From Circuits to
Capital Markets.
My background blends engineering rigor with applied finance, shaping how I approach complex, data-driven problems. Trained in Electrical Engineering, I developed a strong systems mindset centered on structure, optimization, and analytical precision—skills refined through real-world engineering and data-centric roles.
Currently pursuing a Master’s in Applied Finance at SMU Singapore, I apply this discipline to financial modeling, quantitative analysis, and risk-aware decision making, bridging technical depth with capital-markets-focused thinking across investment and technology-driven roles.
3+
Years Experience
MA
Applied Finance
Madhvendra S. Palawat
Engineer & Finance Candidate
Quant Finance
Analysis & Strategy
Engineering
Full Stack & Systems
Data Systems
SQL, MongoDB, PostgreSQL, Redis
Professional Experience
Engineering systems → data-driven decision making → finance
Full Stack Engineer
Leading software and R&D initiatives for industrial instrumentation, with a focus on automation, data visibility, and operational efficiency.
Key Contributions
Launched the company website using LAMP stack, integrating automated lead capture and email workflows.
Designed an expense-tracking application that reduced fraudulent claims by 36%, improving cost control by 11% per field operation.
Built a real-time GPS tracking dashboard using Google Maps API to improve asset monitoring and operational decision-making.
Web Developer
Freelance
Graduate Engineer Trainee
Daimler Trucks
Research Intern
PRDC
Education
Singapore Management University
Focused on financial modeling, quantitative methods, and market analysis, with applications across trading strategies, risk, and technology-enabled finance. Emphasis on translating analytical frameworks into practical financial decision-making.
National Institute of Technology, Warangal
Built a strong analytical foundation through coursework in control systems, embedded systems, and signal processing, forming the mathematical and systems backbone of my quantitative and data-driven approach to finance.
Selected Projects
Decision-support systems and applied projects spanning capital allocation, risk analysis, and data-driven optimization.
Factor-Based Investment Decision System
Built an end-to-end investment decision system designed to support capital allocation across equity factors such as Value, Momentum, Quality, and Low Volatility. The system integrates historical data analysis, regime awareness, and explicit risk constraints to guide portfolio decisions. Rather than serving as a standalone backtest, the product is structured as a decision-support tool—answering what to allocate to, why the allocation is justified, and under what risks it holds. Emphasis is placed on interpretability, robustness, and risk-adjusted outcomes.
Expense Tracking & Financial Analytics
Designed and implemented a mobile-first expense analytics platform to monitor field-level spending and identify anomalies in real time. The system reduced fraudulent billing by 36% and delivered an ~11% cost saving per job, improving financial control and operational decision-making.
GPS-Based Asset Tracking Dashboard
Developed a real-time asset tracking dashboard by integrating Arduino-based GPS hardware with the Google Maps API. The system improved visibility into deployed assets and supported more informed operational and logistical decisions.
SMTrade Secure Platform Module
Built a full-stack MERN application for a trading-oriented platform, implementing JWT-based authentication, secure session handling, and role-based access control. Focused on system reliability, data security, and platform scalability.
Low-Cost Energy System Emulator
As a research intern, developed a photovoltaic system emulator using an 8086 microcontroller, linear regression, and lookup tables—achieving an ~85% cost reduction compared to standard solutions. Supported simulation and optimization of energy systems using Python and C#.