Ayush Patel

Data Analyst with 2+ years of experience at Bosch Global Software Technologies delivering business-critical insights through data visualization, automated reporting, and end-to-end analytics pipelines. Proficient in Python, SQL, Power BI, Tableau, and Excel, with hands-on expertise in ETL design, KPI dashboard development, data migration, and performance analytics.

Designed

Power BI dashboards consumed by leadership for project tracking and efficiency measurement, built data migration pipelines serving 1,000+ users, and automated reporting workflows—reducing processing time by 30% and eliminating 90% of manual effort. Complementary strengths in Software Engineering, Data Engineering, Automation, and AI/ML that enable a full-stack, data-driven approach to complex business problems.

Data Analyst — Automation & Reporting at Bosch Global Software Technologies (2024-01 – Present)

• Engineered a Python-based automation pipeline for STELLANTIS vehicle software signing across 200+ ECU artifacts, reducing end-to-end signing cycle time from 8+ hours to under 45 minutes and eliminating 90% of manual effort in the release workflow.
• Architected a full-scale data migration pipeline from IBM ALM to JIRA, transferring planning, ticket, and task lifecycle data for 1,000+ users—enabling seamless migration of reporting queries and powering management dashboards and operational insights used by leadership and reporting teams.
• Automated user provisioning via Bosch's OneIDM (IAM) platform integrated with JIRA through Python REST APIs, ensuring compliance with Bosch internal standards and providing audit traceability across the entire data access lifecycle.
• Designed and maintained Power BI dashboards and MySQL-backed reports tracking associate work output, project progress, data quality, and team efficiency metrics—used by project managers and senior leadership to drive resource allocation and performance reviews.
• Developed 5+ IBM Rational Rhapsody plugins to extract and transform system model validation results, requirement traceability data, and design quality metrics from complex engineering models—reducing reporting team's data preparation time by 70% and enabling standardized dashboard generation.
• Built Python-driven ETL scripts to extract and transform 11,000+ records from IBM ALM into structured, analysis-ready datasets consumed by project managers for planning analytics and fed into ML model training pipelines across multiple Bosch divisions.
• Designed multi-format data pipelines (JSON, XML, XLS, CSV) from 10+ disparate sources, reducing data processing time by 30% and enabling real-time downstream analytics for cross-functional engineering teams in an Agile/Scrum environment.

Data Science Intern at CybeReconn (2023-04 – 2024-01)

• Performed EDA and statistical analysis on 50K+ PPP project records using Python/Jupyter, improving data accuracy by 20% and delivering automated reporting frameworks that increased initiative success rate by 15%.
• Built NLP-powered LLM applications using LangChain, reducing information retrieval time by 25%.

Data Science Intern at Forsk Technologies (2022-03 – 2022-06)

• Built an NLP sentiment classification pipeline on 15K+ e-commerce reviews, achieving 91% accuracy (+20% over baseline) and automating product satisfaction analytics.

Bachelor of Technology in AI & Data Science – SRM University (2019 – 2023)