Shophia A.

Key Skills

Software

Other

Top Subject Matter

Medical claims / healthcare analytics

Medical claims / healthcare data engineering for AI pipelines

Top Data Types

Text

Top Task Types

Classification

Data Collection

Segmentation

Freelancer Overview

Machine Learning Intern, VIP Clients Customer Segmentation (VIP segmentation via RFM and clustering). Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other. Education includes Bachelor of Engineering, Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College (2025) and Higher Secondary School Certificate, T.S.T. Rajah Girls Mat. Hr. Sec. School (2021). AI-training focus includes data types such as Medical, DICOM, and Computer Code and labeling workflows including Classification, Data Collection, and Segment

Labeling Experience

Data Engineering Intern, Incremental Data Pipeline for Medical Claims using Azure and PySpark

OtherData Collection

Built an incremental data pipeline for medical claims data using Azure and PySpark, supporting creation of structured datasets used for AI training. Simulated hourly ingestion from CSV sources and converted them into Parquet for reliable downstream consumption. Implemented surrogate key generation and fact/dimension structuring to enable consistent labeling and model feature usage. • Used PySpark and Azure Data Lake Storage to process a 1M-record dataset • Triggered Azure Data Factory pipelines for CSV-to-Parquet conversion using MD5-hash based events • Structured transformed data into fact and dimension tables in the sink layer • Managed archival and purge policies to optimize storage while preserving data needed for training

2025 - 2025

Machine Learning Intern - Ignitus

TextSegmentation

As a Machine Learning Intern, she performed customer segmentation for VIP clients using large-scale transaction data. She handled data quality issues by cleaning outliers and missing values and rescaling features to improve model inputs. She applied unsupervised learning techniques and evaluated segmentation effectiveness using RFM analysis and clustering metrics. • Analyzed terabytes of customer transaction data from a UK-based e-commerce shop. • Cleaned outliers and missing values and performed data rescaling for better modeling. • Built unsupervised models including KMeans and Hierarchical Clustering. • Delivered VIP segmentation using RFM analysis with a reported silhouette score.

2024 - 2024

Machine Learning Intern, VIP Clients Customer Segmentation (VIP segmentation via RFM and clustering)

OtherClassification

Built an end-to-end incremental data pipeline for a 1M-record medical claims dataset using PySpark and Azure. Performed data preparation steps to structure records into analytic-ready fact and dimension tables for downstream modeling. Implemented ingestion simulations and ETL automation to maintain dataset consistency for label generation and model training workflows. • Simulated hourly CSV ingestion using MD5 hashes to trigger automated processing • Generated surrogate keys and organized data into fact/dimension schema in the sink layer • Configured lifecycle management to archive and purge files for storage optimization • Validated transformation outputs within the ETL pipeline using the configured storage and processing flow

2024 - 2024

Education

Work History