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Steve NOUATIN

Steve NOUATIN

Data Scientist

Datastorm | Asnières-sur-Seine, France

Technologies

My Portfolio Highlights

My New Track

Shiny Fundamentals

My New Course

Introduction to Python

My New Track

Shiny Fundamentals

Data adventurer, fearlessly exploring the vast landscapes of information.

My Work

Take a look at my latest work.

course

Introduction to Power BI

course

Introduction to Python

course

Intermediate Python

course

Introduction to Power BI

My Certifications

These are the industry credentials that I’ve earned.

Other Certificates

Udemy Deep Learning for Computer Vision with TensorFlow 2

Udemy Machine Learning & Deep Learning in Python & R

WorldQuant University Applied Data Science II: Machine Learning & Statistical Analysis (with honors)

WorldQuant University Applied Data Science I: Scientific Computing & Python (with honors)

DataCamp Course Completion

Take a look at all the courses I’ve completed on DataCamp.

Courses

Introduction to Python
Introduction to Power BI
Intermediate Python
Supervised Learning with scikit-learn

My Work Experience

Where I've interned and worked during my career.

Réseau de Transport d'Électricité | Jul 2025 - Present

MLOps Engineer Consultant

Consultant for the R&D Department Keys Tasks: ------------- ---> Supported the development of a Python package for building and training Graph Neural Networks (GNNs) on real power-grid scenarios – Improved and generated the package documentation using Sphinx. – Developed and refactored several neural-network layers for GNNs using Flax (flax.nnx). ---> Advised on and implemented MLOps best practices for training and deploying GNN models. – Implemented unit and integration tests for the package, achieving 90% test coverage. – Developed and containerized a web application (Vue.js frontend, FastAPI backend) to facilitate interaction with a feature store backed by PostgreSQL and an S3 bucket.
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EDF | Apr 2025 - Jul 2025

Data Scientist Consultant

Consultant for the R&D Department - Development of a toolbox of unsupervised methods for anomaly detection in time series of electrical loads Keys Tasks: ------------ ---> Conducted an in-depth analysis to characterize different types of anomalies. ---> Analyzed and decomposed electric-load time series using standard statistical methods and modeling with Prophet. ---> Proposed and implemented an anomaly-detection pipeline combining diverse techniques such as DBSCAN, Wavelets, matrix profile, Granular Markov Models, Tukey’s depth, Z-score, etc. This work resulted in a ready-to-use Python library.

ENSAI | Sep 2024 - Present

Teaching Assistant

As a Teaching Assistant for practical and tutorial sessions in Machine Learning, I supported students in understanding core concepts and applications of machine learning algorithms. My responsibilities included: ---> Assisting in the preparation of course materials and assignments to reinforce theoretical knowledge through practical application. ---> Guiding students through hands-on exercises and projects, fostering a practical understanding of machine learning algorithms and techniques. ---> Addressing students' questions and helping them troubleshoot issues in their pratical labs. This role allows me to deepen my own knowledge while mentoring the next generation of data scientists and machine learning practitioners.

RATPgroup | Apr 2024 - Present

Data Scientist Consultant

Development of a Data Orchestration, Qualification, and Validation Application Context: -------- ---> Design and develop an application to orchestrate data flows, ensuring the qualification and reliability of measured data. ---> Collaborate with cross-functional teams to optimize data integration and processing workflows. ---> Provide ongoing application support and maintenance to ensure continuous system reliability and performance.

Enedis | Jun 2023 - Sep 2024

Data Scientist Consultant

Consultant for the Financial Directorate - Development of a Web Application Suite for Data Flow Monitoring and Energy Forecast Quality Control Key Taks: ---------- ---> Redesign and rewrite an existing R Shiny application to create a comprehensive ecosystem of R Shiny applications. ---> Develop R Shiny modules for monitoring data flows, tracking model performance, and comparing model outputs. ---> Implement Dockerization and set up a continuous deployment pipeline tailored to the infrastructure (Kubernetes Cluster).

EDF | Oct 2023 - Dec 2023

Data Scientist Consultant

Consultant for the R&D Department - Development of an Algorithm for Medium/Long-Term Forecasting of Electrical Load Trends Keys Tasks: ------------ ---> Conducted an in-depth analysis and diagnostic of the previous forecasting method. ---> Cleaned and analyzed time series data of electric load from source substations. ---> Proposed and implemented a trend forecasting algorithm using quantile autoregression to forecast electric load trends. This work offered robust results and better insights for future planning and investment.

TRAPIL | Jun 2023 - Jul 2023

Data Scientist Consultant

Consultant for the Analysis Department – Proof of Concept (PoC) for automatic detection and classification of defects on hydrocarbon transmission pipelines Keys Tasks: ------------ ---> Processed and transformed ultrasonic C-scan data produced by non- destructive testing (NDT) inspections of hydrocarbon transmission pipelines to make it suitable for modeling. ---> Developed and finetuned deep learning models (Faster R-CNN, YOLOv8) for automatic defect detection and classification, while optimizing GPU resource utilization. ---> Tuned and optimized models to ensure high accuracy and minimize false positives in anomaly detection.

Datastorm | Apr 2023 - Present

Data Scientist

Data Consulting Firm specializing in various sectors, including energy, healthcare, industry, and services. Key Responsibilities: ---------------------- ---> Conducted data analysis and modeling using Python and R. ---> Managed data throughout its lifecycle. ---> Developed Computer Vision models. ---> Engaged in Research and Development projects in data science. ---> Created packages and web applications using Python, R Shiny, JavaScript, and Vue.js. ---> Implemented MLOps and DevOps practices, including containerization with Docker, Continuous Integration and Continuous Deployment (CI/CD), automated testing, performance monitoring, and application support and maintenance.

Expleo Group | Apr 2022 - Oct 2022

R&D Engineer - Data Scientist

As a data scientist in an R&D team, I focus on validating Generative Adversarial Network (GAN) discriminators for mechanical design by quantifying uncertainty in deep neural network predictions. My work employs Bayesian inference methods in deep learning, primarily using images as data. Key Responsibilities: ---------------------- ---> Developed deep learning models for the design of 3D-printable mechanical parts, including image generation and classification. ---> Built training and evaluation pipelines using KALE to streamline model development. ---> Conducted a state-of-the-art review on uncertainty quantification in deep learning and applied the insights to enhance model performance. ---> Implemented advanced techniques for uncertainty quantification, including Monte Carlo Dropout, Bayes By Backprop, Evidential Neural Networks, Stochastic Gradient Hamiltonian Monte Carlo, and Stochastic Gradient Langevin Dynamics.

Insee | Oct 2021 - Mar 2022

Data Scientist

Semantic Address Matching and Analysis Key tasks: ----------- ---> Automated the extraction of information from postal addresses using Deep Learning (NLP, NER) and Hidden Markov Models. ---> Developed an address matching algorithm with the BD TOPO database using a vector-based database with FAISS. ---> Built a web application with Flask to deploy the constructed model and matching algorithm.

French Agency for Food, Environmental and Occupational Health & Safety (ANSES) | Jun 2021 - Aug 2021

Statistician

Proposed an effective process for analyzing the impact of treatments. Key tasks: ----------- ---> Identified the limitations of using p-values in statistical analysis. ---> Analyzed the prevalence of the hepatitis E virus in pig farms at slaughter age. ---> Implemented bayesian techniques to assess the practical significance of comparison tests.

Credit Agricole Leasing & Factoring | Feb 2021 - Apr 2021

Risk Analyst

Redesigned the retail credit score for leasing. Key tasks: ----------- ---> Selected and discretized of variables using decision trees. ---> Built scoring models using logistic regression and XGBoost with SAS and R. ---> Developed a scoring grid to evaluate and classify risks more effectively. ---> Improved the bank's scoring model's discriminating power by 10%.

National Institute of Statistics and Economic Analysis of Benin | Jul 2017 - Sep 2017

Statistician Economist

This is an academic internship at the Department of Statistics and Economic Studies of the National Institute of Statistics and Economic Analysis of Benin. It was carried out as part of my bachelor's degree in Statistics and allowed me to produce my final dissertation on the topic: Gross Domestic Product and Electrical Energy in Benin: a causality analysis. It allowed me to deepen my knowledge of time series econometric methods (unit root tests, ARDL, VAR, Toda-Yamamoto causality test).

My Education

Take a look at my formal education

Engineer's degree, Data science and Data engineeringENSAI | 2022
Economist Statistician Engineer, Statistics and EconomicsISSEA-CEMAC | 2022
Benin School of Applied Economics and Management (BSAEM/
Bachelor of Applied Science - BASc, Economical and Sectorial StatisticsENEAM) | 2017

About Me

Steve NOUATIN

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