Pruthweesha S A

Data Scientist

Bengaluru, Karnataka, India2 yrs 10 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Developed LLM models saving $2M annually.
Prototyped Computer Vision solution improving customer retention by 11%.
Set up CI/CD pipeline reducing downtime by 70%.

Stackforce AI infers this person is a Data Scientist with expertise in Healthcare and AI-driven solutions.

Contact

Skills

Core Skills

Data ScienceNeural NetworksComputer VisionData MonitoringCi/cd3d AnimationData Modeling

Other Skills

PyTorchTransformersYOLOv5GolangGitHub EnterpriseAutoHotKeyScikit-LearnPassionate about WorkCharacter Modeling3D ModelingProblem SolvingUnreal Engine 5RiggingVideo ProductionGame Art

About

Data Scientist and Software Developer with three years of professional experience. Proven expertise in NLP, Computer Vision, Large Language Model fine-tuning. Familiar with various ML tools and technologies such as PyTorch, NumPy, Pandas, TensorFlow, Scikit-learn, etc. and software development frameworks and languages such as .NET, C++, and C#.

Experience

2 yrs 10 mos

Total Experience

1 yr

Average Tenure

10 mos

Current Experience

Apparel group

Data Scientist

Aug 2025 – Present · 10 mos

Data Science

Apple

Junior Data Scientist (IC)

Nov 2024 – Jul 2025 · 8 mos · Bengaluru, Karnataka, India · Hybrid

Philips

Data Scientist Intern

Jun 2023 – Jul 2024 · 1 yr 1 mo · Bengaluru, Karnataka, India · On-site

1. Developed, deployed and maintained LLM-based classification models using Neural Networks
made with PyTorch and Transformers for handling over 1.5 million Phillips Personal Health
product queries and complaints, which resulted in annual cost savings of 2M USD for
the Grooming & Beauty Business Unit.
2. Prototyped a Computer Vision solution for shelf life estimation of OneBlade components
using YOLOv5, which improved customer app engagement by 14%, and resulted in a 11%
increase in customer retention for the product line.
3. Developed a module in Golang to monitor parsing of Oral Healthcare data from 17 M monthly
users, and automated generation of smart diagnostic reports for businesses. This enabled
the business to track the performance of the parser with 96% lower latency.
4. Successfully set up the CI/CD pipeline for the Oral Healthcare codebase on GitHub Enterprise
after migrating it from Azure DevOps. This enabled annual cost savings of 150K USD and
reduced pipeline downtime by 70%.