Awais Arshad

Results-driven Data Science graduate skilled in Python, Machine Learning, Exploratory Data Analysis (EDA), Feature Engineering, and Agentic AI. Completed a Data Science internship at WorkDesk VA, where I applied EDA, feature engineering, and machine learning techniques to support data-driven decision-making. Developed advanced AI projects, including an autonomous LLM-based multi-agent scientific experimentation system and PitchDeck, a full-stack multimodal AI pitch coaching platform as my Final Year Project.

Data Science Intern at WorkDesk VA

• Performed Exploratory Data Analysis (EDA) using Python libraries such as Pandas, Matplotlib, and Seaborn to identify trends, patterns, and outliers in training datasets.
• Applied feature engineering techniques to preprocess data and improve the performance of machine learning models.
• Implemented and evaluated machine learning models, including Logistic Regression and Decision Trees, using cross-validation, hyperparameter tuning, and performance metrics.
• Trained Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) as part of hands-on learning assignments.
• Evaluated model performance using accuracy, precision, recall, F1-score, and confusion matrices, and applied regularization and dropout techniques to reduce overfitting.

Bachelor of Data Science in Data Science – NUCES-FAST (2022-08 – 2026-06)

FSC (Pre-Engineering) in Pre-Engineering – Cadet College Wana (S.W.A) (2019-04 – 2021-05)