Data Analyst.
Machine Learning.
Intelligent Analytics Systems.
Transforming complex data into intelligent, scalable systems through analytics engineering, machine learning, generative AI, and real-time visualization.
A data portfolio built to feel like a high-end analytics platform.
This experience is positioned around more than dashboards. It combines analytics engineering, machine learning, synthetic data research, and production-style tooling into one cohesive technical identity.
Sri Satya Harsha Pola is a data analytics and machine learning professional with a Master’s in Data Science and hands-on experience across operational analytics, generative AI research, synthetic data modeling, and visualization-driven decision support.
His work spans Python, SQL, Spark, cloud platforms, interactive dashboards, and research-backed AI systems — giving him the ability to work across both exploratory analysis and scalable analytics implementation.
The result is a profile that feels stronger than a traditional data analyst title alone: part data engineer, part analyst, part ML practitioner, and highly effective at turning raw datasets into usable intelligence.
University of West Florida with strong coursework across ML, AI, deep learning, big data, and regression modeling.
Combines business-facing dashboard work with research in synthetic data, computer vision, and applied AI systems.
Comfortable across Python, R, SQL, Spark, Power BI, Tableau, Docker, cloud platforms, and ML frameworks.
Core strengths across analytics, ML, and scalable data workflows.
The portfolio layout uses premium cards to present his profile as a full analytics stack contributor rather than limiting him to one narrow lane.
Data Engineering & Pipelines
Building robust data flows with Python, SQL, ETL design, APIs, and scalable processing patterns for analytics-ready systems.
Machine Learning & Generative AI
Applying ML, deep learning, and synthetic data techniques to solve analytics, prediction, and privacy-preserving modeling challenges.
Visualization & Decision Support
Designing dashboards and visual analytics experiences that help stakeholders understand trends, KPIs, and operational risks in real time.
Cloud, Big Data & Deployment
Working across AWS, GCP, Azure ML, Spark, Docker, and workflow tooling to move analytics systems from experimentation to usable products.
Industry execution backed by strong research depth.
This section combines operational analytics experience with academic research work to show both practical delivery and advanced technical credibility.
Advanced data and AI projects presented like flagship analytics products.
These cards are designed to feel like premium product modules, emphasizing technical depth, measurable outcomes, and futuristic data storytelling.
Trade Insights Engine
Built a big-data trade policy analytics system using Spark, SQL, AWS, and machine learning to predict trade flows, logistics efficiency, and supply chain disruptions.
Synthetic Data Generation with CTGAN + Gaussian Copula
Designed and evaluated synthetic data generation pipelines for privacy-preserving analytics, validating statistical fidelity and downstream ML utility.
Supply Chain Optimization Engine
Combined synthetic data and machine learning to model shipping delays, supplier reliability, and disruption scenarios in an interactive analytics environment.
Real-time Fitness Monitoring
Created a real-time computer vision system for motion tracking, posture classification, and exercise feedback using MediaPipe-based pose estimation.
Academic credibility that strengthens the technical brand.
Most analytics portfolios stop at dashboards. This one includes research output and publications, which elevates the profile immediately for technical and advanced analytics roles.
Synthetic data, AI systems, and applied analytics research
The research section highlights a rare combination of publication-backed work in synthetic data, generative AI, computer vision, and cybersecurity-oriented machine learning. It adds serious depth to the overall portfolio identity.
Integrating Unsupervised and Supervised ML Models for Synthetic Data Analysis from VAE, GAN, and Variable Clustering (2024)
Real-time fitness monitoring with MediaPipe (2024)
Hybrid intelligence for DDoS defense: Combining generative AI, resampling, and ensemble methods (2025)
Generative AI: Comparing CTGAN and CTGAN with Gaussian Copula in Synthetic Data Generation (Conference / In Press)
A modern analytics stack grouped for clarity and visual polish.
The skills area is structured like a premium tooling dashboard, balancing analyst readability with technical depth.
Languages
Analytics & ML
Visualization
Cloud & Platforms
Let’s build analytics that move decisions faster.
Open to data analyst, analytics engineering, business intelligence, and machine learning-oriented opportunities.