Aashutosh Thakur

I am an MBA Finance student with a passion for numbers and technology, possessing expertise in Financial Planning and Analysis (FP&A), investment analysis, and financial modeling. My technical proficiency spans Python, SQL, Excel, and Tableau, with a strong focus on applying machine learning to financial data.

I am skilled in foundational machine learning models such as linear and logistic regression for making predictions and classifying data based on historical patterns. My capabilities also extend to clustering techniques like k-means and hierarchical clustering for customer segmentation and pattern recognition. I understand how decision trees work, including concepts like entropy and information gain, and can build and evaluate their predictive performance. Furthermore, I have experience with cloud-based machine learning tools like Microsoft Azure Machine Learning Studio for building, deploying, and integrating predictive models for real-time applications.

My inquisitive mindset compels me to fetch and convert data into information that leads to well-informed decision-making. Aside from the code and spreadsheets, I believe in personal focus and discipline—traits that I have learned from training in Muay Thai. It has taught me resilience, attention to detail, and the value of ongoing improvement, both professionally and personally."

• Financial Analyst Certification (365 Financial Analyst )
• Investment Analyst Certification (365 Financial Analyst)
• Data Scientist Certification (365 Financial Analyst)
• Data Analysis for Managers (Upgrad)
• Oracle (SQL, PL/SQL), MySQL, MS Access (D'souza Computer Education)

•Comprehensive Biotech Financial Modeling & Strategic Analysis (University Project):
As part of an MBA Finance curriculum, I put together a thorough Excel-based financial model for a biotech firm. This involved digging deep into a new business model and pulling in detailed market analysis, even using SEER data for common cancer types like Lung, Bladder, and Colon. The model covered everything from P&L projections and revenue/COGS breakdowns to detailed R&D and SG&A expenses, all while examining market shifts like erosion and growth. I also built in dynamic charts to make the projections easy to visualize, helping us truly understand segment-specific growth potential and overall financial health for better decision-making.

•Gross Margin Sensitivity & Pricing Strategy Analysis (University Project):
I took on a detailed analysis of gross margin impacts, specifically looking at how things would play out if a 3.5% jump in Cost of Goods Sold (COGS) happened alongside a 3.1% price drop. This meant carefully evaluating how changes in input costs and pricing strategies directly hit our gross margins. All those insights then got transformed into a sharp PowerPoint presentation, clearly laying out the complex financial implications for discussion in class.

•FP&A Dashboard Automation for Real-time Insights (Zomato):
Working hand-in-hand with the Business Intelligence team, I helped design and automate crucial Power BI dashboards. These tools gave us a real-time pulse on key financial metrics like Customer Acquisition Cost (CAC), Gross Merchandise Value (GMV), and contribution margin. The result? Much more efficient financial reporting and faster decisions for our executives.

•Cost Optimization & Enhanced Forecasting (Reliance Retail):
I spearheaded a project focused on dissecting spending trends across our vast retail operations. By building sophisticated Excel models, I was able to pinpoint inefficiencies, which directly led to identifying over ₹16,50,000 (roughly $20,000) in potential cost savings. On top of that, this initiative boosted our overall forecasting accuracy by a solid 15%.

•Data-Driven Promotional Campaign ROI Analysis (Zomato):
My work involved deep-dive financial modeling, combined with SQL-powered cohort analysis, to really understand the Return on Investment (ROI) of our promotional campaigns in Tier-1 cities. This analytical approach brought much-needed clarity to our margins and contributed to a 10% reduction in delivery inefficiencies.

•Quantitative Financial Market Strategy Development (Trading Wolves - Personal/Club Project):
I developed and put into practice financial models that tapped into key technical indicators (like RSI, DMI, and Moving Averages) to predict market movements. This systematic way of looking at data sharpened our trade decision accuracy by 30% and helped improve our simulated ROI by 12%, clearly showcasing a practical grasp of quantitative financial analysis.