Siddhesh Randhir

Strategic and analytical Business Analyst with experience across manufacturing and marketing analytics, skilled in transforming complex business data into actionable insights that drive profitability and operational excellence. Adept at cost analysis, process optimization, resource management, and market research, with a proven record of delivering measurable business value. Proficient in SQL, Python, Power BI, and Tableau, with expertise in Agile and Scrum methodologies for collaborative, data-driven decision-making.
Passionate about bridging the gap between business strategy and data science to help global organizations make smarter, faster, and more sustainable decisions.

Python for Business Intelligence – Udemy

• Delivered measurable cost savings of 18% in manufacturing operations at Sandvik Coromant by analyzing procurement data, optimizing vendor strategy, and implementing data-driven decision frameworks.
• Boosted business profitability by 30% at BDigitaU Solutions through analytical marketing ROI models, strategic budget reallocation, and performance dashboards in Power BI.
• Enhanced client profitability by 45% in a real estate advisory project by building a customized financial tracking system that improved money, client, and resource management.
• Designed 4+ interactive dashboards using Power BI and Tableau, helping leadership teams visualize cost, production, and market data in real time for faster decision-making.
• Automated KPI tracking and reporting workflows, cutting manual reporting time by 90% while improving accuracy and business insight quality.
• Collaborated across 5+ departments (finance, operations, marketing, and supply chain) to align analytical insights with core business goals and strategic planning.
• Recognized for analytical excellence and problem-solving ability by senior stakeholders for transforming complex data into clear, actionable business recommendations.
• Developed SQL-based data models to identify loss trends, forecast demand, and evaluate marketing effectiveness — improving business forecasting accuracy by 25%.
• Contributed to product lifecycle analytics in the manufacturing domain, supporting cross-functional teams with insights that improved efficiency and reduced downtime.
• Mentored team members and interns in business analysis fundamentals, helping them bridge technical data tools with business problem-solving.