Machine Learning,Data Analytics

SWETHA GANESH

Boston, MA, USA LinkedIn ******.**@************.*** +1-857-***-**** GitHub

EDUCATION

Northeastern University, Boston, MA, USA Expected May 2026 Master of Science in Data Analytics Engineering GPA:4.0/4.0 Concentration- Data Science and Machine Learning

Coursework: Database Management for Analytics, Fundamentals of Data Analytics, Statistical Learning (ML), Data Mining Velammal Engineering College, Chennai, TN, India Aug 2019-May 2023 Bachelor of Engineering in Electronics and Communication Engineering GPA:3.94/4.00 Coursework: Machine Learning, Introduction to Python, DSA, Probability and Statistics, Marketing Analytics, Data Mining Extra-curricular: Technical Head of Coders Club, Member of Entrepreneurship Club, Volunteer of Youth Red Cross(YRC) TECHNICAL SKILLS

• Programming & Scripting: Python (Pandas, NumPy, Scikit-Learn, PyTorch, OpenCV, TensorFlow, Keras, Hugging Face), SQL, ABAP, Java, JavaScript

• Machine Learning & AI: Supervised & Unsupervised Learning, Neural Networks, Transformers, LSTM, GRU, Computer Vision, NLP, Large Language Models (LLMs), XGBoost

• Data Visualization: Tableau, Power BI, Matplotlib, Seaborn, Plotly

• Databases & ETL: SQL, MongoDB, Neo4j, SparkSQL, Snowflake

• Big Data & Cloud: Apache Spark, AWS (S3, Lambda, SageMaker, EMR, Glue, CloudWatch), Google Cloud, Databricks

• Automation & MLOps: Docker, Kubernetes, MLflow, Apache Airflow, LangChain

• Web Development: HTML, CSS, React, Angular, Flask, FastAPI PROFESSIONAL EXPERIENCE

Part-time Data and Analytics Engineer, Daeso, Northeastern University, Boston, MA Sept 2024 – Jan 2025

• Developed predictive models for content performance using Python and machine learning techniques, improving forecast accuracy by 20%.

• Built and optimized end-to-end machine learning pipelines leveraging Apache Airflow, Spark, and Kafka.

• Designed scalable data engineering workflows to process large datasets, increasing data retrieval speed by 30%.

• Created real-time analytics dashboards in Tableau, enabling data-driven decision-making for business teams. Data Analyst, HP (Hewlett Packard), Chennai, India Sept 2023-Aug 2024

• Analyzed large datasets using Python, SQL, and Tableau to improve vendor performance tracking by 12%.

• Developed interactive dashboards in Power BI that increased operational efficiency by 10%.

• Implemented machine learning models for trend forecasting, enhancing demand planning. SAP Business Analyst, Kaar Technologies, Chennai, India Dec 2022-Aug 2023

• Developed ETL pipelines using SQL, Python, and SAP S/4HANA, streamlining procurement processes and enhancing efficiency by 20%.

• Designed API integrations utilizing Kafka and SAP PI/PO for seamless real-time data exchange between ERP systems.

• Developed automated data pipelines using SQL, Python, and SAP tools to optimize enterprise reporting.

• Built AI-based predictive analytics models for supply chain and finance teams. PROJECTS

Mental Health and Well-Being Platform for Employees Sept 2024-Dec 2024

• Developed machine learning models to predict content engagement and user retention, optimizing marketing spend.

• Built end-to-end ML pipelines with Python and BigQuery, improving model inference speed by 25%.

• Integrated automated backtesting methods, ensuring high model reliability.

• Created Tableau dashboards to track key performance indicators for media content. Exploratory Data Analysis for Vendor Performance Analysis Apr 2024-Jun 2024

• Developed machine learning-based models using XGBoost and regression techniques to assess vendor performance and improve decision- making.

• Conducted in-depth trend analysis on key vendor metrics, enhancing forecasting accuracy and compliance tracking.

• Designed interactive Power BI dashboards that provided real-time insights, streamlining procurement and operational processes.

• Leveraged data-driven strategies to optimize vendor relationships, leading to improved supply chain efficiency. Computer Vision-Based Automation for Process Optimization Feb 2024-Mar 2024

• Developed an object detection system using OpenCV and PyTorch for real-time process monitoring.

• Integrated ZeroMQ and OPC-UA protocol for seamless industrial automation communication.

• Built self-optimizing AI models to enhance automation efficiency. Toxicity Detection in Multiplayer Gaming (NLP & AI) Dec 2023-Mar 2024

• Designed an AI-powered NLP pipeline leveraging transformer-based LLMs (BERT, GPT) for real-time toxicity detection in gaming chats.

• Engineered a hybrid classification model combining XGBoost with deep learning embeddings to improve detection accuracy.

• Deployed a real-time moderation system using Google Cloud, AWS Lambda, and FastAPI, ensuring scalable and low-latency processing.

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