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Data Analyst

Job not on LinkedIn

πŸ”₯ 1 hour ago

πŸ„ California – Remote

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πŸ‘¨β€πŸŽ“ Internship

🟑 Mid-level

🟠 Senior

πŸ“‰ Data Analyst

Where this job comes from

Posted on Sajix Inc's careers page, not through a job board

First seen by Remote Rocketship 1 hour ago

Still open on Sajix Inc's site. Checked 53 minutes ago.

Logo of Sajix Inc

Sajix Inc

51 - 200 employees

πŸ₯ Healthcare

πŸ€– Artificial Intelligence

🀝 B2B

Healthcare β€’ Artificial Intelligence β€’ B2B

Sajix Inc is an IT products and services company (founded 2006) that builds AI-driven software and delivers digital transformation and product engineering services. The company markets AI-Based Revenue Cycle Management (AI-RCM) and IntelliHealth OCR for healthcare providers, AI chatbots (NIVISH), and other automation and predictive-analytics capabilities (NLP, smart workflow optimization). Sajix also offers product engineering (DevOps, CI/CD, Kubernetes, microservices), digital-transformation consulting, small-hospital solutions, testing services, and a training program (__init__py). The firm serves enterprise/B2B clients across multiple countries and cites 35+ clients and 3. 5M+ managed records; headquarters listed in Pleasanton, California.

πŸ“‹ Description

β€’ Assist in data extraction, transformation, and loading (ETL) processes β€’ Perform exploratory data analysis β€’ Create visual reports β€’ Support internal teams with ad-hoc data requests and reporting β€’ Work closely with business and technology teams to understand data structures and reporting needs

🎯 Requirements

β€’ Bachelor's degree in Computer Science, Statistics, Mathematics, or related field β€’ Basic understanding of SQL β€’ Basic understanding of data visualization tools, including Excel, Power BI, and Tableau β€’ Analytical mindset and attention to detail

πŸ–οΈ Benefits

β€’ Employees can work remotely β€’ Structured, hands-on learning initiative β€’ Real-world experience through live projects β€’ Personalized mentorship β€’ Exposure to industry-relevant tools and practices β€’ Tiered training tracks

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