- Promote and coordinate daily activities with the Lead AI/ML Instructor and other AI/ML Instructors, and contribute to building the calendar and additional activities for each cohort.- Act as support to the instructors on daily operations. Support daily activities for each cohort-based training, assisting the Lead AI/ML Instructor and other AI/ML Instructors, providing technical support to Fellows.- Organize and track other daily group activities than live classes, such as pair programming, sprint project group collaboration, etc. - Mentor Fellows on individual assignments, pair programming activities, sprint project and final graduation projects. - Run feedback tools on live classes or other activities and provide feedback to leadership to improve Anyone AI training activities. - Monitor and track individual Fellow progress and build periodical weekly reports on technical aspects and cohort based progress of each Fellows. - Help Fellows resolve tools problems and other technical issues they may have. - Contribute to adding and/or improving content of the Anyone AI platform (e.g you will be responsible of uploading daily lecture recordings or changing content that may be wrong or needs improvement). - Plan and provide special support to those Fellows that are behind schedule and need support in order to move into the next career sprints. - Clear student doubts during the live class, answer Fellows queries outside the live online class through our Discord internal channels. We aim to answer each Fellows query with 24 hours (Monday to Friday) from receiving the query, and provide 1:1 mentorship calls on sprint projects or practical assignments with no less than 48 hours since receiving the request from the Fellows. - Contribute to the development of Anyone AI community, supporting main program channels, providing admission support and mentorship and other activities related to maintaining and growing the community.

• Sentiment Analysis Project on Streaming Service Reviews: -Engineered data by manipulating textual content, conducting pre-processing, and applying TF-IDF vectorization for sentiment analysis. -Generated word embeddings to serve as the foundation for data vectorization. -Trained an SVM-based Sentiment Analysis model proficient in identifying positive and negative sentiments.• Prediction of Vehicle Brand and Model from Unstructured Images in E-commerce: - Visualized, cleaned, and pre-processed a dataset encompassing 196 classes for predicting vehicle brand and model. - Achieved an 82% F1-score by training a classification model using Convolutional Neural Networks (CNN) with the ResNet50 architecture. - Engineered an API service with Flask and incorporated a Redis message broker. Orchestrated services within Docker containers.• Creditworthiness Prediction for Mortgage Applicants: - Managed and visualized a vast dataset of over 350,000 transactions for assessing creditworthiness. - Conducted comprehensive data pre-processing and trained diverse supervised models (RandomForest, XGBoost, LightGBM), yielding a ROC-AUC score surpassing 72%.

- Led a team of data engineers and scientists to optimize and manage data pipelines for a cloud-based research collaboration platform.- Refactored FastAPIs into the MVC pattern, enhancing API performance and scalability.- Managed the ingestion, integration, and quality control of data from multiple diverse sources.Developed and implemented data pipelines for seamless extraction, transformation, and loading (ETL) processes.- Optimized PostgreSQL databases, improving query efficiency and data retrieval speeds.- Implemented advanced data wrangling techniques to cleanse and unify raw data, ensuring consistency and accuracy.- Created an innovation score formula for patent publications, adding valuable insights for research evaluations.- Mentored and guided team members in technical tasks, fostering a collaborative and innovative work environment.

• Managed the ingestion and integration of data from diverse sources including databases and APIs.• Developed a formula to calculate an innovation score based on the closest year of a patent publication.• Sourced new data from various platforms, unified, cleaned, organized, and uploaded it to PostgreSQL.• Debugged and tested data pipelines for seamless dataflow from multiple sources.• Managed PostgreSQL databases and optimized queries for improved efficiency.• Cleansed and transformed raw data using various data wrangling techniques, ensuring data quality and consistency.• Assisted in constructing and maintaining data pipelines that extract, transform, and load data from diverse sources.

- Developed mathematical models to optimize the allocation and scheduling of public transportation fleets.- Collaborated with urban planners and transportation authorities to ensure models meet real-world needs.- Evaluate the impact of fleet allocation strategies on commuter satisfaction and system efficiency.- Refined models based on feedback from field tests and pilot implementations to enhance practical application.

• Trained Large Language Models to develop complex tasks in Data Analysis.• Categorized prompts according to multiple categories and specifications.• Evaluated responses generated by LLM, assessing them according to different parameters.

• Led data collection efforts from diverse sources including surveys, social media, and public records.• Conducted statistical analysis, hypothesis testing, and descriptive analysis to support political strategies.• Automated statistical analysis encompassing data collection from diverse sources, handling data cleaning procedures, and culminating in comprehensive result generation.• Created visually appealing data visualizations and dashboards for non-technical stakeholders.