Amalorpava Jeni A

About Amalorpava Jeni A

I am a Data and AI Engineer with a strong focus on building intelligent, data-driven solutions that transform raw data into meaningful insights and predictive capabilities.My core expertise lies in Machine Learning, Deep Learning, and Natural Language Processing (NLP), where I work on designing, training, and optimizing models to solve real-world problems. I have hands-on experience in data preprocessing, feature engineering, and model evaluation, with a strong emphasis on improving performance and accuracy.I am proficient in Python and SQL, and I extensively use tools such as Pandas, NumPy, and Scikit-learn for data analysis and model development. I am experienced in working with structured datasets, identifying key patterns, and applying appropriate algorithms for regression, classification, and analytical tasks.I have applied these skills in projects involving predictive modeling, where I focused not only on building models but also on understanding the underlying data, optimizing features, and deriving actionable insights.What sets me apart is my ability to combine analytical thinking with a problem-solving mindset, enabling me to approach complex data challenges with clarity and efficiency.My areas of interest include:• Machine Learning and Predictive Analytics• Natural Language Processing• Deep Learning• Data Analysis and Insight GenerationI am continuously enhancing my knowledge in AI and staying updated with emerging trends to build scalable and impactful data solutions.I am currently seeking opportunities as a Data Engineer / AI Engineer where I can contribute to data-driven decision-making and create intelligent systems that deliver real-world value. Open to opportunities | Let’s connect!

Experience

1. Data Scientist

   First Quadrant Labs

   Apr 2026 — Present

   Data Science / AI Intern – First Quadrant Labs• Contributed to AI-driven projects by applying Machine Learning techniques to real-world datasets • Designed and implemented data preprocessing pipelines, improving data quality and model performance • Built and evaluated predictive models using supervised learning algorithms • Performed in-depth exploratory data analysis to identify key patterns and influencing factors • Optimized model performance through feature engineering and hyperparameter tuning • Utilized Python-based tools such as Pandas, NumPy, and Scikit-learn for data analysis and modeling • Strengthened practical understanding of end-to-end ML workflow from data preparation to evaluation