Professional Overview
Data Scientist with 6 years of combined Data Science and Machine Learning engineering experience( supervised and unsupervised learning, NLP), specializing in building ETL pipelines, data models, and dashboards to inform product decisions. Proven expertise in SQL over large datasets, Pytorch, Python data analysis, Deep Learning Frameworks and AWS.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Analyzed vehicle data for failure patterns improving quality and built metrics.
- Identified data sources, automated pipelines using Python, SQL, Airflow.
- Optimized anomaly detection using ML techniques and provided solution.
- Ensured model training/evaluation (supervised ML) with statistical refinements.
- Deployment via AWS services
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Partnered with other analyst in the pricing team for business context analysis.
- Built RAG LLM app using Ray dataset and scaled workloads across Ray workers.
- Evaluated application configurations for fine-tuning, prompt-engineering.
- Drove scalable AI/ML systems from data chunking/embedding(Hugging Face).
AI Expertise
Skills
Employment Type
Education
Field of Study: Computer Science(Machine Learning and Data Science)
Areas of Expertise
Data Science & Analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.
