Data Scientist

Summary

Research Assistant
Topic: Container Runtime Comparison for the Optimal Container Engine
Comparative study to find segments that help in making the optimal decision for the right container engine by analyzing metrics like CPU, Memory, Disk IOs and Network IOs performance.
Technologies: Docker, Kata containers, Kubernetes, IBM Nabla, gVisor, FireCracker, Rkt.
Graduate Teaching Assistant
Course: Database Systems for Analytics
Helped students in resolving queries related to database management systems for data analytics.
Assisted in teaching, checking assignments, exams, and projects for 50 grad students.
Technologies: Redshift, Athena, RDS, Snowflake, S3, Oracle, SQL, NoSQL, Node.js, MongoDB
ACADEMIC PROJECTS
Driver Drowsiness Detection:
Skills: Python, Keras, TensorFlow, OpenCV, ANN, LSTM
Modeled a facial landmark drowsy detection system to reduce the number of road accidents and improve transportation.
Implemented techniques like dropout that reduced variance by 20%.
Proposed a method to extract significant features, to track and analyze both drivers eye regions and facial landmarks to measure the level of drowsiness with 75% accuracy (ANN).
Mushroom Classification
Skills: Random Forest, K Nearest Neighbors, Naïve Bayes, Decision tree, Tableau
Implemented machine learning algorithms to classify various species of mushrooms from UCI repository into edible or poisonous.
Achieved accuracy of 97.66% with decision trees.
Implemented techniques like and feature selection that reduced complexity and increased performance.
Ebay Delivery Prediction
Skills: Random Forest, Linear Regression, Catboost Regressor, XGBoost, Asana, Tableau
Engineered a model which predicts the delivery dates for items sold by retailer by using shipping information.
Random forest gave the best results for our model with 1.59 RMSE.
Instacart Analysis
Skills: Python, SQL, AWS (S3, Redshift, Glue, DynamoDB, Lambda, Quicksight), Latex
Performed Exploratory Data Analysis along with recommendations for frequently bought items.
Achieved 95% accuracy using Apriori Algorithm.
California Collision Analysis
Performed ETL on California Highway Patrol dataset to load data into AWS.
Build visualization focused on gaining meaningful insights and identifying accident-prone areas.

Expectations

I am glad to work as a data scientist where I can use my knowledge in Python and my problem-solving skills to good use. I would love to find trends and patterns which can help optimize business solutions. I have always loved to work on data where I have made sense of it to find out helpful market trends. I enjoy working in a fast-paced environment where I can grow with others.

Employment Preferences
  • New York, United States
  • Illinois, United States
  • Texas, United States
  • California, United States
Expected Base Salary

**,000 USD / year

Academic Degree
Experience

Total Professional Experience

no experience

Startup Experience

no experience

Big-Tech Companies

no experience

Enterprise Experience

no experience
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