Abhishek Arvindhan

Experience

• 

  CodeSpeedy Technology Private Limited

  Sept 2019 - Dec 2019

  Blog Contributor

  Contributed a good amount of posts with respect to Python and concepts related to Machine learning.

• 

  Etag

  Aug 2020 - Sept 2020

  Flutter Mobile App Developer
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  Flatworld Solutions

  Jan 2021 - Sept 2022

  • Software Engineer

    Apr 2022 - Sept 2022

  • Associate Software Engineer

    Jan 2021 - Jan 2022
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  Grow platform, A Bosch Company

  Jun 2023 - Jun 2024

  Data Science Intern

  My main responsibilities included performing exploratory data analysis (EDA), feature importance analysis using SHAP (SHapley Additive exPlanations), and developing predictive models for wind turbine components.🔹Conducted in-depth EDA to gain insights into the behavior and performance of wind turbines and their components.🔹 Analyzed large datasets with over 200,000 data points to identify patterns, anomalies, and correlations.🔹 Utilized SHAP values to interpret the importance of different features in predicting the health and performance of wind turbine components.🔹 Gained a deeper understanding of the factors influencing the reliability and maintenance needs of these assets by performing various integration tests. 🔹 Developed predictive models for wind turbine components, specifically the induction generator and blades.🔹 Played a crucial role in integrating a robust code structure within our Digital Twin architecture. This encompassed asset-level, component-level, and fault-specific coding, ensuring a streamlined approach to asset management and fault prediction. Minder weergeven

• 

  Bundesinstitut für Berufsbildung (BIBB)

  Nov 2024 - now

  Research Assistant

  The German Labour Market Ontology (GLMO) integrates ISCO (occupations), WZ-08 (industries), KldB(job classifications), and Weiterbildung (skills) into a structured knowledge graph, enabling more straightforward insights between various mapping schemes. My main responsibilities are as follows: Data Integration – Processed and harmonized ISCO (occupations), WZ-08 (industries), KldB (job classifications), and Weiterbildung (education programs).🔹 Ontology Construction – Defined key concepts and relationships, ensuring structured career path mapping.🔹 Knowledge Graph Expansion – Linked education programs to job qualifications and occupations to industries for better workforce planning.🔹 Error Resolution – Fixed unintended merging of ISCO and WZ-08, ensuring clear classification boundaries.🔹 Automated Export – Serialized the ontology into Turtle (.ttl) format for seamless integration into AI and Linked Open Data platforms. Minder weergeven