational teams.

Earlier, as a CAMO Performance Engineer intern at Corsair, I developed and automated fleet analytics reporting with Skywise/Palantir, producing dashboards for 9 aircraft and enabling weekly management and monthly executive views. I built a predictive engine penalty cost model from scratch (SQL, Skywise), forecasting $2M+ in end-of-year penalties and designing it to self-adjust as operational data accumulated—achieved as a solo, self-taught contributor within a 6-month internship.

Experience

Work history, roles, and key accomplishments

Methods & Process Engineer

Transavia

Jan 2023 - Jan 2026 (3 years)

Automated contractor Excel report ingestion and dashboard refreshes, and built predictive maintenance KPI dashboards with automated alerts for ~10 daily users to help prevent AOG events. Led 3 Power Platform application deployments with a 2-person team, supporting real-time between-flight intervention tracking (~50 ops/day) and streamlined purchase request management.

Power Automate Power BI Predictive Maintenance KPIs Trax Real Time Tracking Stakeholder Management

CAMO Performance Engineer

Corsair

Mar 2023 - Sep 2023 (6 months)

Developed 4 fleet-performance dashboards and automated weekly/monthly reporting for management and executives across 9 aircraft. Built a predictive engine penalty cost model from scratch using SQL and Skywise, forecasting $2M+ in end-of-year penalties and adjusting as operational data accumulated.

SQL Skywise Palantir Fleet Management Performance Dashboards HTML CSS Model Self Adjustment

Education

Degrees, certifications, and relevant coursework

IPSA - Institut Polytechnique des Sciences Avioniques

Master Degree, Mechanics & Structures

2018 - 2023

Activities and societies: Specialization: Mechanics & Structures; Python, SQL, MATLAB, Statistics; Certifications: TOSA Excel/VBA; TOEIC 930/990.

Earned a Master Degree at IPSA with a specialization in Mechanics & Structures and training in Python, SQL, MATLAB, and statistics. Completed certifications including TOSA Excel/VBA and achieved TOEIC scores of 930/990.

Universidad LaSalle México

2021 -

Completed an exchange semester at Universidad LaSalle México in Mexico City.

Le Wagon

Data Science & AI Bootcamp, Data Science & AI

2026 -

Activities and societies: Advanced Statistics, Advanced Python, SQL; ML (Scikit-Learn, XGBoost); Deep Learning (TensorFlow/Keras); MLOps (Docker, MLflow, FastAPI); Cloud (BigQuery, Google Compute).

Completed a Data Science & AI Bootcamp in Paris, covering advanced statistics and Python, ML (Scikit-Learn, XGBoost), deep learning (TensorFlow/Keras), and MLOps practices. Included projects spanning Docker, MLflow, FastAPI, and cloud tooling such as BigQuery and Google Compute.