Data Scientist & Machine Learning Engineer

Sushmitha Guraja

+1-720-***-**** *****************@*****.***

www.linkedin.com/in/sushmitha-guraja-185735209

https://github.com/sushmithaguraja

SUMMARY

Computer Science graduate with an M.S. from Purdue University and experience in data science, machine learning, data analytics, data engineering, and business intelligence through internships and end-to-end technical projects. Skilled in Python, SQL, exploratory data analysis, statistical analysis, feature engineering, machine learning, data transformation, ETL pipelines, data visualization, and model evaluation. Experienced in developing analytical datasets from structured and heterogeneous data, identifying patterns and trends, training and validating predictive models, and communicating insights through interactive dashboards and visualizations. Hands-on experience with AWS S3, Snowflake, Power BI, Tableau, Pandas, NumPy, Scikit-learn, TensorFlow, and relational databases.

EDUCATION

Master of Science in Computer Science

Purdue University GPA: 3.7/4.0

Bachelor of Science in Computer Science

Savitribai Phule Pune University GPA: 9.1/10.0

SKILLS

Programming: Python, SQL, Java, JavaScript, C, C++, Linux

Data Science & Analytics: Exploratory Data Analysis (EDA), Statistical Analysis, Predictive Analytics, Feature Engineering, Feature Selection, Data Interpretation, Correlation Analysis, Data Visualization, Pattern Analysis

Machine Learning: Classification, Regression, Time Series Forecasting, Model Training, Model Evaluation, Hyperparameter Tuning, ROC-AUC, Cross-Validation Concepts, Predictive Modeling

Data Engineering: ETL Pipelines, Data Ingestion, Data Cleaning, Data Transformation, Data Validation, Data Processing, Data Aggregation

Business Intelligence: Power BI, Tableau, Dashboard Development, KPI Reporting, Reporting Automation, Business Analytics

Cloud & Data Platforms: AWS S3, Snowflake

Databases: SQL, Relational Databases, Data Warehousing

Libraries & Tools: Pandas, NumPy, Scikit-learn, TensorFlow, Keras, Matplotlib, Git, Jupyter Notebook, Excel, PowerPoint

PROJECTS

Cloud-Based Movie Data Analytics Pipeline with Interactive Dashboard

Built an end-to-end data analytics pipeline using AWS S3, Snowflake, SQL, and Power BI

Designed ETL workflows for data ingestion, data cleaning, transformation, and aggregation

Transformed raw data into structured analytical datasets for downstream reporting and business intelligence

Developed SQL-based analytical workflows for querying and analyzing transformed datasets

Created interactive Power BI dashboards for KPI tracking, trend analysis, and data-driven decision support

Leveraged cloud storage and data warehousing technologies to support scalable data processing and analytics

Customer Churn Prediction

Built an end-to-end machine learning pipeline using Python and SQL for structured customer data

Performed data preprocessing, validation, exploratory data analysis, and feature engineering

Trained classification models including Logistic Regression and Random Forest

Evaluated model performance using ROC-AUC and classification metrics

Applied hyperparameter tuning and experimentation to improve predictive performance

Analyzed model results to assess predictive effectiveness and identify relevant data patterns

Job Market Data Analysis & Dashboard

Designed ETL pipelines to process and transform large job market datasets

Performed exploratory data analysis and statistical analysis to identify patterns and trends

Analyzed relationships among job skills, salaries, and hiring patterns

Developed interactive dashboards using Power BI and Tableau for data exploration and reporting

Used data visualization techniques to communicate findings and support data-driven decision-making

Produced structured analytical reports summarizing insights from heterogeneous job-market data

LLM Evaluation Platform — AI Model Benchmarking & Evaluation System

Technologies: Python, OpenAI API, Ollama, Streamlit, Pandas

Engineered a dataset-driven evaluation system for benchmarking multiple large language models

Developed an LLM-as-a-Judge evaluation mechanism to assess semantic correctness of model outputs

Implemented cross-model benchmarking using accuracy, latency, and response consistency metrics

Built an interactive Streamlit dashboard for analyzing model performance and comparing experiments

Designed modular components for model inference, evaluation logic, and analytics

Implemented structured logging and CSV-based experiment tracking to support reproducible model evaluation

Stock Price Prediction using LSTM Networks

Developed a time-series forecasting model using LSTM neural networks with TensorFlow and Kera

Preprocessed financial time-series data using normalization and sliding-window techniques

Implemented train-test data splitting and sequence preparation for time-series modeling

Trained and evaluated forecasting models using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE)

Visualized predicted and actual trends using Matplotlib

Developed data-processing workflows using KNIME for financial data analysis

Multimodal Image Captioning System with Web Data Pipeline

Built an end-to-end data collection and preprocessing pipeline using Python, Requests, and BeautifulSoup

Developed data validation and retry mechanisms to improve robustness of web-based data collection

Designed a deep learning image-captioning system using Vision Transformer (ViT) and transformer-based decoder architectures

Developed tokenization and dataset-generation workflows using TensorFlow

Trained and evaluated models using sequence prediction techniques and BLEU score metrics

Integrated computer vision and natural language processing techniques to generate contextual image descriptions

EXPERIENCE

Assistant STEM Advisor August 2025 to May 2026

Purdue University

Assisted students in identifying and connecting with the appropriate academic advisor based on individual questions and needs.

Coordinated and scheduled advising appointments while maintaining accurate appointment information and records.

Communicated clearly and accurately with students while following established advising procedures and processes.

Access Assistant August 2024 to May 2025

Purdue University

Supported students unable to attend lectures by providing accurate lecture notes and course materials in a timely manner.

Organized and delivered academic information according to individual student requests and established procedures.

Managed requests independently and consistently while following established processes and service expectations.

Web Development Intern December 2021 to January 2023

MVP’s KBTCOE

Developed SQL-based data retrieval and reporting workflows for application functionality

Built backend logic supporting data transformation and storage

Improved query performance and data accessibility

Collaborated with development teams on software functionality and testing

Contributed to web application development and technical implementation workflows

Cybersecurity Intern August 2021 to November 2021

Konsola Infotech

Performed systematic vulnerability analysis and testing of computer systems to identify potential issues.

Analyzed technical information and documented findings for review and follow-up.

Troubleshot system issues and supported diagnostic activities using analytical tools.

Maintained organized documentation of findings and recommended improvements based on analysis.

Followed structured testing procedures while reviewing system information for accuracy and potential risks.

Cybersecurity Intern June 2021 to July 2021

Smartknower

Performed network vulnerability analysis and security assessments using structured testing procedures.

Reviewed system and network information to identify potential vulnerabilities and inconsistencies.

Applied security protocols and encryption concepts while working with technical systems.

Documented findings and supported activities focused on identifying and mitigating system risks.

AI & Machine Learning Intern May 2021 to June 2021

Cognifront

Developed machine learning models and analytical workflows using Python

Performed data preprocessing, feature engineering, and model evaluation

Worked with structured datasets to generate predictive insights

Conducted experimentation and debugging to improve model performance

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