• Provided strategic guidance to clients in the automotive and services sectors, helping them leverage data-driven solutions to optimize operations and drive business innovation.
• Successfully pitched and secured multiple client projects by showcasing tailored data solutions, demonstrating value propositions, and leveraging market knowledge to expand engagement opportunities.
• Facilitated Agile sprints with clients to develop, evaluate, and deploy scalable data-driven solutions, ensuring alignment with business goals and delivering high-quality outcomes.

• Led the development and deployment of cloud-based solutions that automated the integration, analysis, and management of large-scale electric vehicle datasets, ensuring seamless data flow and enhanced operational efficiency.
• Implemented CI/CD pipelines to enable rapid delivery, continuous improvement, and faster time-to-market for analytics platforms.
• Automated ETL workflows and integrated scalable storage solutions, reducing manual effort and accelerating insights for clients in the automotive sector.

• Led the migration of legacy ETL solutions to modern open-source platforms, significantly improving system efficiency and reducing operational costs by 50% .
• Transitioned data analysis workflows from VBA to Power BI , resulting in a 40% increase in processing speed and enabling more dynamic and interactive data visualization.
• Automated ETL processes and integrated scalable cloud-based solutions, ensuring seamless data flow and reducing manual intervention.
• Collaborated closely with clients to align technical strategies with business goals, ensuring the adoption of new tools enhanced both operational performance and decision-making capabilities.
• Communicated the benefits of the migration to stakeholders, securing their trust and support for ongoing collaboration and future projects.

Topic on request

• Pitched project advancements and PoCs to academic and industry stakeholders, leading cross-functional meetings to align technical outcomes with business requirements and secure buy-in for further development.
• Developed a novel system that combined multiple data sources and languages to improve representation of social media content, achieving more accurate and comprehensive insights than previous methods.
• Automated data labeling processes and extracted features using state-of-the-art NLP models and computer vision models, reducing manual labeling time by 40% and accelerating preprocessing workflows for large-scale datasets.
• Engineered neural network architectures and supervised the training to address a classification task, achieving an accuracy improvement of 20% over baseline models.
• Evaluated the learned embedding of the trained models using unsupervised machine learning methods, resulting in a 20% increase in clustering performance.

• Architected a solution for managing automotive driver assistance test case data, enabling real-time data processing and remote access through touch-command simulations, while working closely with clients to align technical strategies with business goals.
• Coordinated a cross-functional agile team, managing tasks, tracking tickets, and prioritizing client requirements through sprint planning, while acting as a liaison between the technical team and clients to ensure transparency and progress updates.
• Deployed multiple cloud-native solutions utilizing AWS, ensuring seamless remote testing capabilities, and communicated deployment strategies and benefits to clients, which helped secure ongoing collaboration and trust.

• Designed a data analysis pipeline for bacterial genome research, enabling the manipulation of gene expressions for over 100 doctoral students and integrated ETL workflows and statistical analysis tools to automate data processing, reducing manual effort by 40% and accelerating insights.
• Developed and led the implementation of the ?Gaussian Hellinger Very Fast Tree? (GHVFDT) for detecting pulsar candidates, processing over 100,000 records in an unbalanced data stream, and conducted 50+ experiments to evaluate model performance, resulting in a 15% increase in detection accuracy over benchmark models.
• Preprocessed and fine-tuned large-scale internet crawl data to improve a Large Language Model (LLM) for sentiment analysis, increasing accuracy by 20%, and deployed the model into a web application for real-time user insights and decision-making.