Santhosh R.

Key Skills

Software

Other

Top Subject Matter

Breast cancer diagnosis (ML classification)

Enterprise workflow migration and LLM-based formula automation

Top Data Types

Text

Top Task Types

Diagnosis

Function Calling

Freelancer Overview

Data Science Intern - breast cancer diagnosis ML development. Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other and Microsoft Fabric. Education includes Bachelor of Technology, National Institute of Technology Srinagar (2025). AI-training focus includes data types such as Medical, DICOM, and Computer Code and labeling workflows including Diagnosis and Function Calling.

Labeling Experience

Software Engineer I - Alteryx to Databricks/Fabric migration with LLM automation

Function Calling

Migrated and automated analytics workflow logic to modern data platforms to enable reliable AI/data transformation pipelines. Converted Alteryx workflows into Databricks notebooks and Microsoft Fabric dataflows by extracting workflow logic, metadata, and formulas from .yxmd/.yxzp assets. Integrated LLM-based formula automation to translate expressions into platform-compatible transformations.• Automated workflow conversion using PySpark and Fabric APIs with notebook generation.• Extracted workflow logic, metadata, and formulas from Alteryx project files (.yxmd/.yxzp).• Integrated LLM-based automation to convert Alteryx expressions into Databricks/Fabric transformations.• Supported enterprise data modernization by ensuring migration correctness and reducing manual effort.

2025 - Present

Data Science Intern - breast cancer diagnosis ML development

OtherDiagnosis

Developed a breast-cancer related AI/ML approach intended to support accurate diagnosis. Used Logistic Regression and Random Forest to improve predictive accuracy and achieve 95+% performance. The work involved translating modeling outputs into operational steps for patient-support use.• Applied Logistic Regression and Random Forest for classification-style diagnostics.• Built and deployed a Python desktop application using a Tkinter interface.• Focused on breast cancer diagnosis enablement with reported 95+% accuracy.• Implemented 20+ processing operations to produce optimal results for breast cancer patients.

2024 - 2024

Education

Work History