Data Scientist

KAUMUDI DEGEKAR GULBARGA

602-***-**** • ********@***.*** • linkedin.com/in/kgulbarg • github.com/kgulbarg • Norristown, PA, USA Applied ML and data professional combining statistical analysis, machine learning, and engineering to build robust decision-support systems.

EDUCATION

MS, Software Engineering (Thesis, ML focus) Jan 2024 – Dec 2025 Arizona State University, AZ, USA 4/4 GPA

B. Tech, Computer Science & Engineering Aug 2016 – May 2020 G. Narayanamma Institute of Technology & Science, Hyd, IN 3.3/4 GPA WORK EXPERIENCE

Software Engineer – Data Analytics, 7 Seas Entertainment Jun 2021 – Dec 2023

• Engineered reusable SQL pipelines aggregating gameplay, onboarding, and purchase events for analytics.

• Analyzed conversion funnels and user behavior; insights drove product changes improving conversion by 12

• Built Tableau dashboards for funnel, retention, and monetization analysis supporting data-driven decisions.

• Designed analytics instrumentation enabling reliable revenue reporting and behavioral tracking. Software Engineer – Full Stack, Micron Technology Jul 2020 – May 2021

• Built RESTful APIs and data integration pipelines for fabrication and quality analytics using Angular/JavaScript.

• Created Tableau dashboards surfacing production trends, anomalies, and quality metrics for engineering teams. RESEARCH & APPLIED ML PROJECTS

Classifying Cell-Cycle Phase from scHi-C Data with Statistical Features and INRs Fall 2025 International Conference on Information Systems (IADIS IS), 2026

• Developed novel approach combining statistical features and per-cell INR embeddings for cell-cycle classification.

• Achieved 89% accuracy via ensemble learning, preserving cell-level variability without data imputation. Siamese Network for Facial Recognition Spring 2025

• Implemented a Siamese model with VGG16 and contrastive loss for face-pair verification.

• Reached near-perfect accuracy on the AT&T dataset and analyzed model behavior in low-data settings. Scalable Implicit Full Waveform Inversion for Velocity Reconstruction Spring 2025

• Modeled velocity maps as continuous functions using implicit neural representations for high-res reconstruction.

• Trained and scaled FWI workflow on HPC cluster using Slurm batch jobs to process 100+ velocity samples. Smart Habitat Ranking for Urban Relocation Fall 2024

• Built a locality recommendation system using knowledge-graph modeling and Semantic Web tools.

• Combined multiple datasets into an ontology to compute habitat scores via graph-based reasoning. CO-CURRICULAR EXPERIENCE

Graduate Teaching Assistant, Arizona State University Jan 2025 – Dec 2025

• Held office hours, graded assignments, provided individual feedback and personalized support (SER 502). Apprenticeship Fellow, Apple Maps Hyd, IN Dec 2019 – Apr 2020

• Designed database schemas, validation, retrieval routines and SQL/JPA logic with expert mentorship. TECHNICAL SKILLS

Languages: Python, SQL, Java, C, Bash; SPARQL, RDF, OWL, SWRL ML & DS: Classification, Regression, Clustering; Feature Engineering, Model Evaluation; Pandas, NumPy, scikit-learn, statsmodels, PyTorch, Matplotlib, Seaborn, SHAP, NLP Tools: Tableau, MySQL, GraphDB, Jupyter, Linux, Slurm (HPC), Docker, AWS, Git

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