Tanya Agarwal

Software Engineer

Rajasthan, India4 yrs 10 mos experience

Highly Stable

Key Highlights

Expert in Machine Learning with real-world applications.
Finalist in national-level hackathon for innovative solutions.
Proficient in multiple programming languages including C++ and Python.

Stackforce AI infers this person is a SaaS-focused software engineer with strong expertise in Machine Learning and data-driven solutions.

Contact

Skills

Core Skills

Machine LearningJava

Other Skills

C++PythonData AnalysisJavaScriptMySQLHTMLAlgorithmsEvent PlanningPublic SpeakingData ModelingHadoop

About

Dedicated tech-enthusiast and proficient in C++, python, Machine Learning, who aims at solving real life problems with the use of technology. 1. Worked on very important use case of Machine Learning i.e. Customer Retention in Adobe Systems. 2. Finalist in Smart India Hackathon. It is a national level technical event organized by government of India.I have been selected for final level where we tried to solve real life problem of Orissa small scale Industry for easy identification of land.

Experience

4 yrs 10 mos

Total Experience

3 yrs 8 mos

Average Tenure

1 yr 2 mos

Current Experience

Salesforce

Member of Technical Staff

Mar 2025 – Present · 1 yr 2 mos · Bengaluru, Karnataka, India

Working as a Software Development Engineer in the Tableau team at Salesforce, focusing on Tableau Bridge—a critical component that enables secure connectivity between on-premise data sources and Tableau Cloud.

C++PythonMachine Learning

Adobe

3 roles

Software Development Engineer 2

Promoted

Feb 2023 – Mar 2025 · 2 yrs 1 mo · Noida, Uttar Pradesh, India

Worked on TypeQuest, Adobe’s font management platform, building features to enhance font discovery, preview, and integration across creative applications.

Java

Software Engineer

Jul 2021 – Feb 2023 · 1 yr 7 mos · Noida, Uttar Pradesh, India

Product Development Intern

Jan 2020 – Jul 2020 · 6 mos · Noida, Uttar Pradesh, India

Worked on Machine Learning Project to target users who are expected to churn early in the user's life cycle using gradient boosting algorithm. The model is further extended to carry out customer segmentation of the predicted set to analyse possible reasons for churn, thereby providing a possibility of selective customer targeting.

Machine Learning