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Boston,MA PHONE NUMBER AVAILABLE EMAIL AVAILABLE LINKEDIN LINK AVAILABLE github.com/kanagalasrilakshmi Work ExperienceMachine Learning Engineer, Dassault Systems, Providence, RI May 2023 - Dec 2023 Spearheaded development of RAG chatbot, reducing regulatory compliance penalties by $68K annually and strategically increased user adoption from 10% to 78% in 7-weeks. Optimized search efficiency by 40% by storing Mistral-7b embeddings in OpenSearch vector db and utilizing Llama-2 for response generation, achieving 0.68 ROUGE evaluation metric score. Built an ETL training pipeline to perform data mining of 30 GB unstructured healthcare data, stored in Snowflake using Spark. Pioneered GNN-based supervised machine learning model for stress location classification, achieving 96% accuracy. Built a CI/CD pipeline in AWS for scalable model development and storage, expediting product design analysis by 50%. Machine Learning Engineer, Qualcomm, Hyderabad, India Jan 2021 - Aug 2022 Deployed BERT model for feedback analysis, uncovering roadblocks and resolved problems, cutting service requests by 20%. Reduced request completion time by 167 hours through automation of data preprocessing and back testing using Jenkins CI/CD pipeline. Mitigated $50K potential losses by enhancing anomaly detection efficiency using SVM and XGBoost, A/B testing and statistical analysis. Collaborated with business stakeholders to deploy ML tracking tool, reducing response time by 50% by automating processing, anomaly detection and status tracking. Created agile KPI dashboards for advanced data analysis using AWS Quick Sight to visualize data anomalies; reducing detection and decision-making time by 12 hours.Data Scientist, Vitra.ai, Bengaluru, India Oct 2020 Dec 2020 Engineered NLP pipeline to summarize lengthy lectures with 85.63% accuracy, cutting down time spent by 50%[paper link]. Led a team of 4 in building article scraping tool using Selenium, BS4 and SQL that reduced article search time by 45%. Boosted performance and client engagement by 35% by creating interactive product interface using ReactJS with Django. Amplified annual customer retention by $30K and elevated user satisfaction by 70% through sentiment analysis and Tableau visualization. Data Scientist, Fulcrum GT, Hyderabad, India Dec 2019 - Sep 2020 Predicted client churn using Random Forest & Logistic Regression with 83% accuracy; enhanced customer retention understanding 10x. Launched interactive Power BI dashboard for visualizing financial trends and client retention metrics; reducing analysis time by 20 hours. Deployed dashboard using Docker, Amazon ECR, AWS EKS and Kubernetes that elevated performance, reliability, accessibility by 60%.SkillsProgramming Languages : Python, SQL, C, C++, Java, JavaScript, Matlab, HTML, CSS, R. Data Science Tools : PyTorch, Keras, TensorFlow, Scikit-Learn, Numpy, Pandas, OpenCV, Matplotlib. NLP Tools : Spacy, NLTK, TextBlob.Gen AI tools : HuggingFace, Langchain.Databases: PostgreSQL, MySQL, NoSQL, Oracle, SQLite. Cloud: AWS - QuickSight, EC2, Sage Maker, DynamoDB, Lambda, OpenSearch, Azure Data bricks, Snowflake, Spark. Software Tools: Github, Gitlab, Jenkins, Tableau, PowerBI, Pinecone, Milvus, Faiss, Docker, Kubernates, NodeJS, Jira. ProjectsPrediction of Customer Lifetime Value (CLV) XGBoost, Random Forests, Feature importance analysis Improved CLV prediction accuracy by 14.7% over baseline by training and fine-tuning of diverse regression models: Linear Regression, Decision Tree, Random Forest, and XGBoost. Extracted key predictors of CLV through feature importance analysis, to drive marketing strategies and bolster customer retention. Creation and Mining of a Medical Database MySQL, SQL, AWS RDS, Databases, Normalization Designed a logical data model, normalized relational schema and loaded 350,000+ records into MySQL database on AWS utilizing R. Enhanced query retrieval time by 26% leveraging star and snowflake schemas to build summary and fact tables. Time series forecasting with DeepAR and Temporal Fusion Transformer LSTM, DeepAR, Prophet, TFT. Forecasted energy consumption leveraging DeepAR, Prophet and TFT models, adjusting parameters to maximize forecasting accuracy. Outperformed traditional ARIMA and LSTM models by over 12.7% by implementing multi-step ahead forecasting, delivering actionable insights for efficient resource management.EducationNortheastern University, Boston MA Sep 2022 - Dec 2024 Master of Science in Artificial Intelligence GPA: 4.0/4.0 Coursework: Machine Learning, HCI, Large Language Modeling, Algorithms, Pattern Recognition, MLOPs, Computer Vision Vellore Institute of Technology, Chennai, India Jul 2016 - Jun 2020 Bachelors in Electronics and Computer Engineering GPA: 9.04/10.0 Coursework: NLP, Database Management Systems, Data Analytics and Visualization, Computer Vision, Statistics |
