Bonjour · Hallo · Hello, I'm

Anais Storp

Data Science×Engineering×People

I'm an MSc student in Data Science & AI for Business at École Polytechnique and HEC Paris, currently doing an ML research internship at Safran Tech. I like building things that actually get used, not models that stay in a notebook. Looking for my next internship.

Get in touch Resume
Français — native Deutsch — native English — C1 Español — B1

01 · About

Two cultures, one engineer

I grew up in Munich in a French-German family, spent my first year of university in Montreal, and then moved to Paris for the rest of my studies. Switching between languages, school systems and ways of working has just been normal life for me, and it turns out to be pretty useful at work too.

I can explain technical work in three languages, and I'm comfortable landing somewhere unfamiliar and figuring it out. That's a big part of why forward-deployed and client-facing roles appeal to me: someone has to sit between the engineering team and the people they're building for, and I enjoy being that person.

On paper I'm not an engineer. My Bachelor's at Sorbonne was in Chemistry with a Data Science & AI minor, and I built the rest myself through projects, certificates and daily LeetCode practice. Right now I'm doing my MSc in Data Science & AI for Business at École Polytechnique and HEC Paris, and interning at Safran Tech in ML research.

I do my best work in fast environments where I have to push myself to keep up, ideally surrounded by people who are better than me at something. I also just like people: meeting them, working with them, staying in touch. Outside of work I build Technic Lego sets, play tennis and spend a lot of time with my family.

02 · Education

Academic journey

Sep 2025 — Present

M.Sc. Data Science & Artificial Intelligence for Business

École Polytechnique × HEC Paris — Palaiseau / Jouy-en-Josas, France

Joint program between Polytechnique (machine learning, statistics) and HEC (business strategy). So far: 8+ courses in machine learning, deep learning, database management and Python/R, plus team projects on real-world datasets that we present to faculty and peers.

Machine Learning Deep Learning Database Management Business Strategy

Sep 2022 — Jun 2025

B.Sc. Chemistry, Data Science & AI

Sorbonne Université — Paris, France

Major in Chemistry, with 20+ lab experiments in spectroscopic analysis and titration. Minor in Data Science & AI, where I built my first database systems with SQL and Python and visualisations for 10+ datasets. This is where I started moving from the lab towards data.

Chemistry SQL Python Scientific Method

2024 — Present

Certificates — completed & in progress

Harvard (edX) · Google Cloud · MITx

Completed: Harvard University (edX) — Data Science with Python, Machine Learning & AI (Jan–Apr 2024): implemented ML algorithms (KNN, Naive Bayes) reaching 85%+ classification accuracy across 5+ projects with regression, classification and cross-validation, and Google Cloud Professional Data Engineer certification.
In progress: MITx — Data Analysis: Statistical Modeling and Computation in Applications.

HarvardX ✓ Google Cloud Data Engineer ✓ MITx Statistical Modeling — in progress

2021 — 2022

Complément de Formation — Preparatory Year

Université de Montréal — Montreal, Canada

My first year of university, spent in Montreal in a bilingual French-English environment, before coming back to Paris for my Bachelor's. An early taste of starting over somewhere new.

International North America

03 · Experience

Where I've worked

Safran Tech — Research & Technology Center

Apr 2026 — Aug 2026

Research Intern — Machine Learning for Materials Science · Paris, France

Context: Safran is a leading international high-technology group in aerospace propulsion, aircraft equipment and defense: jet engines, landing gear, avionics, space systems. Safran Tech is its central research center. The work is safety-critical and demands a lot of rigor, and a big part of my job is explaining ML results to materials scientists who are not data scientists.
  • Developing and benchmarking machine learning models to predict the creep rupture life of nickel-based single-crystal superalloys from chemical composition, temperature and stress
  • Designed and compared several learned embedding architectures in PyTorch (MLP, self-attention) against an XGBoost baseline, evaluated with R², MAE and RMSE
  • Implemented uncertainty quantification and Rashomon-set analysis to assess prediction reliability in a small-data regime
  • Structured a reproducible codebase (Python, Git/GitLab) and presented results to supervisors and materials-science domain experts
Python PyTorch XGBoost Uncertainty Quantification Git/GitLab Aerospace & Defense

04 · Skills

Toolbox

Programming

Python — advanced SQL — advanced Scala R HTML

ML / AI

PyTorch XGBoost scikit-learn pandas NumPy Feature engineering Time-series CV Uncertainty quantification

Data Viz

matplotlib Plotly seaborn Data storytelling

Tools & Data Engineering

Git / GitLab Apache Spark LaTeX Google Cloud Excel / Office

Human Skills

Trilingual communication Cross-functional collaboration Presenting to non-technical audiences Cross-cultural fluency

05 · Projects

Selected work

ENS Data Challenge · Top 7% of ~1,250 teams · 2026

QRT — Asset Allocation Performance Forecasting

Binary classification of next-day asset return signs from 20-day price and signed-volume history (527k training rows). Engineered 400+ features across six packs, pruned to a compact 25-feature set for out-of-distribution transfer. Built rolling-block time-series CV to prevent temporal leakage and a magnitude-weighted XGBoost ensemble blended in rank space with linear challengers under a rare-switch arbitration scheme. Team of 2.

Python XGBoost Feature Engineering Time-Series CV

Database Management Project · 2026

Connected Component Finder — Distributed Graphs in Spark

Implemented the CCF connected-components algorithm (iterative MapReduce) in Apache Spark across four variants (Python and Scala, each with the RDD and DataFrame APIs). Benchmarked scalability on synthetic and real-world SNAP graphs, validating correctness against the reference paper. Team of 2.

Apache Spark Scala PySpark Distributed Computing

Personal Project

GENFORM — Data-Driven Ergonomic Analysis

Reusable, modular Python library translating ergonomic and biomechanical models from scientific papers into code for adjustable fitness equipment design. Clean, reproducible pipeline on ANSUR II (the US Army anthropometric reference dataset), with configurable sensitivity analyses and automated visual reports to evaluate design trade-offs.

Python pandas Scientific Computing Sensitivity Analysis

Client Case Study · In progress

Vélib' Rebalancing — A Forward-Deployed Case Study

Treating Paris's bike-share operator as a client: framing the problem the way they would (empty and full stations frustrate riders and cost revenue), forecasting demand on open Vélib' station data, and delivering a decision-support dashboard plus a one-page executive write-up. The goal is to run it like a real client engagement, not a notebook.

Python Forecasting Streamlit Open Data Client framing

06 · Why Forward Deployed

A model that never leaves the notebook doesn't help anyone. I want to work where engineering meets the real world.

Forward-deployed engineering combines the two things I care about most: serious technical work and direct contact with the people who use it. Growing up between France and Germany, and studying in three different countries, taught me to adapt quickly and build trust with very different kinds of people. That is more or less the job description.

Technical depth

Enough ML, statistics and data engineering to build things that hold up in production.

Client empathy

Every project I've done ended in front of an audience: materials scientists at Safran, faculty at X-HEC. I'm used to presenting to the people who will actually use the work.

Cultural bridge

Three languages, three countries of study, and plenty of practice at being the new person in the room.

07 · Contact

Let's talk

I'm looking for my next internship in data science, machine learning or forward-deployed engineering. Happy to talk in French, German or English. Email is the fastest way to reach me.

Emailanaisstorp@gmail.com LinkedInanais-storp GitHubAnaisStorp