NIMESH BHAVSAR
Experience

Where the work has happened.

  1. Graduate study in data science, covering statistical learning, machine learning, and the mathematical foundations behind them. 70+ credits completed through the second semester.

    • Statistical Learning
    • Machine Learning
    • Bayesian Analysis
    • Research Methods
    • Coursework and research spanning machine learning, statistics, and data-driven methods.
    • 70+ credits completed by the end of the second semester.
    • Independent research and experimentation, including causal inference applied to Formula 1 pit-strategy data.

  2. Concurrent MBA with a specialization in AI/ML, run alongside professional and graduate work.

    • AI/ML Strategy
    • Business Applications of AI
    • Specialization coursework focused on applying AI/ML in a business and decision-making context.

  3. Worked on data-driven decision systems — spanning predictive modeling, enterprise retrieval-augmented generation, and applied generative AI.

    • Predictive Modeling
    • Enterprise RAG
    • Generative AI
    • Agentic Workflows
    • Built and evaluated predictive models to support business decision-making.
    • Designed enterprise RAG systems — document ingestion, chunking, embeddings, retrieval, and grounded generation.
    • Prototyped agentic, tool-using AI workflows on top of LLMs.
    • Ran embedding and retrieval experiments to evaluate document AI readiness.

  4. Undergraduate degree in Computer Science — the foundation for everything since.

    • Computer Science Fundamentals