Rajit Rajpal CA, 510-***-****, adkboh@r.postjobfree.com

https://www.linkedin.com/in/rajit-rajpal https://github.com/rajit906 EDUCATION

University of California, Berkeley, CA

May 2022

BA, Data Science and Statistics GPA: 3.8/4.0

Relevant Coursework: Data Structures and Algorithms, Linear Modelling, Statistical Learning, Data Science I & II, Applied Linear Algebra, Mathematical Economics, Probability, Statistics, Convex Optimization, Stochastic Processes, Numerical Analysis, Real Analysis, Business Management, Microeconomics, Macroeconomics SKILLS

Languages: Python, Java, R, SQL

Frameworks: PyTorch, TensorFlow, Sci-kit Learn, Pandas, Tableau, OpenCV, NLTK, Django, Git Fields: Machine Learning, Data Analysis, Statistical Modeling, Data Visualization, Inference, NLP, Computer Vision EXPERIENCE

UC Berkeley

Teaching Assistant: Linear Algebra for Data Science Jan – Now

2021,CA

• Developing the curriculum for a course centred around applying linear algebra in data science. Created problem sets that include written work and programming in Python.

• Teaching discussion sections and holding office hours for a class of 50 students and collaborating with the course staff to optimize learning in the online learning regime. Helen Wills Institute for Neuroscience

Undergraduate Research Apprentice

• Designed and implemented a novel computational algorithm using representation learning to predict time-varying signals by working with 2 PhDs and Prof. Bruno Olshausen. Trained model with PyTorch on massive natural image datasets. BoffinAI

Founding Engineer: Data Science, Software

Sep - Dec

2020,CA

• Created beta product by heading a team of 6 based on AGILE. Spearheaded the business and marketing strategy to obtain pre-seed investments. The product was a recommender system for online SMEs.

• Produced a scalable recommender system after training deep learning model on massive datasets and processed with NLP and Computer Vision on TensorFlow.

• Developed an automated pipeline with Airflow and integrated with Shopify web app. BoxOfDocs (Now Ontopical)

Machine Learning Engineer Intern

Apr-June

2020, CA

● Conducted exploratory data analysis and NLP on 45000 documents in a team of 3 to improve the search engine from 87% to 97% accuracy through a Deep Learning pipeline for document classification.

● Implemented predictive statistics (demographics) into the search engine for municipalities using text mining along with PCA and regularized regression.

PROJECTS

● Forecasting Apple Stock Prices: Conducted a stochastic calculus research project to predict stock prices with Monte-Carlo simulations and implemented an ML model to test findings and achieved a 90% accuracy.

● House Prices: Implemented regression and clustering with Sci-Kit Learn and Pandas to predict prices of real- estate with 90% accuracy based on location and numerous other factors.

● Spam Email Classifier: Used logistic regression, feature engineering and NLP to create classification software to discern between ham and spam e-mails.

● Predicting COVID spread: Developed a linear model that achieved 95% accuracy in predicting COVID-19 spread using ridge regression from JHU dataset.

● Food Safety: Employed Pandas, SQL and several other tools to conduct exploratory data analysis on Berkeley restaurants’ food safety as part of a research project.

● FakeNetAI: Collaborated with a team of 5 to build Saas startup to detect deepfakes by researching and implementing learning methods on PyTorch with a 95% accuracy. Built API on Django RF.

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