P
Portfolio
Hyderabad, India

Sai Dushyanth Pandiri

Application Engineer

Data Engineering • Automation • Machine Learning

Application Engineer specializing in enterprise data integration, workflow automation, and applied machine learning. Currently leading end-to-end implementation of Strada Exchange Platform for global clients across 10+ countries, managing data pipelines.

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Sai Dushyanth Pandiri

Sai Dushyanth Pandiri

Application Engineer

Hyderabad, India

About Me

My Journey

I'm an Application Engineer passionate about transforming data challenges into intelligent solutions. My expertise lies in building enterprise-scale data integrations, implementing ML-driven automation, and managing robust data pipelines that power critical business operations.

Currently at Strada, I lead end-to-end implementations of the Exchange Platform for global clients like Philip Morris International, Syensqo, and Sedgwick, deploying across 10+ countries.

My work spans the full spectrum of modern data engineering - from designing complex HRIS-to-Payroll data mappings to building ML models that achieve 96.2% accuracy in classification tasks. I'm driven by the challenge of managing robust data pipelines that power critical business operations.

Key Achievements

Led implementations across 10+ countries for Fortune 500 clients
Managed data pipelines for enterprise deployments
Saved significant reporting hours through automation tools

Education

B.Tech in Computer Science and Engineering

VNR VJIET

Hyderabad, India • 2020 - 2024

CGPA: 8.3/10

Focus Areas

Data Integration

Enterprise-scale HRIS to Payroll data flows

ML Solutions

Building production-ready ML systems

Workflow Automation

Power Automate & n8n implementations

Work Experience

Application Engineer

Strada
Jul 2024 – PresentHyderabad, India

Leading end-to-end data integration and implementation of Strada Exchange Platform for global clients including Philip Morris International, Syensqo, and Sedgwick. Deploying enterprise-scale solutions across 10+ countries with a focus on data integrity and system reliability.

Led end-to-end data integration and implementation of Strada Exchange Platform for global clients (Philip Morris International, Syensqo, Sedgwick), deploying across 10+ countries
Designed complex data mappings and transformation rules for HRIS → Payroll flows (e.g., Workday → SAP)
Managed and monitored live pipelines supporting 2,000+ users per implementation, ensuring availability and data integrity
Debugged data flow issues using Azure Logs and Kubernetes
Created bug reports in Azure DevOps for data discrepancies and enhancements
Built workflow integrations in Power Automate
Built a Dynamic Data Validation & Reporting Tool in Python (exe), saving significant reporting hours for the team
SAPAzure Data FactoryAzure SQLKubernetesAzure DevOpsPower AutomatePython

Data Analyst Intern

Teknopoint/Dept
Jun 2023 – Aug 2023Remote

Worked on data analytics projects using Adobe Analytical Workspace to uncover user behavior patterns and business insights. Focused on data processing, structuring, and visualization to improve reporting accuracy.

Used Adobe Analytical Workspace to uncover trends and user behavior patterns
Processed and structured raw data for business insights
Improved reporting accuracy and data visualization with stakeholders
Collaborated with cross-functional teams to deliver data-driven recommendations
Adobe Analytical WorkspaceData AnalysisData VisualizationSQL

Research Intern

VNR VJIET
Jul 2022 – Oct 2022Hyderabad, India

Conducted research on exoplanet detection using machine learning techniques. Main author of a research paper that combines CNN-LSTM hybrid models with synthetic oversampling to improve detection accuracy.

Main author: 'Augmenting Exoplanet Detection: A Hybrid Model Integrating Machine Learning and Synthetic Oversampling' (under review)
Performed preprocessing + FFT transformations on Kepler light curves
Handled class imbalance using SMOTE (Synthetic Minority Over-sampling Technique)
Built CNN-LSTM hybrid model achieving 96.2% detection accuracy
Presented findings to research team and academic advisors
PythonTensorFlowKerasSMOTECNNLSTMSignal Processing

Technical Skills

Cloud & Data Tools

Azure Data FactoryAzure SQLKubernetesAzure DevOps

System Integration

HRIS → SAP Payroll Data IntegrationData Mapping & TransformationEnterprise Data Pipeline Monitoring

Automation

Power Automaten8n

Programming

PythonSQL

Data Science

Data Processing & AnalysisFeature EngineeringStatistical Data AnalysisMachine Learning

Core Competencies

Problem SolvingLeadershipTeam ManagementPublic SpeakingDecision Making

Featured Projects

Click "More Details" to view complete project information

Atmospheric Data Analysis & Humidity Prediction
Machine Learning2023

Atmospheric Data Analysis & Humidity Prediction

ML-powered humidity prediction system with real-time monitoring and automated AC control

PythonScikit-learnStreamlit+4
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Key Highlights

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Processed real-time atmospheric gas concentration datasets using regression-based ML models
Implemented model training and evaluation with cross-validation techniques for improved robustness
Developed interactive Streamlit web application for real-time humidity monitoring and visualization
Integrated automated control logic enabling Air Conditioner adjustments based on predicted humidity levels

Technologies:

PythonScikit-learnStreamlitPandasNumPyMachine Learning
Meteoroid Impact Prediction & Classification
Data Science2023

Meteoroid Impact Prediction & Classification

Data science project for predicting meteorite landing zones and classifying meteorite composition

PythonScikit-learnPandas+4
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Key Highlights

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Applied statistical modeling and spatial data analysis to estimate landing zones of chondritic meteoroids
Collected and preprocessed astronomical datasets with data cleaning, outlier removal, and feature engineering
Implemented supervised ML classification models to identify meteorite composition and potential origins
Developed and deployed a public-facing web platform presenting meteoroid data insights and visualizations

Technologies:

PythonScikit-learnPandasSpatial AnalysisStatistical ModelingData Visualization
Exoplanet Detection using Hybrid Deep Learning
Deep Learning2023

Exoplanet Detection using Hybrid Deep Learning

CNN-LSTM hybrid model for detecting exoplanets from Kepler telescope data with 96.2% accuracy

PythonTensorFlowKeras+6
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Key Highlights

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Designed CNN-LSTM hybrid deep learning model achieving 96.2% accuracy and 0.98 ROC-AUC on Kepler data
Applied SMOTE to address severe class imbalance (99:1 ratio), improving model generalization
Integrated Fast Fourier Transform (FFT) for frequency-domain feature extraction and signal interpretation
Automated preprocessing, normalization, and visualization of 5K+ stellar flux time-series samples

Technologies:

PythonTensorFlowKerasScikit-learnPandasNumPy

Get In Touch

Phone

+91 9949309156

Location

Hyderabad, India

Let's Work Together

Have a project in mind? Let's discuss how I can help.

P
Sai Dushyanth Pandiri

Application Engineer passionate about transforming data challenges into intelligent solutions.

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