M A RAHMAN

DATA & BUSINESS ANALYST

Predictive Modeling | Workflow Automation | Strategic Intelligence | ML Deployment | Advanced Analytics

FEATURED IMPACT METRICS

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

PROFESSIONAL EXPERIENCE

DATA ANALYST | PDA INFOTECH

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.

DATA SCIENCE INTERN | AI VARIANT

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.

BUSINESS ANALYST | AMAZON

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.

TECHNICAL PROJECTS

APPLE STOCK PRICE FORECASTING (TIME SERIES ANALYSIS)

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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BANKRUPTCY PREVENTION & RISK ASSESSMENT

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

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NLP SENTIMENT & CUSTOMER INTELLIGENCE

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

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SOLAR POWER PREDICTION & DEPLOYMENT

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

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ACADEMIC BACKGROUND

B. Tech in Mechanical Engineering
Jawaharlal Nehru Technological University (JNTU) Hyderabad
Classification: First Class 2015 – 2019

PROFESSIONAL CERTIFICATIONS

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