Faridabad, Haryana, India
I am Sumit Bhardwaj, an innovative and detail-oriented problem solver with a strong foundation in Python, Data Structures & Algorithms, Machine Learning, and Deep Learning, With hands-on experience across data analysis, algorithmic trading, financial modeling, and AI-driven applications, I thrive at the intersection of mathematics, programming, and market dynamics, continuously challenging myself to push beyond conventional approaches. My journey began with mastering Python, NumPy, Pandas, and advanced libraries, which allowed me to design efficient data pipelines and handle complex datasets. Over time, I specialized in building end-to-end ML systems—from data preprocessing, feature engineering, and model training to optimization, evaluation, and deployment. My exposure to classification, regression, deep learning architectures like LSTMs and GRUs, and ensemble methods has provided me with a well-rounded toolkit to solve diverse real-world problems. In the domain of algorithmic trading, I’ve applied ML and DL techniques to identify hidden patterns, forecast price movements, and design trading strategies rooted in momentum, volatility, and trend-based indicators. I developed an OOP-based trading framework encompassing data loading, technical indicator generation, signal design, portfolio risk management, and backtesting. This practical approach not only sharpened my coding and analytical skills but also enhanced my ability to balance innovation with discipline—a mindset essential for both trading and data science. With a strong command over mathematics, probability, and statistical reasoning, I approach problem-solving with a logical, skeptical, and questioning mindset—always testing assumptions, validating outcomes, and refining methods. My curiosity fuels me to learn continuously, while my practical orientation ensures results are not just theoretical but optimized for real-world deployment. As I look ahead, my career aspiration is to become a Quantitative Researcher and Strategy Developer, leveraging ML, AI, and advanced analytics to design intelligent systems for high-frequency and intraday trading. I am equally motivated to contribute to broader AI applications, where innovation, efficiency, and scalability define success.
Das Organizer is a company specializing in spiritual trip planning and execution. I contributed as a Data Analyst, focusing on identifying profitable opportunities, optimizing offerings, and supporting data-driven decision-making. My role was part-time and occasional, but it allowed me to directly engage with real-world business problems and apply analytical thinking to strategic planning. I worked on evaluating different trip options by considering cost structures, demand patterns, and profitability metrics. This required analyzing which destinations and trip packages were more beneficial for the company, and recommending those that aligned with long-term business goals. Additionally, I studied customer behavior and segmentation, identifying target groups most likely to benefit from and engage with the company’s services. This helped in improving marketing effectiveness and ensuring that the right offerings reached the right audience. Another key responsibility was to suggest resource allocation strategies—how budgets, manpower, and schedules could be optimized to maximize returns while minimizing risks. In doing so, I practiced quantitative reasoning and predictive thinking, aligning well with my academic and project experience in AI, Machine Learning, and Quantitative Analysis. This experience strengthened my ability to translate raw data and observations into actionable business strategies, which is highly relevant to fields like Quantitative Finance and Machine Learning, where the ultimate goal is not just building models, but also ensuring they deliver value in decision-making. It gave me a practical perspective on how analytical insights drive real business growth, bridging the gap between technical skills and strategic impact.