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Data Science with less than a year in AI, MLOps & Data Engineering
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Assessing your cultural and operational fit
Highly motivated and results-driven Data Science student with 4 months of internship experience in AI, MLOps, and data engineering. Proficient in Python, SQL, and various data processing, machine learning, and cloud tools. Demonstrated ability to build scalable ETL pipelines, develop AI-powered recommendation systems and chatbots, and ensure data integrity. Eager to apply strong analytical and technical skills to complex data challenges.
National University of Computer and Emerging Sciences
Bachelor of Science · Data Science
August 1, 2022 – June 30, 2026
Dakota
Data Research Intern
October 1, 2025 – January 1, 2026
India
Automotive Artificial Intelligence (AAI-GmbH)
AI Intern
June 1, 2025 – July 1, 2025
India
Final Year Project - UpClout
June 24, 2026 – Present
• Built a scalable ETL data pipeline using Apify to scrape, Pandas to transform, and load 500+ Instagram profiles into PostgreSQL hosted on Neon and profile images stored on AWS S3, reducing manual data collection time by 95%. • Engineered an AI-powered recommendation engine using Sentence-BERT embeddings and ChromaDB with a custom weighted-scoring algorithm, achieving top-5 match accuracy for creator-brand pairing across 15+ niches. • Developed a RAG-powered AI chatbot using Groq LLM, LangChain, and MongoDB for persistent conversation history.
End-to-End MLOps Pipeline
June 24, 2026 – Present
• Engineered an end-to-end ML pipeline using Apache Airflow to orchestrate data ingestion, feature engineering, and model training across 11 DAG stages. • Integrated MLflow to track 8 experiments across hyperparameter sweeps, automatically registering models exceeding 85% accuracy thresholds. • Containerized the entire 3-service architecture using Docker Compose, reducing environment setup time from hours to under 5 minutes.
Near-Real-Time Data Warehouse
June 24, 2026 – Present
• Designed a hybrid-join algorithm combining hash and merge strategies for processing 1M+ Walmart sales transactions, guaranteeing 99.7% data retention during peak burst ingestion with zero record loss. • Automated ETL pipeline, enabling real-time data integration from CSV sources. • Developed 20 OLAP analytical queries to support business intelligence and analytics.
Music Recommendation Model
June 24, 2026 – Present
• Developed a real-time recommendation engine using Apache Kafka, Spark, and MongoDB on a 100GB dataset. • Deployed a Flask web application supporting real-time data streaming to concurrent users with <500ms latency.
ETL and Data Pipelines with Shell, Airflow and Kafka
IBM, Coursera
January 1, 2025 – Present
Cultural Fit Analysis
The candidate's project portfolio showcases a strong initiative and passion for data science and AI, with a mix of academic and personal projects. The diversity of projects, from social media analytics to MLOps pipelines and real-time recommendation systems, indicates a broad interest and adaptability. The internship experiences, particularly at AAI-GmbH, suggest an interest in applying AI in practical, industry-relevant scenarios. The candidate's proactive learning, evidenced by certifications in ETL and Data Pipelines, aligns with a growth-oriented culture. The target role of Data Science is well-aligned with the candidate's education, projects, and internship experiences.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through project descriptions, such as designing a custom weighted-scoring algorithm and a hybrid-join algorithm. The ability to reduce manual data collection time by 95% and environment setup time from hours to minutes indicates an efficiency-oriented mindset. Experience with containerization and orchestration tools suggests an understanding of operational best practices. However, without specific psychometric or behavioral test results, a deeper assessment of stress handling, team collaboration, and work attitude is not possible.