Srinidhi Kandimalla

Data Scientist with experience delivering machine learning models, data pipelines, and analytics solutions across healthcare and consulting environments. Strong background in Python-based data processing, statistical modeling, feature engineering, and large-scale data systems. Experienced in building classification, regression, and clustering models, working with cloud data warehouses, and supporting production-ready analytics workflows. Adept at collaborating with data engineering, analytics, and operations teams to translate data into measurable business value.

Distributed AI-Powered Personalized Job Recommendation System, 01/2025 – 05/2025

Python, Hadoop (HDFS), Apache Spark, Spark Streaming, BERT, Word2Vec, TF-IDF, XGBoost, SQL

• Built a distributed ML system for job-candidate matching using NLP-based feature representations.
• Engineered feature sets and trained classification and ranking models at scale using Spark.
• Enabled real-time ingestion and processing with Spark Streaming.

CNN-Based Human Activity Recognition using Wearable Sensors,           09/2024 – 12/2024

Python, TensorFlow, CNN, HAR, MEMS sensors (Accelerometer, Gyroscope)

• Designed CNN-based HAR framework using accelerometer/gyroscope data.•
• Achieved robust performance in activity recognition for fitness, health, and monitoring applications.

Efficient Sentiment Analysis using Encoder-Only Transformer           08/2024 – 12/2024

Python, PyTorch, NLP, SQL

• Implemented an encoder-only transformer model for sentiment classification on social media datasets.
• Improved contextual understanding and model accuracy through advanced preprocessing and feature design.

Video Analysis for Weapon Detection and Alerting,           01/2024 – 04/2024

Python, OpenCV, TensorFlow, Keras, CNN, RCNN, YOLO

• Developed deep learning model to detect weapons (knives, pistols) in video streams.
• Contributed to public safety with proactive threat detection in sensitive areas.

Detection of Stress in IT Employees using Machine Learning Techniques   09/2023 – 12/2023

Python, Scikit-learn, SVM, Random Forest, Tableau

• Built supervised ML models for stress detection using behavioral and text-based data.
• Visualized insights using Tableau for interpretability.

• Video Analysis for Weapon Detection and Alerting, International Journal of Innovative Research in Multidisciplinary Fields (IJIRMF), Vol. 14, Issue 05, May 2024.
• CNN-Based Human Activity Recognition Using Wearable Sensors, International Journal of Innovative Research in Multidisciplinary Fields (IJIRMF), Vol. 14, Issue 11, Nov 2024.