Archit Garg
Data AnalystAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Lead Data Scientist at Prudential, Computational Finance from Carnegie Mellon University. Seeking Opportunities in Data Science.
I am a Machine Learning and Finance enthusiast with a strong academic foundation and practical experience at the intersection of data science and financial analytics. With a Bachelor's degree in Computer Science and Mathematics and a Master's in Computational Finance, I bring a unique blend of technical expertise and domain knowledge to solving complex challenges. Over the past five years as a Data Scientist, I have developed expertise in designing and implementing advanced machine learning models to drive efficiencies, optimize decision-making, and create scalable solutions. My skills span a wide range of AI and data science techniques, including: ★ Predictive Analytics using traditional classification and regression frameworks, along with advanced statistical techniques such as survival analysis ★ Natural Language Processing (NLP) for extracting insights from unstructured data ★ Generative AI and Large Language Models (LLMs) for enhancing customer experiences and improving operational workflows I am currently exploring opportunities where I can apply my expertise at the intersection of data science and business, leveraging both my technical skills and strategic mindset to deliver impactful outcomes.
Carnegie Mellon University
Master of Science - MS, Computational Finance
January 1, 2018 – January 1, 2019
Delhi University
Bachelor's Degree, IT & Mathematical Innovation
January 1, 2013 – January 1, 2017
Somerville School, New Delhi
High School
January 1, 1999 – January 1, 2013
Prudential Financial
Lead Data Scientist
November 1, 2023 – Present
Newark, New Jersey, United States
HighPeak
Data Science Consultant
June 1, 2023 – December 1, 2024
Newark, New Jersey, United States
Prudential Financial
Senior Data Scientist
July 1, 2021 – November 1, 2023
Newark, New Jersey, United States
Prudential Financial
Data Scientist
February 1, 2020 – June 1, 2021
Newark, New Jersey, United States
Prudential Financial
Data Science Summer Associate
June 1, 2019 – August 1, 2019
Newark, New Jersey, United States
Pitney Bowes
Data Scientist
July 1, 2017 – July 1, 2018
Pitney Bowes
Machine Learning Intern
January 1, 2017 – June 1, 2017
Defence Research and Development Organisation (DRDO)
Machine Learning Research Intern
June 1, 2015 – December 1, 2015
Delhi
Defence Research and Development Organisation (DRDO)
Machine Learning Research Intern
May 1, 2014 – May 1, 2015
Delhi
Scene Text Detection/Recognition
May 1, 2017 – Present
Created a software to detect and recognize text in random scene images. -- Used various Image Processing techniques such as edge detection, Blob Detection, and Thresh-holding, to preprocess the input scene images, and detect blobs of text. -- Trained an binary Support Vector classifier, along with HOG feature extraction, to predict presence or absence of text. -- Used a Convolutional Neural Network to recognize the text present in the detected blobs. Used Keras Deep Learning library in Python.
Diabetic Retinopathy Detection using LAMSTAR Neural Networks
March 1, 2016 – Present
Created a software to read retina images and detect the presence of symptoms for diabetic retinopathy, a disease very common in several parts of the world, with detection difficult by humans. The program works on a large dataset of about 25000 retina images, with efficient and quick testing, using LAMSTAR, a neural network specifically built to handle large input classifiers. Achieved significant accuracy on training set. Majority cases were correctly classified during testing. Better results as compared to other techniques like Convolutional Neural Networks, Deep Belief Networks and other Deep Neural Networks, as tested on the same data set.
Hybrid Classifier Using Neural Networks & Soft Set Theory
June 1, 2015 – Present
Combined various neural networks including Back Propagation, RBF and multilayered Restricted Boltzmann Machine, with various neutrosophic fuzzy set techniques. Used MNIST dataset for testing results. Obtained significant improvement over most results as compared to the traditional standalone neural network techniques. Research Paper under review for being published in Elsevier Journal - Materials Today: Proceedings (ISSN: 2214-7853)
Offline Signature Verification
May 1, 2015 – December 1, 2015
Worked on Classification and Machine Learning problems for application in the field of signature verification. Obtained acceptable results in the same. Worked on Kernel Principal Component Analysis and Linear Discriminant Analysis for classification purpose. The development was done on both C and Matlab, with implementation of several Algebraic methods, like QR Factorization, Cholesky Decomposition, etc, for use as per requirement.
Reinforcement Learning in NES Games - Mario
January 1, 2015 – Present
Created an automatic game solver for the NES game Mario, using Reinforcement Learning technique, which learns a set of motifs along with their favorability, based on the reward they yield. Integrated the code with the game using FCEUX emulator. Created a generic game-independent reward function, by learning various lexicographic orderings of the 2 KB RAM available with NES games, with varying weights. Incorporated backtracking, future-sight, and deeper search depth, to optimize the efficiency of the trained solution.
Classification Using KPCA-LDA Algorithm
June 1, 2014 – Present
Undertook an internship at the end of the first year of B.Tech under the guidance of Dr. A.K. Bhateja, Scientist G,on the project “Signature Verification”. In this project, the Kernel Principal Component Analysis Algorithm was implemented along with the Linear Discriminant Analysis Algorithm, in both C and MATLAB, to classify different datasets provided by the mentor, including Face Recognition, Digit and Character Classification, Cryptosystem Classes and Noisy Channel Detection. Image alignment techniques were used for better classification of signatures. Various Linear Algebra methods were also implemented in both C and MATLAB, for parts of the algorithm like finding Eigen Values. Significant achievements include zero False-Acceptance Rate in signature dataset, with overall accuracy of about 93%;results with upto 99.71% accuracy in handwritten digit classification;confidence calculation of the classification, with more than 90% testing vectors incorrectly classified belonging to the category of ‘less than 50% confidence’;identification of noise in a text having 20% noise with 94% accuracy, while in text having 10% noise with 80% accuracy.
15 Puzzle
April 1, 2014 – Present
Developed a game - 15 Puzzle - on Matlab, with Graphical User Interface. The game had components of AI for automatic solving, as well as an option for the user to solve the puzzle.
3D chess
February 1, 2014 – Present
Created a Human vs. Computer Chess Game, using Reinforcement Learning for training the Computer Player. The reward function used was developed based on past researches, with constant upgradation in accordance with the results. Different levels of the Computer Player, based on the search depth while learning, were incorporated. The hardest level computer player could successfully defeat 5 out of 10 opponents. Incorporated 3-D graphics using JMonkey Engine.
Bloomberg Market Concepts
Bloomberg
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio shows a strong inclination towards research and complex problem-solving, particularly in AI/ML. The experience at Prudential and HighPeak indicates a fit for data-driven, innovation-focused environments. The diverse range of personal projects, from neural networks to game AI, suggests intellectual curiosity and a proactive approach to learning and applying new technologies. The target role of 'Data Analyst' might be a slight mismatch given the candidate's senior 'Data Scientist' and 'Lead Data Scientist' experience, which typically involves more advanced modeling and strategic responsibilities than a pure analyst role. However, the analytical rigor demonstrated aligns well with the core requirements of a data-centric role.
Soft Skills & Operational Fit
The candidate's experience as a Lead Data Scientist and Data Science Consultant suggests strong leadership, strategic thinking, and client engagement skills. The project descriptions indicate a problem-solving mindset and the ability to work on complex, real-world challenges. The progression through roles at Prudential Financial from intern to Lead Data Scientist demonstrates dedication and growth within an organization.