Bhavya Doshi

Analyzing operational and service data from intralogistics and warehouse management systems to identify AI-driven automation opportunities and viable use cases for predictive maintenance and digital services. Supporting concept development for AI agent integration and internal tooling through structured data analysis, and LLM API benchmarking. Contributing to innovation prioritization by translating analytical findings into structured use case proposals for technical and business stakeholders.

Performed statistical analysis, data preprocessing, and validation across high dimensional research datasets, preserving structural integrity for downstream modeling and publication. • Developed Power BI dashboards to communicate complex findings to academic stakeholders and support reporting workflows. • Co-authored a peer-reviewed Springer publication (2025) on geometrical and regression based modeling, contributing statistical analysis, visualization pipelines, and software development.

Automated Salesforce data workflows across B2B and B2C pipelines, cutting manual data latency by 30% and reducing reporting cycle time for 20 commercial stakeholders. Developed Power BI dashboards for campaign and operational KPIs, enabling faster decision making and clearer performance tracking. Managed structured CRM and sales data across Salesforce and Power BI, supporting commercial reporting for regional B2B operations across the DACH market.

Developed a Python based optimization workflow for automated tape laying of thermoplastic composite laminates, integrating geometry and meshing logic. Implemented a CLT based screening algorithm to rank stacking sequences under specific load cases and validate structural viability. Validated optimization outputs using simulation tooling, ensuring reproducibility and computational accuracy across engineering test cases.

Built Python and PostgreSQL migration scripts for Odoo ERP upgrades, reducing customer query downtime during migration by around 40%. Optimized ERP modules and backend workflows, improving system functionality and query performance by 25%. Developed automation workflows, API integrations, and responsive JavaScript interfaces to support scalable business operations.

Managed large scale experimental datasets and automated data cleaning pipelines in Python to improve prediction accuracy for plasma disruption forecasting. Deployed an LSTM based deep learning model for plasma disruption sequence forecasting, improving prediction reliability across diverse HPC environments. Used high performance computing clusters to optimize computational workflows and support reproducible experimentation.