Mohamed Abderrahim NAIT ALI
Alger, Algeria
Mohamed Abderrahim NAIT ALI
Data Science
Category : Computer assistance
Data Scientist and Machine Learning Engineer with a background in Energy and Mechanical Engineering (Master's degree, USTHB) and hands on experience turning raw data into clear business decisions.
I build end to end data solutions: cleaning and exploring datasets, engineering features, training and tuning machine learning and deep learning models (scikit learn, TensorFlow, Keras), and presenting results through dashboards and reports that non technical stakeholders can act on.
Recent work includes:
Image classification and transfer learning projects (VGG16, MobileNetV2) for defect detection and object recognition.
Natural language processing with BERT for sentiment analysis.
Recommendation systems using clustering (K Means) and dimensionality reduction (PCA, t SNE).
SQL based data analysis on e commerce datasets, from query design to business insights.
Data visualization and storytelling with Matplotlib and Tableau, including factory downtime and operational analysis.
I also completed virtual experience programs with JPMorgan Chase (quantitative research), BCG X (e commerce analytics), Quantium (customer segmentation and A/B testing), and Deloitte (data visualization).
I hold an NVIDIA Deep Learning certification and a Cambridge CAE C1 English certificate, and I currently work as an AI Data Annotator, which keeps me close to real world data quality issues that most analysts never see.
I write clean, documented, production ready code and I am comfortable working independently or as part of a team, in English or French.
If you need someone who can take a dataset from raw files to a finished model or dashboard, and explain the results clearly along the way, I am ready to help.
I build end to end data solutions: cleaning and exploring datasets, engineering features, training and tuning machine learning and deep learning models (scikit learn, TensorFlow, Keras), and presenting results through dashboards and reports that non technical stakeholders can act on.
Recent work includes:
Image classification and transfer learning projects (VGG16, MobileNetV2) for defect detection and object recognition.
Natural language processing with BERT for sentiment analysis.
Recommendation systems using clustering (K Means) and dimensionality reduction (PCA, t SNE).
SQL based data analysis on e commerce datasets, from query design to business insights.
Data visualization and storytelling with Matplotlib and Tableau, including factory downtime and operational analysis.
I also completed virtual experience programs with JPMorgan Chase (quantitative research), BCG X (e commerce analytics), Quantium (customer segmentation and A/B testing), and Deloitte (data visualization).
I hold an NVIDIA Deep Learning certification and a Cambridge CAE C1 English certificate, and I currently work as an AI Data Annotator, which keeps me close to real world data quality issues that most analysts never see.
I write clean, documented, production ready code and I am comfortable working independently or as part of a team, in English or French.
If you need someone who can take a dataset from raw files to a finished model or dashboard, and explain the results clearly along the way, I am ready to help.
Working hours
- Monday:08h00 To 18h00
- Tuesday:08h00 To 18h00
- Wednesday:08h00 To 18h00
- Thursday:08h00 To 18h00
- Friday:08h00 To 18h00
- Saturday:Not available
- Sunday:Not available
I build end to end data solutions: cleaning and exploring datasets, engineering features, training and tuning machine learning and deep learning models (scikit learn, TensorFlow, Keras), and presenting results through dashboards and reports that non technical stakeholders can act on.
Data Scientist and Machine Learning Engineer with a background in Energy and Mechanical Engineering (Master's degree, USTHB) and hands on experience turning raw data into clear business decisions.
I build end to end data solutions: cleaning and exploring datasets, engineering features, training and tuning machine learning and deep learning models (scikit learn, TensorFlow, Keras), and presenting results through dashboards and reports that non technical stakeholders can act on.
Recent work includes:
Image classification and transfer learning projects (VGG16, MobileNetV2) for defect detection and object recognition.
Natural language processing with BERT for sentiment analysis.
Recommendation systems using clustering (K Means) and dimensionality reduction (PCA, t SNE).
SQL based data analysis on e commerce datasets, from query design to business insights.
Data visualization and storytelling with Matplotlib and Tableau, including factory downtime and operational analysis.
I also completed virtual experience programs with JPMorgan Chase (quantitative research), BCG X (e commerce analytics), Quantium (customer segmentation and A/B testing), and Deloitte (data visualization).
I hold an NVIDIA Deep Learning certification and a Cambridge CAE C1 English certificate, and I currently work as an AI Data Annotator, which keeps me close to real world data quality issues that most analysts never see.
I write clean, documented, production ready code and I am comfortable working independently or as part of a team, in English or French.
If you need someone who can take a dataset from raw files to a finished model or dashboard, and explain the results clearly along the way, I am ready to help.
I build end to end data solutions: cleaning and exploring datasets, engineering features, training and tuning machine learning and deep learning models (scikit learn, TensorFlow, Keras), and presenting results through dashboards and reports that non technical stakeholders can act on.
Recent work includes:
Image classification and transfer learning projects (VGG16, MobileNetV2) for defect detection and object recognition.
Natural language processing with BERT for sentiment analysis.
Recommendation systems using clustering (K Means) and dimensionality reduction (PCA, t SNE).
SQL based data analysis on e commerce datasets, from query design to business insights.
Data visualization and storytelling with Matplotlib and Tableau, including factory downtime and operational analysis.
I also completed virtual experience programs with JPMorgan Chase (quantitative research), BCG X (e commerce analytics), Quantium (customer segmentation and A/B testing), and Deloitte (data visualization).
I hold an NVIDIA Deep Learning certification and a Cambridge CAE C1 English certificate, and I currently work as an AI Data Annotator, which keeps me close to real world data quality issues that most analysts never see.
I write clean, documented, production ready code and I am comfortable working independently or as part of a team, in English or French.
If you need someone who can take a dataset from raw files to a finished model or dashboard, and explain the results clearly along the way, I am ready to help.
I hold a Master's degree in Energy and Mechanical Engineering from USTHB (University of Science and Technology Houari Boumediene), one of Algeria's leading engineering schools. My training covered thermodynamics, fluid mechanics, energy systems, and mechanical design, along with strong foundations in mathematics, physics, and problem solving under real world constraints.
This engineering background gave me a rigorous, analytical way of thinking that transferred directly into data science. Engineers are trained to model systems, work with numbers, test hypotheses, and validate results before trusting them. That mindset is exactly what data science needs when working with datasets, building models, and interpreting results.
I later transitioned into data science through an intensive 600 hour bootcamp with GomyCode, combining my engineering fundamentals with modern tools like Python, machine learning, and deep learning. This mix of engineering discipline and data science skills is one of my strongest assets: I don't just build models, I understand the underlying systems and reasoning behind the data.
This engineering background gave me a rigorous, analytical way of thinking that transferred directly into data science. Engineers are trained to model systems, work with numbers, test hypotheses, and validate results before trusting them. That mindset is exactly what data science needs when working with datasets, building models, and interpreting results.
I later transitioned into data science through an intensive 600 hour bootcamp with GomyCode, combining my engineering fundamentals with modern tools like Python, machine learning, and deep learning. This mix of engineering discipline and data science skills is one of my strongest assets: I don't just build models, I understand the underlying systems and reasoning behind the data.
- 🇬🇧 English
- 🇫🇷 French
- 🇲🇦 Arabic
Please sign in as a customer to give your feedback
Sponsored professionals
Need a service ?
Do you want to hire the services of this Professional ?
Post your need and you will receive dozens of proposals from the Professionals of the community.
