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Schedule Interview NowMaham I. Data Science, Machine Learning and AI Platforms
My name is Maham I. and I have over 4 years of experience in the tech industry. I specialize in the following technologies: Python, Data Analysis, Data Science, Machine Learning, Machine Learning Algorithm, etc.. I hold a degree in . Some of the notable projects I’ve worked on include: Targeted Developer Sourcing for Tech Hiring, Web Scraping- Python scraper, Custom AI Automation, Competitor Research & Deep-Dive Analysis for DTC Brands, Ambulance Siren Detection By Machine Learning Algorithm, etc.. I am based in Lahore, Pakistan. I've successfully completed 6 projects while developing at Softaims.
Information integrity and application security are my highest priorities in development. I implement robust validation, encryption, and authorization mechanisms to protect sensitive data and ensure compliance. I am experienced in identifying and mitigating common security vulnerabilities in both new and existing applications.
My work methodology involves rigorous testing—at the unit, integration, and security levels—to guarantee the stability and trustworthiness of the solutions I build. At Softaims, this dedication to security forms the basis for client trust and platform reliability.
I consistently monitor and improve system performance, utilizing metrics to drive optimization efforts. I’m motivated by the challenge of creating ultra-reliable systems that safeguard client assets and user data.
Main technologies
Data Science, Machine Learning and AI Platforms
4 years
Python
1 Year
Data Analysis
1 Year
Data Science
3 Years
Additional skills
- Python
- Data Analysis
- Data Science
- Machine Learning
- Machine Learning Algorithm
- Computer Vision
- Python Scikit-Learn
- pandas
- NumPy
- Seaborn
- Matplotlib
- Testing
Direct hire
Potentially possible
Previous Company
Systems Limited
Experience Highlights
Targeted Developer Sourcing for Tech Hiring
Sourced backend/full-stack developers for a startup, based on strict hiring criteria. Focused on candidates with 3–5 years’ experience, short job tenures (2–3 years max), and minimal role switching. Filtered out profiles with experience in Java, Python, C#, AI, frontend, or leadership roles. Preferred candidates with startup backgrounds and optional React experience. Researched across multiple companies and platforms to deliver high-quality, relevant leads aligned with company goals.
Web Scraping- Python scraper
Built a Python scraper to extract detailed investor profiles from the NFX Signal platform using GraphQL API. It fetches data by category (e.g., SaaS, CRM), handles pagination, and retrieves full investor details including firm, social links, investments, and education. Includes rate limit handling, exponential backoff, and random delays to avoid detection. Saves data in JSONL format for easy processing. Used Python, Requests, Selenium, and Webdriver Manager.
Custom AI Automation
Custom AI automation solution, focusing on automating RFQ validation and price comparison tasks. The project involved handling large sets of supplier and pricing data, applying custom logic for validation, and automating the data processing workflow. It’s the type of solution that can easily be applied to industries like tech, banking, or manufacturing, where accurate and efficient data handling is critical.
Competitor Research & Deep-Dive Analysis for DTC Brands
Conducted in-depth competitor research for DTC brands by analysing over 50+ data points, including web traffic, customer reviews, product positioning, CTAs, funnel stages (ToFu/MoFu/BoFu), ad creatives, personalisation, trust signals, and content strategy. Mapped landing pages, quiz flows, and product pages to uncover growth hooks, conversion tactics, and brand messaging. Delivered insights to benchmark competitors and guide strategic improvements.
Ambulance Siren Detection By Machine Learning Algorithm
Project Description: I am an experienced data scientist specializing in Machine Learning models, seeking to leverage my skills and expertise on the SoftAims platform. In a recent project, I developed a robust audio classification system to distinguish between ambulance sirens and other road sounds. Project Overview: Objective: The primary goal was to build a RandomForestClassifier capable of accurately classifying audio samples into two categories: "Ambulance" and "Other." Data Collection: I collected a diverse dataset containing audio samples of ambulance sirens and various road sounds. The dataset was organized into folders for each class. Feature Extraction: Leveraging the librosa library, I implemented a feature extraction function to compute Mel-frequency cepstral coefficients (MFCCs) from the audio files. Model Training: A RandomForestClassifier with 100 estimators was trained on the extracted MFCC features. The model achieved high accuracy in distinguishing between ambulance and other road sounds. Model Persistence: The trained model, along with the label encoder used for encoding class labels, was saved to files using the joblib library for future use. Technologies Used: Python NumPy librosa scikit-learn (sklearn) RandomForestClassifier joblib Outcome: The final model and label encoder are saved as "random_forest_model.joblib" and "label_encoder.joblib," respectively. These files can be readily employed for real-time audio classification tasks.
Education
University of the Punjab
in Computer science
Languages
- English
- Urdu
Github