S.Swaroop

• Roles & Responsibilities.
• Analyzed and processed large volumes of aerospace data, deriving actionable insights to improve operational efficiency and performance.
• Conducted a detailed analysis of aerospace product orders, revenue, and parts inventory, providing critical insights into trends, demand forecasting, and operational efficiency.
• Translated raw data into meaningful information using statistical techniques.
• Designed and implemented advanced analytical models to track and optimize revenue performance across various aerospace products, identifying key growth opportunities, and cost-saving measures.
• Conducted an in-depth analysis of customer and product performance metrics, generating actionable insights that helped improve customer satisfaction and retention.
• Utilized SQL and Python for data extraction, cleaning, and analysis to ensure high-quality, actionable insights across multiple business functions.
• Conducted statistical analysis and predictive modeling to forecast trends and guide decision-making for engineering and product development teams.
• Worked closely with cross-functional teams, including engineers and product managers, to interpret complex data and present findings to drive improvements in the aerospace business.
• Developed comprehensive reports and dashboards using Power BI and Tableau to visualize business performance, including order fulfillment rates, revenue trends, and inventory turnover metrics.
• Price Developer – Aerospace Parts.
• Managed pricing for aerospace parts using SAP, analyzing costs, market trends, and competitor data to optimize profitability. Collaborated with teams to develop competitive pricing models. Utilized SAP to forecast pricing impacts and enhance pricing accuracy. Ensured compliance with industry standards.

Training: Python with Machine Learning (6 months)

I have undergone Python and Machine Learning training at Afitech Corporate Limited, Bangalore.

Using machine learning models and algorithms for data analysis to make predictions by collecting and analyzing large amounts of data on certain populations.

Collected and cleaned large datasets for analysis.

Conducted statistical analysis and created visualizations to communicate findings.

Skills Used: Python libraries for data cleaning, analysis, and machine learning, including supervised and unsupervised learning, and regression.