Data Scientist | 6+ Years Experience | GenAI, LLM, Time Series, Azure, AWS, Dataiku

I am an innovative Senior Data Scientist and Machine Learning Engineer with nearly six years of experience architecting end-to-end AI/ML solutions, specializing in generative AI (LLMs, RAG, multi-agent systems) and advanced time series forecasting on cloud platforms. Driven by a passion for translating complex data into actionable insights, I excel at automating processes and delivering measurable business value. I thrive in dynamic, collaborative environments that emphasize creativity, scalability, and continuous learning.

Core Skills & Expertise

• Programming & Frameworks: Python (Pandas, NumPy, Scikit-Learn), R, PySpark, TensorFlow, PyTorch, LangChain, REST APIs (Flask, FastAPI), Streamlit
• Machine Learning & AI: Supervised/Unsupervised Learning, Deep Learning, NLP, Generative AI (LLMs, prompt engineering, RAG, HyDE), computer vision basics, AutoML pipelines, reinforcement learning concepts, AI ethics & explainability
• Data Engineering & Big Data: ETL/ELT pipelines, SQL/NoSQL databases, Apache Spark, Hadoop ecosystem, real-time ingestion (Kafka), cloud data lakes, data governance, data quality frameworks
• Cloud & MLOps: Azure (OpenAI Service, Databricks, Functions, ML), Dataiku, AWS/GCP familiarity, Docker, Kubernetes, CI/CD (Azure DevOps, GitHub Actions), serverless architectures, monitoring, model drift detection, automated retraining
• Analytics & Visualization: Time-series forecasting (Prophet, Darts, custom models), predictive modeling, A/B testing, statistical analysis, data visualization (Tableau, Power BI, matplotlib), business intelligence collaboration
• Soft Skills & Collaboration: Agile/Scrum methodologies, cross-functional teamwork, stakeholder engagement, clear communication of technical concepts, leadership & mentoring, ROI-focused problem solving, adaptability in dynamic settings

Work Experience & Achievements (3–4 Key Highlights)

• AI-Powered Code Modernization: Led an AI multi-agent system upgrading ~650k lines of Java (8→21, Struts→Spring Boot), cutting manual effort by ~65% and accelerating migration timelines for a major client.
• Intelligent Virtual Assistant with RAG: Deployed a HyDE-enabled assistant over 8,000+ ServiceNow tickets, slashing average resolution from ~6 hours to ~5 minutes, reducing human intervention by 60% and boosting satisfaction by 35%.
• Market Forecasting Platform: Developed custom time-series models that improved forecast accuracy by 65% versus legacy approaches, saving approximately $1M annually through optimized demand and price predictions.
• Real-Time Quotation Engine: Architected serverless pipelines ingesting 30+ data sources, achieving full coverage and an XGBoost ensemble with 92% accuracy, driving a 12% margin uplift on a $5.5M portfolio.
• Predictive Maintenance for IoT: Built end-to-end pipelines handling 4M+ daily sensor readings for forecasting maintenance needs, reducing unplanned downtime by 35% and lowering operational costs.
• Policy Chatbot (Llama): Created a semantic-search chatbot over 900+ policy documents, delivering 95% accuracy on HR queries and cutting lookup effort by 80%.
• Scalable MLOps & Monitoring: Implemented CI/CD and monitoring frameworks for multiple AI services, ensuring 99.9% uptime and reducing infrastructure costs by up to 30%.

I’m seeking a Mid to Senior Data Science or AI Engineering role where I can lead generative AI, advanced analytics, data processing pipelines, and time series forecasting initiatives, champion responsible AI practices, and build cloud-native, scalable solutions. I aim to partner cross-functionally to tackle complex challenges and drive measurable business outcomes.