Data Science with 1+ years in Deep Learning, NLP, and Computer Vision
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Data Scientist with real-world experience in deep learning, NLP, and computer vision. Trained voice cloning models, built multi-sensor activity recognition pipelines, and co-developed an AI-powered education platform end-to-end. Fluent in Python and SQL, with a solid grasp of the full model lifecycle from data preprocessing to model deployment. Currently deepening expertise in large language models. Seeking a data-driven role where I can build impactful ML solutions from day one.
St. Xavier's College, Ahmedabad
M.Sc. · Big Data Analytics
July 1, 2023 – July 1, 2025
Lachoo Memorial College, Jodhpur
Bachelor of Computer Applications · Computer Applications
September 1, 2020 – June 1, 2023
Meril Life Science
Data Science Intern
December 1, 2025 – Present
India
PetPooja
Data Science Intern
November 1, 2024 – May 1, 2025
India
Real-Time Voice Cloning
January 1, 2025 – May 1, 2025
Architected a 6-stage end-to-end voice cloning data pipeline (collection, preprocessing, diarization, transcription, model training, deployment), achieving 0.83–0.91 cosine similarity across 8 speakers by fine-tuning XTTS on under 5 minutes of audio per speaker — shipped publicly on Hugging Face with sub-5-second CPU inference.
Human Activity Recognition (HAR)
December 1, 2024 – January 1, 2025
Assembled a multi-sensor ML data pipeline classifying 18+ simultaneous human activities at high F1 scores, by fusing body-worn, object-based, and ambient sensor streams and tuning multi-label decision thresholds on an imbalanced dataset.
Fraud Detection in a Government Scheme
September 1, 2024 – November 1, 2024
Constructed an anomaly detection system combining supervised ML with rule-based filters on a heavily imbalanced welfare dataset, optimising the precision-recall trade-off to flag fraudulent beneficiaries while keeping false positives operationally manageable.
Traffic Sign Recognition for Autonomous Vehicles
September 1, 2024 – November 1, 2024
Trained a CNN model classifying 40+ traffic sign categories and integrated model outputs into a simulated autonomous navigation module, achieving end-to-end decision latency under 100ms from image capture to action trigger.
Housing Market Price Prediction
September 1, 2024 – November 1, 2024
Optimised a housing price prediction model by building 15+ predictive features across location, size, age, and amenity dimensions and benchmarking 5 regression approaches — cutting cross-validated RMSE by ~18% over baseline.
Power BI Training
PetPooja
January 1, 2025 – Present
IBM SkillsBuild Internship Program
IBM
July 1, 2024 – Present
IBM SkillsBuild Micro-Internship: Data Science
IBM
May 1, 2024 – Present
IBM SkillsBuild Micro-Internship: Generative AI
IBM
March 1, 2024 – Present
Tata Group Job Simulation: Data Visualization
Forage / Tata
March 1, 2024 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from fraud detection and human activity recognition to voice cloning and educational platforms, indicates a broad interest in applying data science across various domains. Their involvement in internships and certifications from IBM and Tata Group suggests a commitment to continuous learning and professional development. The target role of Data Science aligns well with their demonstrated skills and project experience, particularly in applied machine learning and deep learning. However, the experience level (1) is low for a senior role, which might impact immediate cultural fit in a senior-level team without significant mentorship.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through optimizing database queries and ML models. Their experience in building an AI-powered education platform and deploying models publicly suggests a proactive and product-oriented mindset. The ability to work with diverse data streams and tune complex model parameters indicates attention to detail and analytical rigor. Collaboration is implied through project descriptions and internship roles, though not explicitly detailed.