Predictive Modeling | Workflow Automation | Strategic Intelligence | ML Deployment | Advanced Analytics
| Metric | Achievement | Business Unit |
|---|---|---|
| Conversion Optimization | +25% Trial-to-Paid Conversion |
PDA Infotech |
| Operational Efficiency | 60% Reduction in Manual Effort |
PDA Infotech |
| Forecasting Precision | 85% Accuracy in Stock Trends |
AI Variant |
| Resource Optimization | 12% Reduction in Grid Costs |
AI Variant |
| Risk Reduction | 18% Decrease in False Positives |
AI Variant |
Dubai, UAE | Nov 2024 – Present
Overview: Driving data-led digital transformation by bridging the gap between raw data engineering and executive-level strategic decision-making.
Predictive Analytics & Revenue Growth
- Context: Addressed customer churn within subscription tiers by identifying behavioral patterns.
- Technical Implementation: Engineered and deployed machine learning frameworks (Classification & Regression) for Churn and CLV. Used segmentation analysis to identify high-value user personas.
- Business Impact: Directly generated AED 150K in quarterly revenue and improved retention rates by 18%.
BI Infrastructure & Executive Intelligence
- Context: Streamlined fragmented reporting into a unified source of truth for leadership.
- Technical Implementation: Architected high-performance Power BI data models using a Star Schema. Developed complex DAX measures and utilized Power Query for advanced data transformation.
- Business Impact: Delivered KPI-driven insights for C-suite and board-level reporting, reducing the time-to-insight for strategic pivots.
ETL Engineering & Workflow Automation
- Context: Targeted the elimination of high-latency manual reporting processes.
- Technical Implementation: Automated complex data workflows using Python (Pandas/NumPy) and Google BigQuery. Built SQL-based pipelines to ensure data integrity and governance.
- Business Impact: Slashed manual reporting effort by 60%, significantly increasing departmental bandwidth for high-level analysis.
Strategic Advisory & Cross-Functional Leadership
- Context: Bridged communication gaps between technical teams and financial stakeholders.
- Action: Partnered with Finance and Product teams to translate technical metrics into ROI-improving strategies.
- Business Impact: Guided long-term budgeting and resource allocation through data-backed forecasting and variance analysis.
Hyderabad, India | Jun 2023 – Aug 2024
Overview: Leveraged advanced machine learning and time-series modeling to solve complex problems across finance, energy, and e-commerce sectors.
Financial Market Intelligence & Time Series Forecasting
- Context: Collaborated with BlackRock to enhance decision-making in the NSE and international stock markets.
- Technical Implementation: Developed an ensemble of forecasting models including LSTM (Deep Learning), ARIMA, and Facebook Prophet. Performed rigorous model tuning to handle market volatility.
- Business Impact: Achieved a 15% reduction in RMSE and predicted stock trends with 85% accuracy, enabling more informed investment strategies and risk assessment.
Energy Analytics & Resource Optimization
- Context: Addressed the need for high-precision energy output forecasting for Cygni Energy Private Limited.
- Technical Implementation: Designed a predictive model for solar power generation using historical weather and generation data. Focused on minimizing variance in power supply predictions.
- Business Impact: Achieved an 88% accuracy rate, allowing the company to optimize grid resource allocation and reduce operational costs by 6%.
Risk Management & Bankruptcy Prevention
- Context: Developed a proactive financial health monitoring system to identify companies at risk of insolvency.
- Technical Implementation: Built and compared classification models using Logistic Regression, Random Forest, and Gradient Boosting. Focused on optimizing the precision-recall trade-off to minimize false positives.
- Business Impact: Reduced false positives by 18% and improved overall prediction accuracy by 22%, providing a vital early-warning system for financial stakeholders.
Automated Sentiment Intelligence & Market Research
- Context: Streamlined the collection and analysis of customer sentiment from e-commerce platforms.
- Technical Implementation: Engineered an automated data scraping framework using Selenium. Processed raw text data using NLTK and Scikit-learn, implementing extensive feature engineering and model tuning.
- Business Impact: Improved sentiment classification accuracy by 10%. Established KPIs for management that translated customer feedback into actionable marketing and product execution plans.
Hyderabad, India | May 2022 – May 2023
Overview: Focused on operational excellence and process transformation within Seller Flex and Transparency projects, utilizing automation to scale seller support capabilities.
Operational Automation & Process Engineering
- Context: Targeted high-latency manual workflows in seller support and daily catalog operations.
- Technical Implementation: Spearheaded the design and deployment of advanced Excel macros (VBA) to automate routine data entry and validation tasks.
- Business Impact: Achieved a 15% reduction in daily operational time and a 10% overall increase in process efficiency, significantly lowering manual intervention requirements.
Data Governance & ETL Optimization
- Context: Managed large-scale catalog data to ensure high standards of data integrity for the Seller Flex program.
- Technical Implementation: Utilized Hubble Query Language (HQL) and internal ETL frameworks to extract, transform, and load complex datasets. Optimized data flows to enhance catalog management precision.
- Business Impact: Enhanced reporting reliability and catalog accuracy, ensuring that high-volume data streams remained consistent and error-free for downstream stakeholders.
Standardization & Cross-Functional Strategy
- Context: Needed a scalable framework for the Transparency project to ensure global team alignment.
- Action: Authored and documented comprehensive Standard Operating Procedures (SOPs) and established KPIs to monitor cataloging health. Partnered with cross-functional teams to streamline seller support systems.
- Business Impact: Enabled seamless team adoption of new workflows and fostered a more efficient, data-driven environment for multi-departmental projects.
Objective: Developed a high-precision forecasting framework to predict Apple Inc. (AAPL) stock prices for a 30-day horizon, utilizing historical data from 2012 to 2019.
The Challenge: Stock market data is inherently non-stationary and noisy. The goal was to build a robust model that could minimize Root Mean Squared Error (RMSE) while capturing both short-term volatility and long-term seasonal trends.
Methodology & Technical Implementation:
- Data Engineering: Conducted stationarity testing (Augmented Dickey-Fuller) and implemented Min-Max Scaling. Created advanced features including Moving Averages (SMA/EMA) and lag variables.
- Multi-Model Ensemble Approach:
- Statistical Models: Implemented ARIMA and SARIMA to handle linear trends and seasonal fluctuations.
- Deep Learning (LSTM): Architected a Long Short-Term Memory network to capture non-linear, long-term dependencies in sequential price data.
- Prophet & Smoothing: Leveraged Facebook’s Prophet for trend components and Holt-Winters for triple-level seasonality.
- Evaluation: Benchmarked models using MSE, MAE, and RMSE to identify the most reliable predictor for 30-day trajectories.
Business Impact & Results:
- Precision: Achieved an 85% accuracy rate in trend prediction with a 15% reduction in RMSE compared to baseline models.
- Intelligence: Successfully generated a 30-day price forecast that identified key resistance and support levels for informed investment decision-making.
Links: GitHub Repository | Watch Project Demo
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Objective: Developed a proactive financial diagnostic tool to predict corporate insolvency based on qualitative and quantitative risk factors.
The Challenge: Financial distress is caused by non-linear interactions (e.g., management quality vs. financial flexibility). The goal was to maximize "Recall" (identifying all at-risk firms) while minimizing "False Positives" to prevent unnecessary credit denial.
Methodology & Technical Implementation:
- Feature Engineering: Analyzed six critical dimensions: Industrial Risk, Management Risk, Financial Flexibility, Credibility, Competitiveness, and Operating Risk.
- Algorithmic Multi-Modeling: * Logistic Regression: Established a high-interpretability baseline for risk-factor weighting.
- Ensemble Methods: Implemented Random Forest and Gradient Boosting to capture complex interactions.
- Support Vector Machines (SVM): Utilized RBF kernels to find optimal hyperplanes in high-dimensional feature space.
- Optimization: Conducted exhaustive searches via GridSearchCV and RandomizedSearchCV to tune parameters and prevent model overfitting.
- Deployment: Launched an interactive Streamlit dashboard allowing users to receive real-time bankruptcy probability predictions via risk-factor sliders.
Business Impact & Results:
- Enhanced Accuracy: The optimized Random Forest model delivered a 22% improvement in accuracy over standard models.
- Risk Mitigation: Reduced false positives by 18%, ensuring stable companies were not misclassified as distressed.
Links: GitHub Repository | Watch Project Demo
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Objective: Engineered an end-to-end NLP pipeline to scrape, process, and classify customer sentiment from Amazon reviews to drive product and marketing strategy.
The Challenge: Customer feedback is unstructured and contains domain-specific slang. The goal was to build a system that could scrape dynamic content and accurately categorize emotional nuances into Positive, Negative, and Neutral sentiments.
Methodology & Technical Implementation:
- Data Acquisition: Developed a multi-threaded web scraper using Selenium and newspaper3k to extract reviews across diverse product categories.
- Text Preprocessing: Implemented a rigorous NLTK pipeline including Tokenization, Lemmatization, and Part-of-Speech (POS) tagging to improve classification context.
- Modeling Architecture: * Rule-Based Baseline: Utilized VADER for initial lexicon-based scoring.
- Deep Learning (LSTM & GRU): Architected RNNs to capture sequential dependencies in long-form reviews.
- Deployment: Developed a Flask-based REST API and web interface for real-time sentiment predictions from raw text or URLs.
Business Impact & Results:
- Classification Excellence: Achieved an 87% overall accuracy and an 0.85 F1-Score, a 20% improvement over baseline metrics.
- Operational Scalability: Automated the feedback loop, reducing manual market research time from weeks to minutes.
Links: GitHub Repository | Watch Project Demo
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Objective: Developed a precision forecasting tool to estimate solar energy output based on real-time environmental and meteorological variables.
The Challenge: Solar generation is highly intermittent. For energy providers, inaccurate forecasts lead to inefficient grid management. The goal was to build a regressor that could handle non-linear dependencies like cloud cover, humidity, and temperature.
Methodology & Technical Implementation:
- EDA & Feature Engineering: Identified key drivers of power output and created rolling averages for wind speed and pressure to capture temporal weather shifts.
- Algorithmic Selection: Chose Gradient Boosting and XGBoost for their superior performance in modeling complex environmental interactions.
- Optimization: Tuned hyperparameters to minimize Mean Squared Error (MSE) and maximize R-squared (R²) values.
- Deployment: Built a Streamlit web application, allowing grid operators to input weather parameters and receive instantaneous power output predictions in kilowatts (kW).
Business Impact & Results:
- Forecasting Accuracy: Achieved an 88% accuracy rate, significantly outperforming traditional persistence-based methods.
- Operational Efficiency: Contributed to a 12% reduction in operational grid costs by enabling data-driven resource allocation.
Links: GitHub Repository | Watch Project Demo
- B. Tech in Mechanical Engineering
- Jawaharlal Nehru Technological University (JNTU) Hyderabad
- Classification: First Class | 2015 – 2019
- Master’s Program in Data Science | NASSCOM, Government of India
- Data Science Certification | ExcleR Solutions
- Machine Learning with Python | IBM Developer Skills Network
- Python 101 for Data Science | IBM Developer Skills Network