Building intelligent systems from data • ML • LLMs • Scalable pipelines
I’m a Data Scientist with 4+ years of experience building scalable data systems and applying machine learning to solve real-world business problems.
- ⚡ Improved retailer churn by 30% using ML models
- 📈 Increased sales revenue by 20% through statistical analysis
- 📊 Worked with 5TB+ data pipelines for analytics and modeling
- 🤖 Hands-on with LLMs, prompt engineering, and fine-tuning
I specialize in bridging Data Engineering → Machine Learning → Production systems, enabling end-to-end AI solutions.
- Languages: Python, SQL, R, PySpark
- ML & AI: XGBoost, LSTM, NLP, CNN, LLMs, Prompt Engineering
- Data: Spark, Databricks, Airflow, Snowflake, BigQuery
- Cloud: Azure, AWS, GCP
- Visualization: Tableau, Power BI
- Tools: Git, Docker, dbt, CI/CD
- Built real-time expense tracking using GPT-4o + FastAPI
- Automated receipt parsing and structured financial insights
- Integrated interactive feedback using robotics simulation
- Fine-tuned LLM on 10K+ conversations
- Designed dual-objective evaluation framework
- Improved efficiency using Qwen over LLaMA
- Built XGBoost model → reduced churn by 30%
- Developed pipeline using Databricks + PySpark
- Enabled data-driven retention strategies
- Performed EDA on 84K+ records
- Built Random Forest + Neural Networks
- Applied feature selection (RFE) for optimization
🔹 End-to-end ML pipelines (data → model → deployment)
🔹 LLM-powered applications & AI agents
🔹 Scalable data platforms (Spark / Databricks)
🔹 Predictive models for business impact
- 📚 Microsoft Azure AI-900 Certified
- 🥉 3rd Place – LLM Alignment Hackathon
- 🎟️ NVIDIA GTC Golden Ticket Submission
- 🎤 Speaker – Women in Analytics
- LLM evaluation & alignment
- AI agents & autonomous workflows
- Real-time ML systems
I’m open to:
- Data Science / ML opportunities
- AI/LLM collaborations
- Interesting and impactful problem statements
⭐ If you find my work interesting, consider emailing me!