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Data Science with less than a year in Machine Learning and NLP
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Detail-oriented Data Science graduate from FAST-NUCES with hands-on experience in machine learning, NLP, and data quality workflows. Adept at applying structured analytical thinking to labeling and classification tasks including content categorization, spam detection, and semantic annotation. Familiar with guidelines-driven workflows and committed to maintaining high accuracy and consistency across large-scale annotation tasks.
FAST-NUCES
B.S. in Data Science
August 1, 2022 – June 30, 2026
ITSOLERA
Data Science Intern
July 1, 2025 – September 30, 2025
Islamabad, Islamabad Capital Territory, Pakistan
InVision Space - Al-Powered Interior Design Platform
June 1, 2026 – Present
Developed and validated structured labeling schemas for room images, furniture categories, and spatial object relationships to power AI-driven object placement. Applied consistent multi-label classification logic to annotate object types, scales, and contextual significance - directly analogous to Primary vs. Secondary content classification on platforms like WDP.
Comparative Bug Detection via Code Classification
June 1, 2026 – Present
Performed binary label classification (buggy vs. non-buggy code pairs) and validated annotation quality across the CodeXGLUE/Devign dataset, ensuring consistency across thousands of labeled samples. Applied Chain-of-Thought prompting strategies to improve classification accuracy, reinforcing structured reasoning for edge-case annotation decisions.
Real-Time Business Intelligence Dashboard
June 1, 2026 – Present
Designed KPI taxonomies and data categorization schemas, classifying business metrics by purpose and contextual significance - aligning with content-labeling principles (primary vs. secondary content distinction).
Personalized Music Streaming System
June 1, 2026 – Present
Labeled and structured real-time streaming event data for recommendation model training, applying consistent tagging rules across high-volume data streams.
Cultural Fit Analysis
The candidate's academic projects cover a diverse range of data science applications, from computer vision and NLP to business intelligence and recommendation systems, indicating a broad interest and adaptability. The internship experience aligns well with the target role, focusing on practical data science tasks. The emphasis on data quality and structured thinking suggests a methodical approach, which is a good cultural fit for data-driven organizations. However, the candidate is still early in their career (experienceLevel: 0), and while the projects are relevant, they are academic. More real-world, collaborative project experience would further strengthen cultural fit.
Soft Skills & Operational Fit
The candidate demonstrates structured analytical thinking and a commitment to data quality, which are crucial for operational fit in data-intensive roles. Their experience in documenting data processing workflows and maintaining detailed records suggests good organizational skills. The academic projects highlight an ability to work on diverse data science applications, indicating adaptability. However, the resume does not provide explicit examples of teamwork or leadership, which are important for senior roles.