Shaily Parikh
Data AnalystSenior Software Engineer at Google
Google
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Senior Software Engineer at Google
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
University of Southern California Viterbi School of Engineering
Master of Science, Computer Science
January 1, 2017 – January 1, 2019
The Maharaja Sayajirao University of Baroda
Bachelor of Engineering (BE), Computer Engineering
January 1, 2013 – January 1, 2017
Software Engineer
May 1, 2022 – Present
Software Engineer
June 1, 2019 – May 1, 2022
CiSoft Lab
Machine Learning Research Assistant
June 1, 2018 – May 1, 2019
Greater Los Angeles Area
AAAI, USC
Program Manager
January 1, 2018 – April 1, 2019
Greater Los Angeles Area
University of Southern California
Grader (CS 360-Intoduction to Artificial Intelligence)
January 1, 2018 – May 1, 2018
Greater Los Angeles Area
Maharaja Sayajirao University of Baroda, Vadodara
Intern
July 1, 2016 – April 1, 2017
Vadodara Area, India
Computer Society of India
Volunteer
January 1, 2016 – January 1, 2016
Vadodara Area, India
Computer Society of India
Volunteer
January 1, 2014 – January 1, 2014
Vadodara Area, India
Boeing Data Science Competition
March 1, 2019 – Present
Completed as finalists in the Boeing Data Science competition at USC. Performed exploratory data analysis to identify factors affecting delays in commercial airlines. Built a time series model to predict delays in future which can help in better airline scheduling.
Kickstarter story
August 1, 2018 – Present
Want your Kickstarter campaign to be successful? In this project, I have analyzed data available about Kickstarter projects on Kaggle.com in Tableau to identify the category of projects that are most successful on Kickstarter.
Kaggle's Santander Value Prediction Challenge
July 1, 2018 – Present
Participated in Kaggle's Santander Value Prediction Challenge to predict the value of a transaction by potential customers of Santander. Achieved 1.46 RMSLE on test data by training a LightGBM model using 5 fold cross-validation.
McKinsey Analytics Online Hackathon
July 1, 2018 – Present
Achieved 286 / 5052 rank on the private leaderboard (Score : 0.78648) Achieved 221 / 5052 rank on the public leaderboard (Score : 0.72503) Performed exploratory data analysis and trained a CatBoostRegressor model to predict premium renewal with 0.78648.
Kaggle's Avito Demand Prediction
June 1, 2018 – Present
Avito, Russia’s largest classified advertisements website has challenged to predict the demand for a product based on its advertisement. The dataset includes information about the user who posted the advertisement, the product that is being advertised along with the title and description. Also, information about similar advertisements in the given time frame is also provided. My solution tries to leverage the textual information in form of title and description, numerical information as well as categorical data to predict the demand of the product advertised. This solution achieves 48.327% better performance over the baseline solution.
Music Recommendation System
June 1, 2018 – Present
Performed extensive feature engineering on 7.4 million rows to extract and generate useful features for Machine Learning model using Python libraries like Pandas, Sklearn, Seaborn, and Matplotlib. Achieved 65.81% accuracy on private test data using LightGBM to gain 7.3% better accuracy score over the baseline solution to predict whether a user would listen to a particular song within a month of the first listening event.
Lane detection in video
April 1, 2018 – Present
Developed a pipeline to detect lanes in a video. Detection pipeline written in Python using OpenCV library. Do visit https://sites.google.com/usc.edu/lane-detection/home fro more details.
Classification of Images (Circle versus Triangle)
March 1, 2018 – Present
Trained a Convolutional Neural Network to classify images of circles and triangles. Do visit https://sites.google.com/usc.edu/hand-drawing-classification/home for more details.
IrisDataset
November 1, 2017 – Present
1) Used multi layer perceptron with three hidden layers to get an accuracy of 94% on test dataset. 2) Used K-Nearest Neighbour to classify with 100% accuracy with the help of K-Fold cross validation.
Inference Engine
November 1, 2017 – Present
Inference Engine using first order logic resolution. Implemented Resolution using proof by contradiction. Implemented concepts such as "Unification","Factoring" and "Standardizing". Code thoroughly tested on more than 50 test cases.
Fruitrage : Gaming agent
October 1, 2017 – Present
Designed a gaming agent to compete against the adversarial agent in an n x n board game under time constraints using the Minimax algorithm with alpha-beta pruning in Java. Gaming agent ranked in top 25 amongst 500 gaming agents.
Lizard Problem : Modified n-queen problem
September 1, 2017 – Present
Implemented uninformed(Breadth First Search ,Depth First Search and Simulated Annealing) algorithms in C++ and Python for solving modified n queen problem in larger search spaces with time constraints.
Animated shape filling using images
August 1, 2017 – Present
●Designed an algorithm to fill closed polygon with random sized images in an animated fashion. ●Algorithm written in Python using modified version of breadth-first search
LunarMission
March 1, 2017 – Present
Algorithm based on dynamic programming that allows lunar bot to traverse the lunar surface gaining maximum information and avoiding pitfalls.
Drop
March 1, 2017 – Present
●Conceptualised and developed an Android app that enables local businesses to reach target audience when potential customers search for them. ● App uses Google Maps APIs and Firebase NoSQL database.
Schedule Generation
March 1, 2015 – May 1, 2015
● Designed and developed the database(SQL) and algorithm in Java for automatic generation of schedule for the Computer Science Department,Faculty of Technology and Engineering,Vadodara that directly affects more than 260 students and staff members of the department every semester. ● Modeled the algorithm to be flexible in order to allow scaling according to changes in the curriculum.
NLP with Python for Machine Learning Essential Training
Lynda.com
June 24, 2026 – Present
Kaggle R Tutorial on Machine Learning
DataCamp
June 24, 2026 – Present
Building a Recommendation System with Python Machine Learning & AI
June 24, 2026 – Present
Android Basic Nanodegree
Udacity
June 24, 2026 – Present
Spark for Machine Learning & AI
Lynda.com
June 24, 2026 – Present
Learning C++ Pointers
Lynda.com
June 24, 2026 – Present
Cultural Fit Analysis
The candidate demonstrates a strong passion for data science and machine learning, evidenced by consistent participation in competitions and diverse personal projects. The transition from Software Engineer at Google to a Data Analyst target role suggests a clear career focus. The breadth of projects, from academic AI problems to real-world data challenges, indicates adaptability and a continuous learning mindset. The volunteer and program manager roles suggest an ability to engage with and contribute to a community.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive and results-oriented approach, often highlighting achieved performance metrics (e.g., RMSLE, accuracy, rank in competitions). Participation in organizing ML series and volunteering suggests teamwork and leadership potential. However, specific details on communication style, stress handling, or direct team collaboration from professional roles are limited in the provided data.