10/15/2026 updated
100 % available
Data Scientist, Data Analyst
Sfax, Tunisia
Only remote
Statistics and Data Analysis Engineering DegreeAbout me
Data Scientist with 10+ years of experience specializing in structured data analytics/machine learning, time series forecasting, and NLP solutions. With expertise in both R and Python ecosystems.
Data AnalysisCluster AnalysisData CleansingData MiningForecastingR (Programming Language)Machine LearningSAS (Software)SQL DatabasesStatisticsSupport Vector MachineSupervised LearningData ScienceToad (Software)Decision Trees
R, Data science, Machine Learning, Forcasting, Statistics, Data-Mining, SQL developer, SAS, Guide/Miner, Clustering, data cleaning, Data Analysis, K-means, SQL, Machine Learning, Supervised learning, Regression, SVM, Decision Tree, Unsupervised Learning, Data Science, Oracle DB, Toad, Analytics
Experience
Professional experience5 - 10 years
Freelancer since2018
Languages
ArabicNative speakerEnglishGoodFrenchGood
Project history
* Ranked TOP 11% '13th ' position (only 113 succeed to submit among 711 competitors)
* The objective of this competition is to predict when and where road incidents will
occur next in Cape Town
* The objective of this competition is to predict when and where road incidents will
occur next in Cape Town
CVM and Data-Mining department (SQL developer & SAS (Guide/Miner) user) :
* Clients Segmentation (Clustering & Classification) ::
* Data preparation: creating of the ABT, ensuring data quality, data cleaning
(imputation and transformation ...)
* Data Analysis : detailed exploratory analysis including correlation and different tests
* Clustering : identifying clusters of customers who have the same behavior within the
same group of clients (using K-means), detailed profiling of the segments for
validation
* Classification : assigning new customers to the clusters that have been created (using
decision Tree)
* The model was built with SAS (Guide and Miner)
* Multi_Sim Users Detection ::
* The goal is be able to identify from some pre-hypothesis, the users who have two sim
providers, include testing the hypothesis, detailed profiling of the target, giving a final
score to each client.
* Model was built with SAS (Guide and Miner)
* Family Communities ::
* From a predefined communities results, being able to distinguish into each
community, those who have a family relation (the big family and those who lives int
the same house) and those have not.
* Contributing of building and assisting in a series of workshops along with experts from SAS,
KPMG and Business & Decision, for different models including :
* Churn, Cross-sell and Community Link Analysis (SAS)
* Clients Segmentation (Clustering & Classification) ::
* Data preparation: creating of the ABT, ensuring data quality, data cleaning
(imputation and transformation ...)
* Data Analysis : detailed exploratory analysis including correlation and different tests
* Clustering : identifying clusters of customers who have the same behavior within the
same group of clients (using K-means), detailed profiling of the segments for
validation
* Classification : assigning new customers to the clusters that have been created (using
decision Tree)
* The model was built with SAS (Guide and Miner)
* Multi_Sim Users Detection ::
* The goal is be able to identify from some pre-hypothesis, the users who have two sim
providers, include testing the hypothesis, detailed profiling of the target, giving a final
score to each client.
* Model was built with SAS (Guide and Miner)
* Family Communities ::
* From a predefined communities results, being able to distinguish into each
community, those who have a family relation (the big family and those who lives int
the same house) and those have not.
* Contributing of building and assisting in a series of workshops along with experts from SAS,
KPMG and Business & Decision, for different models including :
* Churn, Cross-sell and Community Link Analysis (SAS)
* The goal is to be built a model that can be used for a future data to be classified into
categories.
categories.