F

Quantitative Rigor. Product Craft. 

+Real-World Projects
+Days of Coding
+Years of Practice
Felix

Core Mission

To build performant systems and predictive analytics architectures that transform raw data streams into high-impact operational tools.

Professional Journey

My entry into technology began in Computer Science & Engineering, where I fell in love with algorithmic structure and data organization. Over time, I discovered that software is only half the puzzle-the real value lies in the data flowing through it.

This realization led me to focus on Data Science and quantitative analytics. Today, I build systems that are both computationally robust and capable of generating actionable intelligence.

Problem Solving

I treat code and modeling with the same logical rigor. When building databases, pipelines, or ML classifiers, I design defensively and verify empirical metrics (precision, recall, latency) at every iteration. I avoid quick hacks in favor of clean, structural code.

Technical Philosophy

Excellent engineering requires empathy. Whether it is a back-end transaction cache or an analytical visualizer, my background in design allows me to translate complex back-end architectures into clean, intuitive interfaces that recruiters and developers enjoy using.

Growth & Motivation

I am driven by structural efficiency-getting a dataset cleanly preprocessed or refactoring a complex codebase into elegant services.

Currently, I am expanding my knowledge in advanced statistical forecasting and real-time streaming architectures to further strengthen my full-stack capabilities.

Capabilities & Systems

Ecosystem

Analytical Engine & ML Pipeline

Processing → Modeling
PythonNumPyPandasScikit-LearnXGBoost
System Relationship

Operates on raw datasets drawn from database layers. Scripts clean anomalies, execute feature scaling, and pass optimized data arrays to predictive algorithms for multi-class classification and forecasting.

Ecosystem

Data Pipelines & Schemas

Ingestion → Integration
SQLMySQLSQLitePostgreSQL
System Relationship

Acts as the structural repository. Implements indexing, schemas, and optimized query routines to deliver datasets cleanly to preprocessing frameworks or transactional microservices.

Ecosystem

Business Intelligence & Analytics

Aggregation → Visualization
Power BIDAXData ModelingExcel
System Relationship

Translates SQL metrics and Python model outputs into interactive executive reports. Employs relational modeling and custom DAX calculations to isolate operational risks for decision-makers.

Ecosystem

Full-Stack Application Delivery

Interface ← API Integration
JavaScriptReactJSNextJSNodeJSExpressJSTailwind CSS
System Relationship

Engineers the client-facing application layers. Uses server-side caching and API gateways to bind databases and machine learning endpoints to clean, responsive interfaces.

Professional Journey

Case Study

Fraud Analyst

@CESOctober 2025 – May 2026 | Chennai, TN
95%+ Review AccuracyImpact & Highlight
Tools Stack
SQLFraud Detection SystemsRisk ClassifiersData AuditingPattern AnalysisExcelInternal Tools

The Problem

High-volume transactional flows presented complex vectors for transaction fraud and financial leakage. Detecting anomalies and verifying patterns required analytical logic, risk assessment, and precise database audits.

Responsibilities

  • Conducted daily audits of transaction datasets using SQL query logic to identify anomalous behavior.
  • Designed and validated structured rule-filters that flagged high-risk merchant accounts.
  • Investigated suspicious financial transactions and investigated fraud patterns.

Impact

  • Maintained high review accuracy while investigating suspicious financial transactions.
  • Mitigated financial liabilities by delivering high-precision risk metrics and predictive reports to operations.
  • Provided actionable insights to operations, leading to data-driven security policies.

Lessons Learned

I learned that data auditing requires absolute precision and logical rigor. Speed is useless without systematic verification, especially in high-risk financial datasets.
Case Study

Data Science Intern

@VCodezFeb 2025 - July 2025 | Chennai, TN
10+ Datasets Cleaned & VisualizedImpact & Highlight
Tools Stack
PythonPandasScikit-LearnXGBoostSQLGitPower BI

The Problem

Raw logs and user engagement data sat in disparate siloed formats, limiting the capacity of analytics to identify retention curves or predict customer attrition triggers.

Responsibilities

  • Built and optimized ETL and feature-extraction pipelines in Python.
  • Conducted Exploratory Data Analysis (EDA) to map active retention metrics.
  • Trained and fine-tuned gradient-boosted classification models (XGBoost) for user profiling.

Impact

  • Provided the data infrastructure and preprocessed records that powered downstream stakeholder dashboards.
  • Reduced manual data retrieval time for downstream analytics teams by hours.

Lessons Learned

Building machine learning models is only a fraction of the challenge; feature engineering, data hygiene, and translating quantitative metrics into actionable business context are where true values are made.

Education

  • Bachelor of Science in Computer Science

    2021-2025 | Tagore Engineering College(AU)

    Coursework highlights: Data Structures and Algorithms, Computer Systems Engineering, Data Science.

  • Online Coursework

    2022-2025 | NxtWave Disruptive Technolgies

    Completed coursework in advanced topics including Web Application Development, Full Stack Engineering, MERN Stack Technologies (SQLite, Express.js, React.js, Node.js).