About Me

  • Full Name:Dhwanit Gajjar
  • Email:dhwanitgajjar@gmail.com
  • Address:Edison, New Jersey

Hello There!

As a soon-to-be graduate with a Bachelor's in Computer Science from Rutgers University, I have cultivated a robust foundation in data science and machine learning. My experiences range from developing predictive models that enhanced claims processing efficiency by 32% during my tenure as a Machine Learning Engineer at MetLife, to deploying deep learning models achieving a 92% accuracy rate in patient outcome predictions at Syneos Health. Proficient in Python, R, SQL, and cloud platforms like AWS, I have a proven track record of translating complex data into actionable insights. I am passionate about leveraging data-driven solutions to tackle real-world challenges and am eager to contribute my skills to innovative projects in the data science and machine learning domains.​

Currently, I am looking for Full time positions beginning from May 2025

My Resume

  • Work Experience

  • Machine Learning Engineer

    MetLife (April 24' - Present)
    • Collaborated with a team of 7 to develop business solutions, reducing the debugging time by 72% for recurring tasks.
    • Developed and deployed 5+ TensorFlow-based predictive models on AWS SageMaker, enhancing claims processing efficiency by 32% and reducing manual review time by 40%.
    • Developed a data validation script using Python (Pandas, NumPy) and SQL, ensuring 99.5% data accuracy and reducing data inconsistencies by 30%.
    • Designed and deployed a claims prediction model using Gradient Boosting and LSTM networks, integrated with Apache Kafka for real-time data ingestion, resulting in 35% reduction in claims processing time and annual cost savings by 7-figure.
    • Implemented a data pipeline using Apache Kafka, Apache Spark, and Python, increasing data freshness by 75% and supporting timely business decisions.
    • Integrated Amazon Lex bots with AWS services like Lambda, DynamoDB, S3, and API Gateway, enabling dynamic and scalable backend operations.
    • Utilized Docker, Jenkins, and Kubernetes to automate ML model deployment, reducing deployment time by 30%.
    • Conducted A/B testing and hyperparameter tuning using Scikit-learn, resulting in a 25% improvement in model accuracy.
  • Junior Data Scientist, Machine Learning

    Syneos Health (Oct 21’ – Mar 23’)
    • Built and deployed 3+ Keras-based Deep Learning models for patient outcome prediction, achieving a 92% accuracy rate and informing enhanced treatment planning.
    • Developed data visualizations using Tableau and Power BI, presenting actionable insights to non-technical stakeholders and driving data-driven decision-making.
    • Automated data processing tasks using Python, Bash, and SQL, reducing manual effort by 60% and increasing team productivity.
    • Built a scalable NLP application prototype on AWS utilizing services like Lambda, DynamoDB, and AppSync.
    • Implemented a data pre-processing workflow for ML systems, resulting in a 20% improvement in data accuracy and consistency, and a 30% increase in predictive performance.
    • Designed a scalable data warehouse solution using AWS Redshift that reduced query response time by 60% and increased data retrieval efficiency by 40%.
    • Developed a data warehousing solution using PostgreSQL, SQLAlchemy ORM, and Apache Spark, supporting 500+ concurrent users and reducing query latency by 80%.
    • Applied statistical process control (SPC) to monitor clinical trial data, detecting outliers within 2 hours of occurrence and ensuring data quality.

  • Education

  • Bachelors Degree

    Computer Science

    Rutgers State University, New Brunswick (2021 - 2025)
  • Associates Degree

    Computer Information Systems

    Middlesex College (2021 - 2023)
  • Certificates

  • Solutions Architect - Assoiciate

    Amazon Web Services
  • Cloud Practioner

    Amazon Web Services


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Intrested in Full Time opportunities

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