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Data Science with 2+ years in ML Pipelines & NLP
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Assessing your cultural and operational fit
Data Scientist with hands-on expertise in building end-to-end machine learning pipelines, NLP systems, and cloud-deployed models. Completed an advanced certification program from IITM Pravartak (HCL GUVI) and delivered real-world projects spanning financial transaction analysis, telecom support automation using RAG pipelines, and digital payments analytics. Proficient in Python, SQL, PyTorch, and AWS — with a strong foundation in transforming complex datasets into actionable business insights.
Annamalai University, Chidambaram
MBA · International Business
August 1, 2018 – June 30, 2020
Sriguru Institute of Technology, Coimbatore
BE · Electrical and Electronics Engineering
August 1, 2013 – June 30, 2017
Customer Workflow Coordinator
Customer Workflow Coordinator
June 1, 2022 – September 1, 2023
Bengaluru, Karnataka, India
ROGII – Wellbore Geology Prediction
May 1, 2026 – June 1, 2026
Objective: Building a predictive model to automate wellbore geology interpretation, contributing to drilling operations automation in the oil and gas industry. Model Development: Applying classification and regression techniques on subsurface geological data to predict lithology and rock properties from well log measurements. Feature Engineering: Preprocessing and engineering features from multi-dimensional sensor data including gamma ray, resistivity, and neutron porosity logs.
Automated Telecom Support RAG System
March 1, 2026 – June 1, 2026
RAG Pipeline Architecture: Designed and deployed a Retrieval-Augmented Generation (RAG) pipeline to simulate automated, context-aware customer support for the telecom sector. Synthetic Data Engineering: Generated, ingested, and vectorized synthetic telecom policy documents to build a robust foundational knowledge base for the AI system. NLP Classification: Implemented a rule-based NLP classifier using LangChain to accurately categorize and route simulated network and billing support tickets.
View ProjectFinancial Transaction Data Engineering & Analysis
February 1, 2026 – March 1, 2026
Data Extraction Pipeline: Engineered a custom Python script to systematically navigate complex state-level directory paths and extract large-scale financial transaction data from JSON files. Data Transformation: Utilized Pandas to parse nested data structures and transform raw extraction outputs into clean, structured DataFrames. Exploratory Data Analysis: Conducted comprehensive EDA to uncover actionable trends in the Indian digital payments and financial services market.
View ProjectAI & Data Science Internship
February 1, 2025 – March 1, 2025
Completed an intensive program covering Artificial Intelligence, Machine Learning, and Data Analytics — applying EDA, classification, regression, and foundational AI concepts to real datasets.
IITM Pravartak Certified Advanced Programming Professional & Master Data Science
HCL GUVI
December 1, 2025 – Present
Cultural Fit Analysis
The candidate's project diversity, including work in telecom, oil & gas, and financial services, suggests an ability to adapt to different industry contexts. The mention of 'Agile Methodologies' in a project indicates familiarity with collaborative development practices. However, the career transition from a Customer Workflow Coordinator to a Data Scientist, while demonstrating initiative, might require additional validation of their practical experience in a dedicated data science team environment. The 'AI & Data Science Internship' and 'IITM Pravartak Certified Advanced Programming Professional & Master Data Science' certification indicate a proactive approach to skill development, aligning with a growth-oriented culture.
Soft Skills & Operational Fit
The candidate's previous role as a Customer Workflow Coordinator highlights strong project coordination, cross-functional communication, and process optimization skills. These are valuable for a Data Scientist role, especially in translating business requirements into technical solutions and managing project timelines. The experience in CRM and database management also suggests an organized approach to data handling.