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Data Scientist at Jiostar | Ex-Swiggy, ZS | M.Tech from BITS
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Jiostar
Data Scientist
June 16, 2026 – Present
Competitive_Data_Science
December 24, 2018 – February 27, 2019
This is a course provided by Coursera
View ProjectRegression-Analysis
November 30, 2018 – March 4, 2019
Linear Regression Analysis on Wine data - Pre-processing data, Exploratory Data Analysis, Building a model, Check assumptions, Goodness of fit and Compare with different methods.
View ProjectTime-Series-Forecasting
October 23, 2018 – April 14, 2019
Rainfall analysis of Maharashtra - Season/Month wise forecasting. Different methods have been used. The main goal of this project is to increase the performance of forecasted results during rainy seasons.
View ProjectZS-Data-Science-Challenge
July 23, 2018 – July 30, 2018
A Data science challenge - "Mekktronix Sales Forecasting" organised by ZS through Hackerearth platform. Rank: 223 out of 4743.
View Project100DaysOfMLCode
July 15, 2018 – October 17, 2018
Learning Machine Learning and showcasing my work for 100 Days.
View ProjectNLP
March 31, 2018 – May 10, 2018
This repository contains Sentiment Classification, Word Level Text Generation, Character Level Text Generation and other important codes/notes on NLP. Python and Keras are used for implementation.
View ProjectComputer-Vision
February 5, 2018 – June 6, 2018
Computer Vision - Impemented algorithms - Hybrid image, Corner detection, Scale space blob detection, Scene classifiers, Vanishing point detection, Finding height of an object, Image stitching.
View ProjectNatural-Language-Processing
August 31, 2017 – March 17, 2018
Language Modelling, CMI vs Perplexity
View ProjectInformation-Retrieval
August 15, 2017 – January 19, 2018
Mainly on text documents. Implemented a Mini Search Engine using different algorithms and then summaried documents using lexrank.
View ProjectCultural Fit Analysis
The candidate's projects are primarily personal and academic in nature, indicating a strong drive for self-learning and exploration in Data Science. However, the lack of team-based projects or professional experience (beyond a future-dated role) makes it difficult to assess cultural fit, particularly regarding collaboration and adaptability within an organizational structure. The diversity of project topics (CV, NLP, Time Series, Regression) suggests a broad interest in the field.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's project descriptions are concise, but there's no information on collaboration, communication style, or problem-solving approach in a team setting.