Prayush Dawda

Software Engineer

Bangalore, Karnataka, India5 yrs 3 mos experience

Most Likely To Switch

Key Highlights

Avid problem solver focused on impactful projects.
Active contributor to open source initiatives.
Experience in full-stack development and data analysis.

Stackforce AI infers this person is a Software Engineer with experience in open source development and data analysis.

Contact

Skills

Other Skills

PythonC++JavaScriptFlutterTensorFlowGoogle Cloud Platform (GCP)C (Programming Language)Dart

About

Avid problem solver looking to work on meaningful and challenging projects. Active contributor to open source projects.

Experience

5 yrs 3 mos

Total Experience

1 yr 1 mo

Average Tenure

1 yr 10 mos

Current Experience

Google

Software Engineer 3

Jun 2024 – Present · 1 yr 10 mos · Bengaluru, Karnataka, India

Cred

2 roles

Software Engineer 2 - Backend

Promoted

Apr 2023 – Jun 2024 · 1 yr 2 mos · Bengaluru, Karnataka, India

Software Engineer 1 - Backend

Sep 2022 – Mar 2023 · 6 mos · Bengaluru, Karnataka, India

Oppia foundation

2 roles

Google Summer of Code Mentor

May 2022 – Nov 2022 · 6 mos

Google Summer of Code Mentor

May 2021 – Aug 2021 · 3 mos

Blinkit

Software Engineer 1

Jul 2021 – Sep 2022 · 1 yr 2 mos · Bangalore Urban, Karnataka, India

Limechat

Software Engineer Intern

Dec 2020 – Jun 2021 · 6 mos

Oppia foundation

2 roles

Google Summer of Code Student

May 2020 – Aug 2020 · 3 mos

Worked on a full-stack project using Angular and Google App Engine that involved adding interactions which enhanced the math learning experience of learners and creators.

Open Source Developer

Dec 2019 – Mar 2023 · 3 yrs 3 mos

Worked on building features and solving issues that help improve the user experience of the people who create lessons and the people who learn from them.

Tatvic analytics

Data Science Intern

Nov 2019 – Jan 2020 · 2 mos · Ahmedabad, Gujarat, India

Worked on a tool for predicting the virality of online news articles.
Accurately cleaned and preprocessed the raw data of more than 90,000 online news articles.
Applied Latent Dirichlet Allocation for topic modeling and used Doc2Vec to find similarity between articles to predict virality considering the number of views as the metric for accuracy.