Jul 2023 – Aug 2024 · 1 yr 1 mo · Gurugram, Haryana, India · On-site

• As a Data Engineer in the Firm-wide Analytical Solutions (FAS) team at KPMG, I developed scalable data-driven solutions for high-profile US-based clients across industries. My work focused on enhancing data accuracy, operational efficiency, and automation in data pipelines.
• Key accomplishments:
• Led the design and implementation of CI/CD pipelines and a Generative AI-powered Data Quality Framework, resulting in a 20% boost in operational efficiency and improved data integrity.
• Developed and optimized ETL processes using Hadoop, Spark, and distributed data processing frameworks, reducing manual effort by 25% and improving data accuracy by 30%.
• Spearheaded the Value Tracking and Connected Datasets projects, leveraging Azure, PostgreSQL, and Python to drive insights and optimize client outcomes.
• Played a key role in cloud migration projects, supporting Azure cloud architecture and ensuring seamless integration with existing data infrastructures.
• Collaborated with cross-functional teams to deliver impactful solutions in data quality management, improving the accuracy and reliability of client data.
• This experience strengthened my technical skills in data engineering, cloud computing, and AI, while sharpening my leadership and project management abilities.

Apr 2022 – Sep 2022 · 5 mos · Singapore, Singapore · On-site

• Data Science Team
• During my internship at Ulavi Technologies, I applied data analysis techniques to drive key insights and improve decision-making processes for clients. I contributed to the development of data visualization solutions and led a team in conducting exploratory data analysis (EDA).
• Key accomplishments:
• Developed and maintained interactive Tableau dashboards and scorecards, reducing reporting time by 30% and improving real-time visualization of key performance indicators (KPIs).
• Led a team of 8 interns, overseeing comprehensive exploratory data analysis (EDA) using Python, SQL, and Excel, which uncovered critical insights that enhanced decision-making by 25%.
• Collaborated with cross-functional teams to streamline data processes and improve overall data management workflows.
• Delivered actionable insights from large datasets, which improved the company’s ability to make data-driven decisions and enhance performance metrics for clients.
• This role helped me strengthen my leadership skills and deepen my expertise in data analysis, data visualization, and team management.

Jul 2021 – Aug 2023 · 2 yrs 1 mo · New Delhi, Delhi, India · On-site

• As a Microsoft Learn Student Ambassador, I served as a campus leader, driving engagement and awareness around cutting-edge technologies like cloud computing, AI, and data science. I led workshops, webinars, and hackathons to empower fellow students with hands-on learning experiences in Azure, Microsoft Power Platform, and GitHub.
• My key contributions included:
• Organizing technical events and workshops to foster a strong tech community and increase student participation in Microsoft technologies.
• Mentoring peers on building projects with Microsoft tools, helping them acquire certifications and practical knowledge in Azure and AI.
• Advocating for Microsoft’s vision of empowering every student to achieve more through technology, while expanding the reach of the Learn platform on campus.
• This role allowed me to deepen my knowledge in cloud and AI technologies, enhance my leadership skills, and support a diverse community of learners passionate about innovation.

May 2021 – Aug 2021 · 3 mos · New Delhi, Delhi, India · Hybrid

• As a Machine Learning Intern at CISCO, I focused on developing machine learning models to optimize network performance, delivering impactful solutions that improved operational efficiency.
• Key accomplishments:
• Designed and trained machine learning models using Python, TensorFlow, and scikit-learn, automating network performance optimization and improving efficiency by 20%.
• Conducted extensive data preprocessing, feature engineering, and model evaluation on large-scale datasets to enhance the accuracy and performance of machine learning models.
• Utilized Docker and Kubernetes for deploying and scaling machine learning models across cloud environments, streamlining the model deployment process.
• Collaborated with cross-functional teams to ensure successful integration of machine learning solutions into production systems, contributing to scalable, real-time optimizations.
• This internship provided me with valuable hands-on experience in applying machine learning techniques to solve real-world problems, honing my technical skills in data science and cloud technologies.

May 2021 – Aug 2021 · 3 mos

May 2021 – Jun 2021 · 1 mo

Apr 2021 – May 2021 · 1 mo

Jul 2020 – Jul 2021 · 1 yr · Worldwide

May 2018 – Apr 2019 · 11 mos · New Delhi, Delhi, India

May 2017 – Apr 2018 · 11 mos · New Delhi, Delhi, India

Apr 2018 – May 2018 · 1 mo · New Delhi, Delhi, India