Ahmadi, Kuwait
Performance-driven professional with over a decade of experience in Oil & Gas operations, enriched by extensive coursework and a keen focus on data analytics and machine learning. Leveraged a robust background in chemical engineering and supply chain management to drive sustainable practices and operational excellence. Adept at project management, with certifications in Agile, Scrum, and Lean Six Sigma methodologies. Demonstrated leadership in guiding large cross-functional teams of over 50 members. Proficient in synthesizing large datasets to inform strategic decision-making and enhance operational efficiency. Fluent in Arabic, French, and English, with basic proficiency in Spanish.
KNPC • 2009 - Present
AGRP II (2017 - Present):
AGRP I (2009 - 2017 & 2021-2022):
Business Administration and Management, General • 2016 - 2020
Master of Engineering in Process Engineering & Environment • 2003 - 2009
Master of Reserarch in Process Engineering • 2008 - 2009
For more projects, please consult my data science portfolio.
In this project, I analyzed the 2021 playoffs shooting data of four notable NBA players to offer strategic insights to a university basketball team. Using Python libraries, I conducted an exploratory data analysis focusing on shot locations, success rates, and defender influences. The visualizations created help delineate effective shooting quadrants and could aid the team in enhancing their strategies and performance.
I conducted a detailed analysis of a dataset from Sprocket Central Pty Ltd, a medium-sized bike and cycling accessories organization, to assess data quality. The analysis identified several data quality issues, setting the stage for strategies to enhance data handling and improve the accuracy of future insights.
In this project, I used the Chi-Square Test for Independence to analyze the effectiveness of two different mailers in a grocery retailer's promotional campaign. The analysis found no statistically significant difference in the signup rates between the two mailer types, suggesting that higher-cost mailers might not yield a higher ROI.
I employed k-means clustering to segment a supermarket chain's customer base into three distinct groups based on their shopping patterns. The segmentation revealed different customer preferences, aiding in the development of more focused business strategies and customer communications.
I analyzed a grocery retailer's previous marketing campaign data to enhance targeting accuracy for their delivery club membership using machine learning techniques. The Random Forest model emerged as the most reliable, setting the stage for more focused and cost-effective future campaigns.
In this project, I developed predictive models to forecast the loyalty scores of a grocery retailer's customer base using transaction data. The Random Forest model proved most accurate, promising to enhance customer tracking and targeting strategies significantly.
I used Principal Component Analysis (PCA) to compress a dataset of 100 unlabeled features into a more manageable size for classifying potential buyers of Ed Sheeran’s latest album. The Random Forest Classifier utilized on the compressed data achieved a 93% classification accuracy, suggesting a viable strategy for targeted marketing.
I analyzed a grocery retailer's "Delivery Club" campaign using Causal Impact Analysis to quantify the sales uplift post-campaign. The analysis indicated a significant 41.1% increase in sales among members, pointing to the campaign's effectiveness and setting the ground for further optimization strategies.
Advancing skills in data manipulation, analysis, and visualization using Python, alongside gaining a deeper understanding of machine learning algorithms.
Developed a strong foundation in data visualization and analytics, mastering Tableau to transform raw data into insightful dashboards.
Acquired expertise in deep learning frameworks and algorithms, and gained practical experience in implementing machine learning models to solve real-world problems.
Engaged in a comprehensive data science training program covering a wide array of topics including statistical analysis, machine learning algorithms, and advanced data visualization techniques. Developed a strong foundation in Python and SQL, and worked on real-world projects to hone practical data science skills.
Focused on data analytics, optimization, and machine learning to enhance supply chain decision-making and operational efficiency. This program includes 5 graduate level courses, including supply chain analytics, fundamentals, design, dynamics and technology.
Certified in Scrum methodology, enhancing the ability to effectively manage data-centric projects by employing Agile principles and ensuring a data-driven approach to continuous improvement.
Explored data-driven decision-making techniques to effectively manage projects, ensuring timely delivery and adherence to budget constraints.
Ahmadi, Kuwait
Performance-driven professional with over a decade of experience in Oil & Gas operations, enriched by extensive coursework and a keen focus on data analytics and machine learning. Leveraged a robust background in chemical engineering and supply chain management to drive sustainable practices and operational excellence. Adept at project management, with certifications in Agile, Scrum, and Lean Six Sigma methodologies. Demonstrated leadership in guiding large cross-functional teams of over 50 members. Proficient in synthesizing large datasets to inform strategic decision-making and enhance operational efficiency. Fluent in Arabic, French, and English, with basic proficiency in Spanish.
KNPC • 2009 - Present
AGRP II (2017 - Present):
AGRP I (2009 - 2017 & 2021-2022):
Business Administration and Management, General • 2016 - 2020
Master of Engineering in Process Engineering & Environment • 2003 - 2009
Master of Reserarch in Process Engineering • 2008 - 2009
For more projects, please consult my data science portfolio.
In this project, I analyzed the 2021 playoffs shooting data of four notable NBA players to offer strategic insights to a university basketball team. Using Python libraries, I conducted an exploratory data analysis focusing on shot locations, success rates, and defender influences. The visualizations created help delineate effective shooting quadrants and could aid the team in enhancing their strategies and performance.
I conducted a detailed analysis of a dataset from Sprocket Central Pty Ltd, a medium-sized bike and cycling accessories organization, to assess data quality. The analysis identified several data quality issues, setting the stage for strategies to enhance data handling and improve the accuracy of future insights.
In this project, I used the Chi-Square Test for Independence to analyze the effectiveness of two different mailers in a grocery retailer's promotional campaign. The analysis found no statistically significant difference in the signup rates between the two mailer types, suggesting that higher-cost mailers might not yield a higher ROI.
I employed k-means clustering to segment a supermarket chain's customer base into three distinct groups based on their shopping patterns. The segmentation revealed different customer preferences, aiding in the development of more focused business strategies and customer communications.
I analyzed a grocery retailer's previous marketing campaign data to enhance targeting accuracy for their delivery club membership using machine learning techniques. The Random Forest model emerged as the most reliable, setting the stage for more focused and cost-effective future campaigns.
In this project, I developed predictive models to forecast the loyalty scores of a grocery retailer's customer base using transaction data. The Random Forest model proved most accurate, promising to enhance customer tracking and targeting strategies significantly.
I used Principal Component Analysis (PCA) to compress a dataset of 100 unlabeled features into a more manageable size for classifying potential buyers of Ed Sheeran’s latest album. The Random Forest Classifier utilized on the compressed data achieved a 93% classification accuracy, suggesting a viable strategy for targeted marketing.
I analyzed a grocery retailer's "Delivery Club" campaign using Causal Impact Analysis to quantify the sales uplift post-campaign. The analysis indicated a significant 41.1% increase in sales among members, pointing to the campaign's effectiveness and setting the ground for further optimization strategies.
Advancing skills in data manipulation, analysis, and visualization using Python, alongside gaining a deeper understanding of machine learning algorithms.
Developed a strong foundation in data visualization and analytics, mastering Tableau to transform raw data into insightful dashboards.
Acquired expertise in deep learning frameworks and algorithms, and gained practical experience in implementing machine learning models to solve real-world problems.
Engaged in a comprehensive data science training program covering a wide array of topics including statistical analysis, machine learning algorithms, and advanced data visualization techniques. Developed a strong foundation in Python and SQL, and worked on real-world projects to hone practical data science skills.
Focused on data analytics, optimization, and machine learning to enhance supply chain decision-making and operational efficiency. This program includes 5 graduate level courses, including supply chain analytics, fundamentals, design, dynamics and technology.
Certified in Scrum methodology, enhancing the ability to effectively manage data-centric projects by employing Agile principles and ensuring a data-driven approach to continuous improvement.
Explored data-driven decision-making techniques to effectively manage projects, ensuring timely delivery and adherence to budget constraints.