SAKETH RAM JALADI

Buffalo

Overview

year of professional experience

Certification

Work History

Data Scientist

Indo-Euro Synchronization Pvt Ltd

Amaravthi

01.2023 - 12.2023

Performed exploratory analysis on large datasets using SQL and Python libraries such as Pandas, NumPy, Scikit-Learn, and Matplotlib.
Implemented supervised and unsupervised learning algorithms such as random forest, KNN, SVM, logistic regression in Python and R environment for predictive analytics projects.
Designed models, algorithms and visualizations to distill insights from huge volumes of data.
Integrated trained models into web applications using Flask framework or other web frameworks.

Education

Master of Science - Computer Science And Engineering

State University of New York At Buffalo

Buffalo, NY

05-2025

Bachelor of Science - Computational Science And Enginnering

Vellore Institute of Technology

05-2023

Skills

Programming & Markup Languages: Python, R, C, Scala, SAS, SPSS
Machine Learning Algorithms: Predictive Models, Logistic Regression, Deep Learning (Neural Networks), Anomaly Detection, Regression models, Ensemble models, Unsupervised Learning, Natural Language Processing (NLP), XGBoost
Data Analysis & Visualization Tools: Tableau, PowerBI, MS-Excel, Looker, Matplotlib, Seaborn, ggplot2

Frameworks & Platforms: Hadoop (Map Reduce), Apache Spark, Kafka, PyTorch, TensorFlow
Databases & Cloud Servers: MySQL, PostgreSQL, MongoDB, Snowflake, AWS (S3, EC2, Lambda, Glue etc)
Other: Git, MS Office, Data Warehousing, Statistical Data Mining, A/B testing, Hypothesis Testing, ETL jobs

Certification

Data Scientist and Data Analyst by Data Camp| Programming, Data Structures, and Algorithms Using Python by NPTEL | Machine Learning with Python by IBM.

Projects

Real-Time Hand Gesture Recognition

Developed a real-time hand gesture recognition system using CNNs trained on ASL, numeric, and motion-based datasets as part of my master's capstone project in Computer Vision.
Achieved up to 100% test accuracy using dataset-specific models and extensive data augmentation techniques.
Integrated live gesture prediction with OpenCV and MediaPipe, enabling real-time model switching and modular processing for both static and dynamic inputs.

Financial Risk Assessment using Machine Learning

Built an end-to-end ML application to predict loan approval status and financial risk scores using a synthetic dataset of 20,000 records.
Trained multiple classification (Naive Bayes, Random Forest, XGBoost, SVM, etc.) and regression models (Lasso, Neural Network) using PySpark for scalability.
Deployed a real-time Streamlit web app delivering instant loan decisions and risk insights to support data-driven lending.

Fitness Tracking System (PostgreSQL, Python, Microsoft Excel)

Implemented a precise Fitness Tracking system with Postgres to log user workouts, nutrition, diet plans and progress utilizing relational database to efficiently store data of 5000+ users, 1200+ exercises in Apache Hive.
Wrote over 150 advanced SQL queries for real time data read and writing features like calorie intake and daily progress tracking, further indexed 100,000+ data entries per day for improved database efficiency.
Performed exploratory analysis using segmentation and clustering to identify patterns in nutritional habits, workout frequencies, exercise types, thereby giving users a tailored plan with 35% reduction in fitness planning time.

Timeline