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Data Science with 1+ years in Predictive Modeling & ML Pipelines
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Data Scientist and Software Engineer with a published research specialization in predictive modeling, high-dimensional statistical analysis, and production-grade ML pipeline design. Conference presenter at NEleX-2026 with a peer-reviewed machine learning publication. Proven track record of engineering architectures achieving a 93% AUC-ROC, deploying high-throughput model endpoints handling 1,000+ daily inference requests, and optimizing data vectorization for complex workflows. Expert Python programmer combining deep algorithmic knowledge with robust deployment strategies for high-scale consumer applications.
Vellore Institute of Technology (VIT)
M.Tech Integrated · Software Engineering
August 1, 2022 – May 1, 2026
Andhra Pradesh State Board
Senior Secondary School
N/A – Present
Andhra Pradesh State Board
Secondary School
N/A – Present
Open-Source Portfolio
Independent Data Scientist
January 1, 2025 – March 1, 2025
India
VIT Vellore
Lead Data Engineer & Researcher
August 1, 2024 – December 1, 2024
Vellore, Tamil Nadu, India
Open-Source Portfolio
Data Pipeline Engineer
March 1, 2024 – June 1, 2024
India
Behavioral Predictive Analytics & Customer Churn Engine
January 1, 2025 – March 1, 2025
Engineered a robust statistical predictive model utilizing 15+ complex user behavioral feature vectors to isolate churn risk indicators, achieving an 89% validation accuracy and a 0.91 F1-score. Conducted extensive data preprocessing, missing data imputation, and synthetic oversampling to alleviate class imbalances, ensuring un-biased model classification metrics. Wrapped the model pipeline into a high-throughput, low-latency microservice endpoint via FastAPI, reliably serving 1,000+ daily inferencing evaluations with sub-200ms processing times on Azure Cloud.
High-Dimensional Multi-Omics Predictive Framework (Published Research)
August 1, 2024 – December 1, 2024
Developed an end-to-end predictive classification pipeline processing over 100K+ high-dimensional data points, achieving a 93% AUC-ROC and 87% Micro-F1 Score via an optimized XGBoost and Random Forest ensemble strategy. Implemented statistical feature selection techniques and variance thresholds to execute a 40% dimensionality reduction, preserving model variance while significantly shrinking training convergence time. Productionalized the predictive architecture by deploying the ensemble model as a serverless microservice via AWS Lambda, minimizing cold-start latency to under 1 second and reducing cloud computational infrastructure costs by 35%.
Vectorized Analytics Pipeline & Public Health Engine
March 1, 2024 – June 1, 2024
Architected an automated ETL data orchestration engine in Python and SQL to ingest, clean, and map 100K+ records, using automated continuous integration jobs to ensure a 99% structural data integrity rating. Engineered 7 distinct interactive data exploration platforms utilizing Plotly; accelerated data manipulation runtimes by 60% (reducing latency from 8s to 2s) by structuring vectorized array operations over scalar loops.
SQL Gold Level Certification
HackerRank
June 1, 2026 – Present
Data Structures & Algorithms in Python Certification
GeeksforGeeks
June 1, 2026 – Present
Microsoft Azure Data Fundamentals (DP-900)
Microsoft
June 1, 2026 – Present
Cultural Fit Analysis
The candidate demonstrates a strong cultural fit for a data science role, particularly one that values innovation, practical application, and continuous improvement. Their academic research, personal projects, and certifications showcase a broad interest and commitment to the field. The diversity of projects, from academic research to open-source contributions, indicates adaptability and a willingness to tackle different problem domains. The focus on optimizing performance and cost aligns with business-oriented values.
Soft Skills & Operational Fit
The candidate's project descriptions highlight a proactive and results-oriented approach, focusing on optimizing performance, reducing costs, and ensuring high data integrity. Their involvement in published research and open-source projects suggests a strong drive for continuous learning and contribution. The emphasis on TDD and CI/CD indicates an understanding of robust software development practices, which is crucial for operational fit in a senior role.