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Parth Bhatnagar

I am a dedicated and results-driven data science professional with a passion for unraveling complex problems through the power of data. I thrive on creating innovative solutions that drive business growth and efficiency.

🔍 Data Science Expertise: As a data aficionado, I possess a proven track record of transforming raw data into valuable insights.

⚙️ Machine Learning Enthusiast: I am deeply fascinated by the potential of machine learning to revolutionize industries. Leveraging my expertise in developing predictive models and algorithms, I am committed to implementing cutting-edge ML techniques that optimize processes and enhance performance.

🤝 Collaborative Team Player: I believe in the power of collaboration and have a strong aptitude for working in dynamic, cross-functional teams. My adaptable nature allows me to effectively communicate complex technical concepts to stakeholders at all levels.

📈 Driving Business Impact: My drive to make a meaningful difference is evident in my approach to projects. I continuously seek innovative ways to apply data science principles to real-world challenges, ensuring tangible and measurable results that positively impact the bottom line.

🌟 Lifelong Learner: In the rapidly evolving world of data science and machine learning, I am committed to staying at the forefront of advancements. Constantly upskilling and learning new technologies, I embrace every opportunity to grow both personally and professionally.

  • Role

    Data Science Researcher & Deep learning engineer

  • Years of Experience

    2 years

Skillsets

  • PyTorch
  • Keras
  • Kubernetes
  • LLMs
  • Machine Learning
  • MERN Stack
  • multimodal AI
  • Power BI
  • PyCharm
  • Python
  • JavaScript
  • R
  • rag
  • SQL
  • Swift
  • Tableau
  • TensorFlow
  • VS Code
  • X-code
  • Deep Learning
  • AWS
  • Big Data
  • Bootstrap
  • C
  • C++
  • Computer Vision
  • CSS
  • Dart
  • Apache-spark
  • federated learning
  • Flutter
  • gans
  • Git
  • Hadoop
  • HTML
  • Ibm watsonx
  • Java

Professional Summary

2Years
  • Nov, 2025 - Jan, 2026 2 months

    Data Science Researcher (Internship)

    Intel Corporation
  • May, 2025 - Jul, 2025 2 months

    AI Engineer (Internship)

    Infosys
  • Sep, 2023 - Apr, 2024 7 months

    Deep Learning Engineer (Internship)

    IBM
  • May, 2023 - Jul, 2023 2 months

    Machine Learning Developer (Internship)

    Bosch

Work History

2Years

Data Science Researcher (Internship)

Intel Corporation
Nov, 2025 - Jan, 2026 2 months
    Built an intelligent document management pipeline using LayoutLMv3, OCR (Tesseract), NER, summarization, knowledge graphs (Neo4j) to parse complex documents (tables, formulas, code, images), achieving 92% layout detection and 89% classification accuracy. Created a multimodal search and conversational system using ASR (Whisper), semantic search (FAISS), LLM-based QA, and timeline analysis over text and video content, achieving 90% entity extraction accuracy and 87% retrieval relevance.

AI Engineer (Internship)

Infosys
May, 2025 - Jul, 2025 2 months
    Developed an AI-Powered personalized pharmaceutical recommendation engine which predicts the disease and suggests the precaution, medications, workouts and diets. Conducted Comparative analysis between different Machine Learning algorithms to decide the best performing model. Random Forest Classifier with 95% accuracy comes out at first following by 92% with Gradient Boosting Classifier.

Deep Learning Engineer (Internship)

IBM
Sep, 2023 - Apr, 2024 7 months
    Developed a personality prediction model using handwriting recognition, achieving 95% accuracy in English and 80% in Hindi by leveraging CNNs and VGG-16/ResNet-50 architectures. Built and integrated a Speech Emotion Recognition (SER) model with Amazon Alexa using a Graph Neural Network (GNN) architecture, achieving 88% accuracy on a dataset of over 10,000 voice samples for real-time emotion detection and interaction.

Machine Learning Developer (Internship)

Bosch
May, 2023 - Jul, 2023 2 months
    Conducted market research on potential concerns for electric vehicles, analyzing trends and gathering insights from 500+ data points to identify key challenges and opportunities. Developed a mobile application for Electric Vehicle Range Optimization, integrating predictive analytics using regression algorithms and analyzing 35+ parameters to enhance range estimation accuracy by 20%.

Major Projects

4Projects

Patient Mortality Prediction using Federated Learning

Dec, 2024 - Feb, 2025 2 months
    Includes de-identified health-related data from over 60,000 patients who stayed in critical care units. The proposed model achieves 87% on the federated model as compared to 89% on the centralized one.

Image Super Resolution

Jul, 2024 - Sep, 2024 2 months
    The model enhances image resolution using a GAN-based backend. The Generated PSNR values range between 30-40, a total of 100,000 images were taken.

AayurGenie: An AI integrated chatbot using RAG and Gemini

Feb, 2024 - Mar, 2024 1 month
    Chatbot built using RAG and Gemini, which suggests remedies for various diseases based on Ayurveda. The proposed model achieves 87% accuracy, outperforming traditional foundational models like ChatGPT, which achieve 82%.

Visual Question & Answering using Multimodal AI

Nov, 2023 - Dec, 2023 1 month
    The model is trained on 75,000 labeled images, achieving an accuracy of 84%. Incorporated with Open-Ai API calling for identifying the images and generating the answers for which the user has asked for.