India - Hyderabad
ZignaAI is focused on delivering innovative solutions that transform healthcare payment operational processes. We empower payers, providers, and patients with AI-powered software solutions that drive transparency in healthcare payment services. Built-in intelligence-enabled machine learning algorithms deliver pre-billing payment accuracy solutions and avoid provider abrasion. We differ from traditional payment services solutions by resolving issues at the root by ensuring accurate payments, automating processes, with nudges delivered to billing coders. Our innovative and scalable solutions cover Medicaid, Medicare, and Commercial policies and deliver results in weeks.
This is a great opportunity for an aspiring clinical data analyst to join a healthcare analytics company and deliver meaningful outcomes that reduce the cost of care. Candidates will work closely with data science leadership, operations, and engineering to build intelligence in the system. This will involve constant learning and application to solve business problems. This position will require the candidate to be an out-of-the-box thinker, intellectually curious, and with great attention to detail. This role offers the potential to grow at Zigna in data science and gain strong domain knowledge. We are committed to developing and nurturing talent at ZignaAI.
We are a startup and expect each team member to wear multiple hats, take initiative, and spot and solve problems.
Our organization is seeking a detail-oriented Clinical Data Analyst & Auditing Specialist. This role primarily focuses on analyzing health data, performing ICD-10-CM, ICD-10-PCS, CPT, and HCPCS audits, as well as assisting in the training of an AI system for coding and auditing healthcare claims. The ideal candidate will hold a coding credential from AHIMA or AAPC, have completed coursework in medical terminology, anatomy, and physiology, and have extensive experience in DRG and/or APC audits, with a deep understanding of coding guidelines, payor guidelines, and contracts.