Mohamed Benallal Hassani

Data Factory services at ENGIE Presales: ▪ Build and promote Dataiku offering to ENGIE business units ▪ Build a POC for EV Mobility Dashboard in Databricks (on AWS environment) ▪ Sizing and design of a GenAI based solution for Finance (Treasury): Databricks, OpenAI, Power Platform ▪ Sizing and technical study (Computation costs in AWS, activities, efforts) of an analytics solution for EV Mobility ▪ Technical proposition of a migration of a data platform: from Informatica and SQL server to Databricks on AWS ▪ Benchmark Databricks capabilities and AWS analytics services for data platform modernization for ENGIE Australia Delivery: ▪ Lead Data Engineering team for a Sustainability reporting project: Data modeling, ETL with Python and Dataiku ▪ Lead Data Engineering team for HR Learning reporting project: SAP SAC Dashboards using Python and Dataiku ▪ Lead and manage a team of 4 data engineers offshore and onshore: capacity planning, coordination and mentoring ▪ Data exploration and time series modeling for Predict Cash project for ENGIE Finance (Treasury): AWS SageMaker ▪ Handle technical escalations and ensure technical engagement of the adopted solutions ▪ Technical audit of data platform of 3 ENGIE subsidiaries and suggest a target platform compliant to CDO data policy Other: ▪ Keynote speaker in a major event: EVERYDAY AI by Dataiku ▪ Present achievements of Capgemini x ENGIE in different events

Skills: Data Pipelines · Technical Presentations · Amazon Web Services (AWS) · Client Relations · Team Building · Technical Solution Design · Dataiku DSS · AWS SageMaker · Requirements Gathering · Interpersonal Communication · Data Modeling · Databricks Products · Customer Facing Roles · Offshore Software Development · Team Leadership

Data & Smart Metering Department ➤ Built a Python-based data processing pipeline in order to solve unissued invoices – deployed in an Oracle Virtual Machine ➤ Built an R-based Anomaly Detection tool in order to detect gaz consumption outliers (takes in 3 database sources) ➤ Built an R and SQL-based Classification tool in order to detect redeemable invoices ➤ Built an R-based end-to-end Data Processing pipeline in Dataiku platform to calculate consumption for missing invoices ➤ Built a Linear Regression model in order to predict missing consumption using Python ➤ Created documentation and project artifact for maintaining production code in virtual machine platform (analytics schema, user stories, process diagrams, work-flows, and technical requirements) Tech Stack : Python, R, SQL, Dataiku DSS, DB Oracle, Rstudio, Eclipse, VM Oracle, Bitbucket / TortoiseGit, Jupyter Outcome: 💡 Reduced the number of unbilled consumers by 50% 💡 Reduced the overall number of outlier invoices by 90% 💡 Reduced the number of refunded invoices

➢ Coordinating and liaisoning 2 technical teams to analyze unbilled consumers problems and design technical solutions ➢ Identified main business and data challenges, prioritized data use cases, while being the front line for client ➢ Produced detailed analysis that facilitates internal team discussion and client facing deliverables ➢ Prepared and delivered management-level presentations ➢ Acted as the point-person on defining business rules and prioritizing features for a data product (built using a Machine Learning classification model to predict potential unbilled consumers)