HELLO, MY NAME IS

Taraka Paruchuru

Data Scientist | SaaS | AI Engineer

TARAKA.PARUCHURU@GMAIL.COM​
+1(636)-333-6999​
About Me

Data Scientist and Gen AI Engineer with 8+years of Professional experience in SaaS and Data Operations.

I build and fine-tune AI models tailored to business needs.

With years of experience in Saas Operations and Analytics I have mastered the skills of understanding client requirements according to their business needs. I have worked with businesses from broad domainsWith years of experience in SaaS Operations and Analytics, I’ve developed a strong ability to understand client requirements and translate them into scalable, business-aligned solutions. I’ve worked with organizations across a variety of domains, delivering value through a mix of business intelligence dashboards, middleware integration, cloud migration, eCRF/database development, DevOps toolchains, SRE, and vulnerability management.
My career has been rooted in building and optimizing platforms that are now running live in production, and I bring a collaborative mindset to every project.

I’m focused on driving the next wave of SaaS transformation by embedding AI capabilities into traditional SaaS workflows—accelerating innovation and helping businesses adapt faster in a competitive landscape..

What I Do

Data Scientist

Data drives decisions, and as a data scientist, I ensure that insights are actionable and impactful. I specialize in analyzing complex datasets, building innovative models, and delivering clear, data-driven solutions

AI Agents

My Work in AI Agents focus on designing and deploying AI agents that simulate human-like conversations and automate complex business workflows. These agents are built to solve real-world problems in customer support, lead generation, and operational automation

MODEL BUILDING

My focus is on creating scalable and efficient machine learning systems that solve real-world problems, all while ensuring ethical and responsible AI practices.

PROMT ENGINEERING/FINE TUNING​

An effective prompt or fine-tuned model is what extracts the best performance and aligns outputs with user needs. I ensure that the prompts are precise and the fine-tuning process is optimized, creating innovative, adaptable, and efficient solutions for large language models while maintaining clarity, relevance, and ethical AI principles

Skills

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EDA
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Model Building
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Python
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Data Visualization
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DevOps
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Cloud Migration

My Experience

2019-2023

FIS Global

Lead Egineer/ System support Specialist

As part of FIS, a fintech giant providing solutions to major banks and merchant clients, I have responsibly led a team of 9 and had opportunity to work in various roles and responsibilities, as Middleware and DevOps Engineer, SRE, Cloud Migration, Vulnerability Management, and Implementation Engineer.I contributed to projects for prestigious FIS clients such as MasterCard, PayPal, Chevron, Intuit, and Starbucks.

2017-2019

Sears Holdings

Middleware and DevOps Engineer

Sears Holdings, a retail giant comprising Sears and Kmart stores, has a massive IT infrastructure for end-to-end retail operations. Also a part of the Middleware and DevOps team, I was responsible for cloud migration and developed CI/CD pipelines to meet the fast-paced demands of the dynamic retail market.

2015-2016

Novartis Health care

Clinical Database Developer

I was a part of Novartis Healthcare,Oncology Business Unit was delighted to work on the clinical trial development process as a CRF and Database Developer for multiple trials conducted globally, benefiting many patients.

Certifications

Oracle Cloud Infrastructure 2023 AI Certified Foundations Associate

CITI Data or Specimens Only Research

Microsoft Azure Fundamentals

Portfolio

Adv Data Analysis for Optimizing Automobile Insurance Claims and Premiums

The automobile insurance industry faces challenges in accurately predicting claim amounts and setting fair premiums due to the complexity of factors like customer demographics, vehicle types, and policy types by advanced data analysis techniques in R, such as Principal Component Analysis (PCA), regression models, and Correspondence Analysis and identified key factors influencing insurance claims, premiums, and policy choices to provide actionable insights for insurers to optimize premium structures and tailor policies based on risk profiles. Git
Sentiment-Driven Classification of Amazon Footwear Reviews (NLP)

This project aims to address the specific domain of footwear sold on Amazon, leading e-commerce platform, by performing sentiment analysis and opinion mining on customer reviews. The core objective is to develop a sophisticated analysis framework that can accurately classify customer reviews into three distinct sentiment categories: good, bad, and neutral. Git
Multicloud DevOps AI Retail Platform

AI-powered e-commerce platform built as part of the Multicloud DevOps AI Challenge. This project demonstrates how intelligent automation, scalable infrastructure, and cross-cloud integration can power modern applications.
Health Data Breaches

Health Data Breaches Visualization
Conduct a comprehensive data analysis leveraging the U.S. Department of Health and Human Services (HHS) Office for Civil Rights portal dataset, spanning October 2009 to September 2021. The aim was to unearth intricate patterns and insights pertaining to data security vulnerabilities prevalent in the healthcare sector, employing advanced data science methodologies and the final results were visualized in Tableau Public.
Diagnosis of Pediatric Appendicitis using AI (Adv ML)

In this Project we have leveraged advanced ML and AI techniques , specifically Multi-Layer Perceptron Random Forest (RF), Support Vector Machine (SVM), and XGBoost algorithms to enhance the diagnostic process for pediatric appendicitis, The results demonstrated that our models, specifically Random Forest and XGBoost, achieved significant improvements in diagnostic accuracy. Git
Object Detection in Monitoring Systems

This project focuses on enhancing object detection for surveillance systems by optimizing the YOLOv11 model. While the pre-trained YOLO model effectively detects common objects like people, bicycles, and cars,it struggled with detecting specialized items such as weapons. To address this, we annotated a dataset with weapon images using Roboflow, Trained and fine-tuned the YOLO model on a custom dataset Our approach
significantly improved detection performance, achieving higher accuracy,precision, and recall for weapon detection. Git
Agentic-AI-builds for business usecases (GUI based Framework)

This project repository showcases two AI-powered automation builds, designed to streamline lead generation and qualification using graphical, low-code tools.
Build 1: WhatsApp-Based AI Customer Support & Lead Generation Agent
Build 2: AI-Powered Lead Qualification Agent
Developed with n8n, Relevance AI, Agentive AI, OpenAir and OpenAI.
Git
Spotify Song Recommender System

The Goal of this Project is to Develop algorithms to measure text similarity between Spotify songs lyrics, where we will be vectorizing the text data (converting text into numerical vectors) and then calculating the similarity between these vectors to find the relevance for the recommendation systems and Implement content recommendation systems to suggest relevant Songs based on user’s and song type. Git