Senior Data Scientist with >5 years of experience designing and deploying enterprise-scale AI, ML, and Generative AI systems. Proven track record of building production ML and Data platforms serving millions of records daily, reducing operational costs by 1/2 to 1/6, and accelerating decision-making pipelines by 3x to 5x across large, regulated enterprises.
Designed and deployed production ML models across classification, regression, NLP, and optimization workflows, achieving 99%+ performance over baseline heuristics. Strong experience in model evaluation, interpretability, and risk-aware deployment.
Built enterprise-grade Generative AI solutions using LLMs, embeddings, and retrieval-augmented generation (RAG), enabling 3x-5x reduction in manual analysis effort and supporting thousands of monthly users.
Led end-to-end MLOps pipelines with CI/CD, automated retraining, monitoring, and drift detection, improving model reliability to 99.9% uptime and cutting deployment cycles from weeks to hours.
Architected scalable data pipelines on AWS processing TB-scale datasets daily, enabling real-time inference, feature reuse, and cost-optimized analytics workloads.
Led cross-functional teams delivering AI and GenAI solutions for global clients. Spearheaded platforms supporting 20+ enterprise applications, improving operational efficiency by 2-6x and reducing analytics turnaround time by 1/4th.
Led medium to large-scale data science and AI initiatives, partnering with business stakeholders to define problem statements and deliver data-driven solutions across analytics, retail, and enterprise domains.
Designed, developed, and deployed end-to-end ML pipelines using Python and PySpark, covering classification, regression, NLP, forecasting, and optimization use cases, leveraging AWS Glue, Iceberg, Athena, S3, Redshift, and SageMaker for scalable training and production deployment.
Built and operationalized advanced ML and NLP models, including classification, entity extraction, semantic search, and forecasting solutions, improving decision-making and reducing manual processing by up to 100%.
Developed Generative AI and RAG-based applications using OpenAI GPT models, AWS Bedrock, Anthropic Claude, Google Gemini, LangChain, and vector databases, replacing legacy rule-based and manual workflows.
Implemented robust MLOps best practices including data and model versioning, automated training and inference pipelines, model registry, monitoring, and CI/CD-driven deployments achieving 20–40% improvements in model accuracy and stability.
Evaluated model performance through rigorous testing, documentation, and monitoring, ensuring production reliability and compliance with enterprise standards.
Collaborated closely with Staff and Principal-level engineers and data scientists to ensure timely delivery of ML products and knowledge sharing across teams.
Engineered secure, scalable data integrations across SAP HANA, Oracle, Snowflake, REST APIs, and SFTP, enabling analytics-ready datasets with 99.9% data availability.
Contributed to algorithmic innovation and reusable ML frameworks, accelerating solution development and adoption across multiple business units.
Advanced coursework and research in machine learning, deep learning, reinforcement learning, and NLP, with hands-on implementation of large-scale AI systems and optimization-driven decision models.
Built a strong foundation in algorithms, probability, statistics, and software engineering, supporting long-term specialization in applied AI and data systems.
Developed an NLP-based bias detection framework achieving 90%+ classification accuracy, with emphasis on robustness, interpretability, and fairness-aware evaluation.
Designed an adaptive learning system supporting students with special needs, improving engagement metrics through personalized recommendation strategies.
Built a retrieval-augmented generation system with OCR and NLP over large document corpora, reducing information retrieval latency by replacing human manual efforts while maintaining auditability.
Applied ML-driven optimization models to business decision systems, delivering multi-million dollar efficiency improvements across planning and forecasting use cases.