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Vice President, Applied AI ML Lead at JPMorgan Chase & Co.
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I am a keen Machine Learning Developer looking to inculcate good coding skills, keep up with latest technologies and practice knowledge I have gained in my academic years. I have experience in fields like Machine Learning as well as Software development, with multiple projects in both fields to boast about. I like to work in a challenging environment and on unconventional ideas. To know more about me and my projects please visit: http://www.mihirmirajkar.com
North Carolina State University
Master's degree, Computer Science
January 1, 2016 – January 1, 2017
University of Mumbai
Bachelor's degree, Computer Science
January 1, 2012 – January 1, 2016
JPMorgan Chase & Co.
Vice President, Applied AI ML Lead
February 1, 2021 – Present
JPMorgan Chase & Co.
Machine Learning Expert
July 1, 2019 – February 1, 2021
JPMorgan Chase & Co.
Machine Learning Developer
February 1, 2018 – July 1, 2019
REDOX TECH LLC
Engineering Intern
May 1, 2017 – August 1, 2017
Cary, North Carolina
North Carolina State University
Graduate Student
August 1, 2016 – December 1, 2017
Raleigh-Durham, North Carolina Area
ARK Technosolutions
Intern
June 1, 2013 – July 1, 2013
Mumbai Area, India
Market Segmentation via Community Detection for Facebook Data
April 1, 2017 – Present
The main aim of this project was to implement community detection algorithm, find the relevant market segments, and evaluate the obtained segments via influence propagation for Facebook data provided by CalTech.
Solr Abstraction API with Spring boot and java
April 1, 2017 – Present
In most of the situation where Solr is used it is directly connected to the client side and has a very tightly bound architecture. Hence, if there is an update to the solr working and any of its functionality changes the client side has to be updated to accommodate these changes. This requires constantly updation of the client side and could create stability and security issues if not done correctly. Hence to avoid such issues, I have created an REST API, using spring boot, that’s an abstraction layer, a generic interface for querying for Solr which acts as a bridge/Abstraction layer between server and the client. This first iteration contains a simple functionality where when a query is passed to the layer it will return the response in a desired manner while keeping the client and server disconnected. This is especially helpful while creating a system like Elastic Search which might be subject to multiple breaking updates on the client side.
Bitcoin Price Prediction with Bayesian Regression
March 1, 2017 – Present
In this project, I was tasked with predicting the price variations of bitcoin, a virtual cryptographic currency. These predictions could be used as the foundation of a bitcoin trading strategy. The tools used in this projects were numpy, pandas, sklearn, and statsmodels for Python. This project aimed at implementing a prediction strategy from MIT’s paper ‘Bayesian Regression and Bitcoin’ by Devavrat Shah and Kang Zhang.
Matching Algorithms for Adwords
March 1, 2017 – Present
This project was inclined on duplicating how adwords or any advertising agency matched its bidders with the adspace in digital world. For this I analyzed three algorithm with its output in terms of revenue, the algorithms used were greedy, mssv and balanced algorithm out of which mssv gave the best revenue for advertising company.
Sentiment Analysis with Doc2Vec and NLP
March 1, 2017 – Present
In this project, I performed sentiment analysis over IMDB movie reviews and Twitter data. My goal was to classify tweets or movie reviews as either positive or negative. For classification, I experimented with logistic regression as well as a Naive Bayes classifier from python’s wellregarded machine learning package scikitlearn. As a point of reference, Stanfords Recursive Neural Network code produced an accuracy of 51.1% on the IMDB dataset and 59.4% on the Twitter data.
Basic Twitter Sentiment Analytics using Apache Spark Streaming APIs and Python
February 1, 2017 – Present
In this project data was streamed from twitter and basic sentiment analysis was done with python. to handle the streaming data Apache Kafka and zookeeper service was used. Live twitter data was fetched using twitter’s API. This formed the base for handling data stream and analyze the data.
Spark Recommender System
January 1, 2017 – Present
This was a project for Algorithms for Data Guided Business Intelligence in my Master’s. It was the foundation for learning spark and its functions. For this project, I created a recommender system that will recommend new musical artists to a user based on their listening history. Suggesting different songs or musical artists to a user is important to many music streaming services, such as Pandora and Spotify. In addition, this type of recommender system could also be used as a means of suggesting TV shows or movies to a user (e.g., Netflix).
Port knocking for compromised systems
August 1, 2016 – December 1, 2016
This basically focused on using a compromised system so that you do not have to breach its firewall every time you want to exploit the system. Hence, a backdoor was written in python which would always run in the background of the victim and would listen to the packets incoming and to which port were the packets were coming from. The attacker would send tcp packets to the victim to particular ports in a sequence. Once these packets arrive, the backdoor will execute a system command which would grant full access to the attacker.
Diploma in Electrician
Government of India
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio shows a strong inclination towards Machine Learning and Data Science, which aligns well with an ML Engineer role. The diversity of projects, from market segmentation to financial prediction and sentiment analysis, indicates a broad interest in applying ML across different domains. However, the projects are all listed as 'personal' and lack explicit team collaboration or open-source contributions, which could be a factor in assessing cultural fit for a collaborative environment. The career progression at a single large organization (JPMorgan Chase & Co.) suggests stability and growth within a corporate structure.
Soft Skills & Operational Fit
The candidate's progression to a Lead role at JPMorgan Chase & Co. suggests leadership potential and the ability to manage complex AI/ML initiatives. The project descriptions, while detailed, could benefit from more explicit articulation of problem-solving approaches and impact. Without psychometric test results, it's difficult to assess logical reasoning, work attitude, stress handling, or team collaboration directly.