Tejaswani Dash

Experienced Data Engineer over 5+ years with experience in Machine Learning, healthcare data, automotive data, customer data, media data, reporting, SAS, SAP, PyTorch, Tensorflow, Spark, Scala, NLP, LLM, various data/reporting tools and programming using SQL, Python, R, Shell scripting. Solid understanding of statistical analysis, machine learning algorithms, and predictive modeling. Proven track record of delivering innovative data solutions with effective project management. Hands-on experience in delivering work before deadlines with 98% accuracy. Seeking a challenging role in a dynamic environment to apply my skills and knowledge effectively. Dynamic ETL Developer practiced in helping companies with diverse transitioning, including sensitive data and massive big data installations. Promotes extensive simulation and testing to provide smooth ETL execution. Known for providing quick, effective tools to automate and optimize database management tasks.

• Excellence Performance Award for Business Client management in Apcer Life Science, 01/2020
• Excellent performance in Data Engineering and recognition in cloud data Migration, 01/2020
• Best Debut Performance in Pharmacovigilance Data, 12/2018

• AWS Certified Data Engineer, https://www.credly.com/badges/885e9f66-7c47-4fea-bb5b-3c6489f30fdc/public_url, 03/2024
• AWS Certified Machine Learning – Specialty, https://www.credly.com/badges/15e9f69c-8ec3-4247-ab25-89f50b88af64/public_url, 04/2024

Capstone Project- Effect of Social Determinants incidents on Diabetes, 2022, https://github.com/TejaswaniDash/Effect-of-Social-Determinants-incidents-on-Diabetes- 

• Used LASSO regression to construct a causal network for explaining variation in social determinants incidence of diabetes, achieving an accuracy of 76%.

AI Project on Fingerphoto, 2022, https://github.com/TejaswaniDash/AIProject-Color-space-finger-photo-presentation-detection-on-background-variation-using-ResNet 

• Addressed presentation attack issues using Convolutional Neural Networks (CNNs) for Fingerphoto-based authentication, a touchless authentication method in medical patient scenarios, incorporating GAN models.

Credit Card Fraud Prediction using ML and AI, 2022, https://github.com/TejaswaniDash/Credit-Card-Detection 

• Achieved 96% Accuracy and Minimized False Negatives Using Python, Logistic Regression, and Ensemble Models.
• Employed exploratory data analysis (EDA), implemented advanced classification algorithms, and fine-tuned models via hyperparameter optimization to effectively predict fraudulent credit card transactions.
• The meticulous approach yielded exceptional results, demonstrating reliable detection and combatting fraudulent activities with a high accuracy rate of 96%.

Machine Learning Model to predict Covid infection and death rates, 2022, https://github.com/TejaswaniDash/Covid-19-Death-Recovery-and-confirmed-Prediction-and-Analysis-using-ML-and-AI 

• Utilized European and country-level USA datasets to predict future US infection and death rates with 90% accuracy. Compared multiple ML models (regression/forest, etc.), LLM and neural network performance.

Text Analysis of data to predict sentiment using NLP, LLM, 2023, https://github.com/TejaswaniDash/Text-Analysis-of-data-to-predict-sentiment 

• Employed LASSO regression, multi-level regression, LLM and ML tools to create a network for sentiment prediction. Achieved a 95% success rate in predicting sentence sentiment.

Mental Health in Tech world using Deep Learning and ML, 2021, https://github.com/TejaswaniDash/Mental-Health-In-Tech-World

• Analyzed and predicted the need for medical treatment for mental health issues in the tech industry using support vector networks, decision trees, and random forests.