Ali Ahmad

Data Scientist

https://www.opentalent.in/ali-ahmad-2

DS Final Year - FAST NUCES

Key Strengths

Strong foundational knowledge in Python for data science and machine learning.
Demonstrated ability to build end-to-end data pipelines and analytical projects.
Experience with various machine learning algorithms (KNN, Decision Tree, SVM, Logistic Regression, Ensemble).
Exposure to advanced concepts like explainability (SHAP), cross-validation, hyperparameter tuning (GridSearchCV), and dimensionality reduction (PCA/t-SNE).
Practical experience with LLMs and NLP tasks (sentiment analysis, auto-reply generation) using Cohere API.
Proficiency in data mining, time series modeling, and clustering techniques.
Experience with web scraping (Playwright) and building interactive applications (Streamlit).

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's portfolio showcases a strong passion for data science and AI, with numerous personal projects demonstrating initiative and a desire to learn and apply cutting-edge technologies. The projects cover a broad range of topics from traditional ML to LLMs and computer vision, indicating a versatile and curious mindset. The 'DS Final Year - FAST NUCES' role suggests an academic background aligned with the target role. However, the lack of team-based projects or professional experience makes it challenging to fully assess collaboration and cultural integration in a corporate setting.

Soft Skills & Operational Fit

The candidate's project descriptions indicate a proactive and self-driven individual, capable of tackling complex problems independently. The diversity of projects suggests adaptability and a strong learning aptitude. However, without formal assessment data for communication, logical reasoning, or teamwork, it's difficult to fully assess operational fit and soft skills.

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Evaluating your skill match against the job requirements…

Assessing your cultural and operational fit

Projects

customer-support-ai

June 15, 2026 – Present

An AI pipeline that classifies customer support messages, detects sentiment, and generates professional auto-replies using the Cohere API — all in a single LLM call per message.

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turo-onboard

June 15, 2026 – Present

Paste a Turo listing URL → get a full operator onboarding package in 60 seconds. Scrapes listing data with Playwright, analyzes with Cohere AI, renders in Streamlit.

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ml-iris-classification-pipeline-

May 28, 2026 – Present

Supervised learning pipeline comparing KNN, Decision Tree, SVM, Logistic Regression & Ensemble on the Iris dataset. Includes SHAP explainability, cross-validation, GridSearchCV, PCA/t-SNE, and an interactive prediction interface.

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CapitalIQ-Rule-Based-Finance-Investment-Advisor-Chatbot

May 28, 2026 – Present

A production-quality rule-based AI chatbot built for the DecodeLabs AI Internship (Batch 2026). Covers 16 finance & investment intents using an O(1) dictionary-based IPO architecture, confidence scoring, conversation memory, and 61 unit tests — no ML dependencies.

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harris-lk-vehicle-tracker

May 17, 2026 – Present

Feature-based vehicle tracker built with Harris corners + Lucas–Kanade optical flow. Handles background competition, partial occlusion, and frame-exit degradation in 4K footage — no deep learning, no GPU required.

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efficiency-vs-performance-peft-research

April 27, 2026 – Present

A research-driven empirical analysis of efficiency vs performance trade-offs in PEFT techniques using DistilBERT, multi-dataset benchmarking, multi-seed evaluation, and statistical validation.

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pk-crop-prices-analysis-and-trend-discovery

April 27, 2026 – Present

This project performs end-to-end data mining and analysis using machine learning, time series modeling, and clustering techniques. It includes data preprocessing, feature engineering, predictive modeling, and temporal pattern analysis to extract meaningful insights and evaluate model performance.

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AI-Assignment-2

April 3, 2026 – Present

Artificial Intelligence Assignment 2 implementing hill climbing, genetic algorithms, and a driver positioning problem using Python and Jupyter notebooks.

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snake-game-x86-asm

March 24, 2026 – Present

Classic Snake game written in x86 real-mode Assembly — runs in DOS VGA text mode with direct hardware rendering, BIOS interrupts, and zero external dependencies.

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MockExaminationApp

May 27, 2025 – Present

Mock Examination App is a Streamlit-based web platform designed for students to practice mock tests across various subjects. It features secure user authentication, randomized exam questions, real-time scoring, user progress tracking, and an admin panel for managing users and privileges.

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Key Strengths

Strong foundational knowledge in Python for data science and machine learning.
Demonstrated ability to build end-to-end data pipelines and analytical projects.
Experience with various machine learning algorithms (KNN, Decision Tree, SVM, Logistic Regression, Ensemble).
Exposure to advanced concepts like explainability (SHAP), cross-validation, hyperparameter tuning (GridSearchCV), and dimensionality reduction (PCA/t-SNE).
Practical experience with LLMs and NLP tasks (sentiment analysis, auto-reply generation) using Cohere API.
Proficiency in data mining, time series modeling, and clustering techniques.
Experience with web scraping (Playwright) and building interactive applications (Streamlit).

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit

Ali Ahmad

Key Strengths

Cultural & Operational Fit

Top Skills

Skills

Experience

Projects

Key Strengths

Cultural & Operational Fit