Harsh S
ML Engineer Preferred Title
$25.00 /hr $ 43.0K /yr Hourly Rate and Yearly Salary
Overview
Basic Summary
LD Talent History - Average Response Time 10.6 hours
- Average Count of Messages/Day 0.8
- Project Completion Rate 2/4
- Interview Acceptance Rate 4/6
- Timezone Overlap 12h PST, 12h UTC
- Code Quality 60%
- Soft Skill Attributes 60%
- Expertise 2/3
- Coding Challenges 60%
- No. of Lifelong Learning Projects 2
- No. of Coding Challenges Completed 2 More details
- Desktop Windows
- Phone Android
- Member Since Mar 14, 2020
- Profile Last Updated Jul 25, 2020
- Last Activity Feb. 10, 2024, 11:11 a.m. UTC
- Location India
Profile Summary
A Machine Learning engineer with experience in Deep Learning and Data Science. Application of analytical skills for problem solving keeps me passionately involved. I am interested in applying technology for real applications and always desire working in a fast paced, test-driven and collaborative engineering environment.
Skills
Total Experience: 8 years
Weekly Availability
Timezone Overlap with 06 - 21 per Week: 12h PST, 12h UTC
Day
Monday
Tuesday
Friday
Saturday
UTC
18 - 21
18 - 21
18 - 21
18 - 21
PST
11 - 14
11 - 14
11 - 14
11 - 14
Vetting
- Interview Data
- Agile Development Process
- Productivity and Responsiveness
- Teamwork
- Software Engineering
- Logical Thinking
- Technical Strength
- Intellectual Merit
- English Communication
- Documentation
- System Design
- Coding Challenges60%
- Algorithms Score60%
- General Score60%
- Easy Algorithm
- Correctness60%
- Performance60%
- Medium Algorithm
- Correctness60%
- Performance60%
- Hard Algorithm
- Correctness60%
- Performance60%
- Expertise2/3
- Design Patterns and Architectures2/3
- Debugging2/3
- Stack Traces2/3
- Testing2/3
- System Administration2/3
- Soft Skill Attributes60%
- Entrepreneurial60%
- Whole Brained60%
- Divergent Thinking / Creativity60%
- Design Ability60%
- Empathy60%
- Project Management Ability60%
- Security60%
- Code Quality60%
- Complex Logic60%
- Models60%
- Controllers60%
- Templates60%
- APIs60%
- Training/Testing Data Models60%
- Code Readability60%
- Ongoing Evaluation
- Number of Lifelong Learning Project2
- Number of Coding Challenge Completed3
Experience
Data Scientist
Employment
Jul 2018 - Present
American Express Company
Finance Industry
Project: Personalisation of offer campaigns
- Machine Learning: Built Machine learning models for personalised offers on American Express Cards
- Data Science: Perform Quantitative Analysis for designing offer construct
Data Scientist
Internship
May 2017 - Present
Maroon.ai Company
Technology Industry
Project: Data Science & NLP
- Machine Learning: Built model for text documents classification using Neural Networks and NLP.
- Natural Language processing: Developed algorithm for hierarchical clustering
Contributing Writer
Contract
Mar 2019 - Present
TowardsDataScience | Medium Company
Technology Industry
Project: Blog writing
- Data Science: Authoring articles revolving around Deep Learning, Machine Learning and Data Science in general
- Machine Learning: Been read by more than 2.5k readers, articles attract 1k+ views monthly with best read ratio of 63%
Student
Course Project
Feb 2018 - Present
IIT Delhi Company
Technology Industry
Project: Dominant Speaker Recognition in videos
- Extracted and prepared frame-wise data of 6 speakers (1500 images per speaker) via youtube using Opencv
- Computer vision: Used OpenCV's Classi er and a heuristic approach based on consecutive images' correlation to remove noise
- Deep Learning: Implemented Grad-CAM and t-distributed stochastic neighbor embedding (t-SNE) to visualize deep net
Student
Course Project
Jan 2018 - Present
IIT Delhi Company
Technology Industry
Project: Hard Coding Core Work-horses of Deep Neural network
- Machine Learning: Hard coded a feed forward neural network from scratch without using machine learning libraries
- Added Lasso, Ridge regularization, batch normalization and dropout to improve the working of DNN
Student
Course Project
Jan 2017 - Apr 2018
IIT Delhi Company
Finance Industry
Project: Thesis: Stock prediction using Deep Learning
- Prepared our own novel nancial news data via scraping web pages for several stocks and indexes
- Natural Language Processing: Experimented with several novel techniques to quantify text, namely, word2vec, tf-idf and sentiment scoring
- Machine Learning: Combined stock and text data using t-distributed stochastic embedding (t-SNE), fed into LSTM architecture
- Implemented CNN-LSTM taking several days' news as input to preserve both spatial and temporal component