Mahmoud Trigui

Mahmoud TRIGUI « Data Scientist » Street El Ain Km 6, Environment Boulevard 3042 Sfax – TUNISIA Mobile phone TN : (- E-mail-Self-tape https://youtu.be/OUljBuMxoXU LinkedIn www.linkedin.com/in/mahmoudtrigui Skill IQ https://app.pluralsight.com/profile/mahmoud-trigui Acclaim www.youracclaim.com/users/mahmoud-trigui/badges Zindi https://zindi.africa/users/Mahmoud_Trigui/competitions Personality https://www.16personalities.com/profiles/46e44008a0245 Profile A Statistics and Data Analysis Engineer seeking always to advance and promote his knowledge and career in Data Science for Machine Learning (Structured Data), Time Series and NLP classification with R programming. My early beginnings included Statistics and Computer Science background along with a hard-preparatory studies in Mathematics and Physics, contributed all positively to a solid thinking and resolving problems approach. I have gained a prestigious knowledge through my work, and I never missed to be up to date in everything related to machine learning techniques, that's why I always continue to learn by working on real projects on DS platform like Zindi, working in a Freelance experience and gaining certificates from the most prestigious partners with MIT X Courses, Udacity, Coursera, Stanford Online and edX. Professional Experiences Professional Positions  January 2018 – March 2020 :: Groupe Tunisie Telecom (Tunis, TUNISIA) : Data Scientist at the 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  Data Analysis : detailed exploratory analysis including correlation and different tests  Clustering : identifying clusters of customers who have the same behavior  Classification : assigning new customers to the clusters that have been created  Multi_Sim Users Detection ::  Identify from based hypothesis, the users who have two sim providers  Family Communities ::   Distinguish into each predefined community, those who lives in the same house 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)  June 2016 – December 2017 :: Groupe Tunisie Telecom (Tunis, TUNISIA) : Data Analyst at the CVM and Data-Mining department (SQL developer & SAS (Guide/Miner) user):  Reports Sheets and Dashboards for various analysis (Analyzing the Network quality KPI,    Monitoring CVM results, Customer profiling, Sim-card sells pattern, Market growth) Automation of the reporting process for PowerPoint presentations via VBA Verification of anomalies in the databases Recommendation of new offers and retention actions (Targeting campaigns, Try&Buy)  November 2018 – January 2020 :: Data Scientist at Freelance :  December 2019 – January 2020, Machine Learning LAB preparation, covering ::  General Introduction to machine learning  Supervised learning(Regression/Classification): Linear, Logistic, SVM & Decision Tree  Unsupervised Learning (K-means)  Tricks for good modeling  Notebooks with Python code for each algorithm  August – September 2019, Text Classification, NLP (R&D) ::  Built a model that can be used for a future data to be classified into categories.  February – April 2019, Vehicle Weight Estimation  From very sensitive sensors values, being able to predict the approximate weight  December 2018 – January 2019, Facebook Page Analysis ::  Using the page's API, collect info about: fans, number of comments, daily visits …  Applying sentimental analysis on the collected comments to see feedback of fans.  August 2019 – Present :: Machine Learning Competitor on ZINDI platform for (R programmer):  AI4D Predict the Global Spread of COVID-19 (Time Series), April 2020   Ranked 4th position (only 47 succeed to submit among 884 competitors) Predict the spread of death rates for the coronavirus globally  Uber Movement SANRAL Cape Town, January 2020    Ranked TOP 11% ‘13th‘ position (only 113 succeed to submit among 711 competitors) Predict when and where road incidents will occur next in Cape Town AI HACKATHON Tunisia 2019, September 2019   Ranked 6th position (only 54 succeed to submit among 191 competitors) Help Tunisian company STEG detect fraud  Basic Needs Basic Rights Kenya - Tech4MentalHealth (NLP), July 2020    Sendy Logistics, November 2019    Ranked TOP 13% (only 678 succeed to submit among 1288 competitors) Predict which individuals are most likely to have or use a bank account IEEE Big Data Cup, October 2020   Ranked TOP 26% (only 431 succeed to submit among 1143 competitors) Predict the estimated time of arrival (ETA) for motorbike deliveries in Nairobi Financial Inclusion in Africa, August 2019    Ranked TOP 36% (only 492 succeed to submit among 900 competitors) Classify text from university students in Kenya towards a mental health chatbot Participated in the Challenge “Predicting Escalations in Customer Support” and submitting a solution (rank 39/42 who managed to submit) Uber Nairobi Ambulance Perambulation, January 2021    Ranked TOP 31% (only 331 succeed to submit among 1003 competitors) Use ML to create an optimized ambulance deployment strategy in Nairobi Wazihub Soil Moisture Prediction, October 2019   Ranked TOP 37% (only 96 succeed to submit among 700 competitors) Predict soil humidity in 5-minute increments in Senegal  Female-Headed Households in South Africa, February 2020   Ranked TOP 38% (only 258 succeed to submit among 498 competitors) Predict rate of household inferior to certain wage at each ward in South-Africa  Akeed Restaurant Recommendation in Oman, August 2020   Ranked TOP 45% (only 242 succeed to submit among 1249 competitors) Predict what restaurants customers are most likely to order from given the customer  UNICEF Arm 2030 Vision #1: Flood Prediction in Malawi, May 2020   Ranked TOP 53% (only 477 succeed to submit among 1633 competitors) Predict flood extent caused by storms in southern Malawi  South African COVID-19 Vulnerability Map, March 2020   Ranked TOP 52% (only 95 succeed to submit among 259 competitors) Infer important COVID-19 public health risk factors from outdated data  Sea Turtle Rescue, March 2020   Ranked TOP 59% (only 95 succeed to submit among 259 competitors) Forecast the of sea turtles Local Ocean Conservation will rescue each week in Kenya Professional internships  June – December 2015 :: LAAS-CNRS (Toulouse, FRANCE) Research Statistician :  Develop an automatic model to know from the owner's habits, if it's the owner who tries to enter or it's an intrusion based on the owner behavior, and then decide to launch the alarm or to deactivate it automatically (for the Smartfox project with Myfox company).  My book (in French) was published by the EUE (Editions Universitaires Européennes) within this link https://www.morebooks.de/store/es/book/gestion-automatique-d%E2%80%99unsyst%C3%A8me-de-s%C3%A9curisation-des-biens-%C3%A0-domicile/isbn/- Scientific Training  October 2018 :: AI lab with the IBM Watson Services, hosted by Sintegra Consulting : I was ranked first on more than 100 competitors and I was invited to do the lab as an instructor for the next iterations. Education  2016 :: MIT xPRO, Data Science: Data to Insights - :: ESSAI, Higher School of Statistics and Data Analysis, Engineering School - :: IPEIS, Preparatory institute for engineering studies (Mathematics- Physics)  2008 :: High School degree in "Computer Sciences" (First Class Honors) Computer & Analytical Skills  Statistical and Modeling software :: R, SAS-Guide, SAS E-Miner  Databases Management software :: Oracle DB (Toad), MySQL, MS SQL Server  Microsoft Office :: Good proficiency of the office pack (Excel, Word and PowerPoint)  Assessments ::    LinkedIn :: Machine Learning // R // Transact-SQL Pluralsight Expert in :: Data Science // Machine Learning // R as a Language // Data Analytics // Programming R for Data Analysis // Feature Engineering // Bringing Data Science into the Business // Microsoft Excel Data Insights // Interpreting Data with R // Cleaning data with R // Data Wrangling with R DataCamp Advanced in :: Understanding and Interpreting Data // R Programming // Importing & Cleaning Data with R // Machine Learning Fundamentals in R // Statistics Fundamentals with R // Data Manipulation with R Certificates & Badges  MIT Professional X :: Data Science : Data to Insights  Stanford Online ::  Statistical Learning (using R) // Machine Learning by Andrew Ng  Mining Massive Datasets (Big-Data Algorithms) // SQL  DataQuest ::  Udacity :: Data Analyst in R Path Data Analyst Track  Coursera ::  ML for Data Analysis with SAS & Python // Inferential and Predictive Statistics for Business  Text Mining and Analytics // Data Science Orientation // Regression Models with R  Managing Big Data with MySQL and TERADATA // Cluster Analysis in Data Mining  edX ::  Analyzing and Visualizing Data with Excel // Querying with Transact-SQL  Introduction to Python for Data Science  IBM Badges ::  Applied Data Science with R – Level 2 // Build your own Chatbot // Watson Studio  Node-Red : Basics to Bots // Data Refinery Essentials // DS Foundations – Level 2  Watson Machine Learning Essentials // Watson Assistant Foundations //Statistics 101  SAS Badges ::    Predictive Modeling and Text Mining // Correlation & Regression Decision Making with Data // Intro to Anova, Regression & Logistic Regression Exploratory Data Analysis // Intro to Statistical Concepts // SAS Programming 1  DataCamp Courses ::  Time Series with R Track (6 courses) // Forecasting Product Demand in R  Statistics with R : Correlation and Linear Regression // Hyperparameter Tuning in R  Intermediate Python for Data Science // Introduction to Machine Learning Languages  Arabic :: Mother tongue  French :: Fluent  English :: Intermediate