Mostafa AKIL

Hello! I am Mostafa AKIL. In an era where data drives decisions, I chose to specialize in data science and machine learning. Over the years, I’ve gained extensive experience in these fields, and I've realized the critical gap between raw data insights and their practical application. This is why my current mission is to bridge that gap by making data-driven knowledge accessible and actionable. Now, with every line of code, I focus on building robust machine learning models, deploying scalable MLOps pipelines, and ensuring that these models are easily accessible and usable by others.

Data Scientist – PRIMEVER
January 2023 – Present
- Utilized Power BI and MyReport for creating analytical dashboards for real-time insights
-  Developed, deployed, and monitored machine learning models integrating MLOps practices for continuous model     updates and monitoring for various projects, including:
• Improving energy efficiency
• Analyzing customer satisfaction

- Built and deployed time series forecasting models for predictive analytics.
- Implemented image and PDF processing models, including OCR systems.
- Developed and deployed interactive applications using streamlit.

Technologies: Python, PowerBI, MyReport, Pandas, Numpy, Matplotlib, Plotly, Scikit_learn, Scipy, Statsmodels, MLFlow, Prefect, Git, Gitlab, Docker, Docker-Compose, Streamlit

Software Engineer – LGCgE
October 2020 – November 2022
Mission 1:  Creation of a web application allowing to optimize the exploitation phase of a real estate park.

- Development Front-end
- Development Back-end
- Migration to a cloud solution (O2switch)
Mission 2: Polder2Cs is a European Interreg project. The goal is to develop the Data Wizard, an innovative solution that facilitates access to knowledge managed in their scientific and conditional context.

- Development Front-end
- Development Back-end
- Migration to a cloud solution (O2switch)

Technologies: Python, Django, Djangorestframework, HTML, CSS, Bootstrap, Javascript, Mysql

Researcher Data Scientist – LGCgE
September 2019 – September 2020
Development of a gas consumption simulation model for a tertiary building.  Estimation of the potential reduction in energy consumption obtained by optimizing the building's heating parameters.

- Data processing
- Feature selection with statistical tests
- Optimization and comparison of several regression models
- Select the best model

Technologies: Python, Pandas, Numpy, Seaborn, Matplotlib, Scikit_learn, Scipy

PHD student Data Scientist – LGCgE
October 2016 – September 2020
Improved maintenance and management of colleges in Pas-de-Calais using Machine Learning.

Mission 1: Decision support tool for detecting anomalies by classifying gas, electricity and water consumption

- Data processing
- Use of K-Means to group days according to their consumption levels
- Optimization and comparison of several classification models to classify new days
- Select the best model

Mission 2: Modeling of the college's heating, electricity and water consumption to detect operating anomalies and improve maintenance of the college

- Data processing
- Optimization and comparison of several regression models
- Select the best model

Technologies: Python, Pandas, Numpy, Seaborn, Matplotlib, Scikit_learn, Scipy, RStudio

Internship Data Scientist – CReSTIC
April 2016 – September 2016
Implementation of a Convolutional Neural Network (CNN) for the classification of infrared thermographic images

- Data preparation
- Creation of the CNN model
- Independence of the classification results from the energy deposit

Technologies: Python, Pandas, Numpy, Seaborn, Matplotlib, Scikit_learn, Tensorflow.Keras
 

Doctorate specialty Data Scientist 

2016 – 2020 University of Artois, FSA Béthune 

Master 2 statistics for evaluation and forecasting

2015 – 2016 University of Reims-Champagne Ardenne