GenAI Business Analyst

December 12

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Data and Analytics • Automation • Application Modernization • Cloud Services • Artificial Intelligence

11 - 50 employees

🤖 Artificial Intelligence

⚡ Productivity

☁️ SaaS

Description

• Collaborate with stakeholders, including geneticists, data scientists, and software developers, to identify and define business requirements and objectives related to GenAI projects. • Act as a liaison between technical and non-technical teams, translating complex genetic concepts and requirements into clear, actionable tasks. • Participate in the planning, scoping, and prioritization of GenAI projects, ensuring alignment with organizational goals. • Develop detailed business requirements documents, use cases, and user stories to support the development of GenAI software and tools. • Work with data scientists and AI developers to ensure that AI models align with genetic research needs. • Facilitate the testing and validation of GenAI applications, ensuring that they meet specified requirements. • Analyze and interpret data to derive insights related to genetic research and AI application performance. • Collaborate on the development and documentation of standard operating procedures (SOPs) for GenAI processes and workflows. • Stay informed about advances in the field of AI and genetics to provide insights on potential improvements and new opportunities. • Mentor and guide junior GenAI Business Analysts and contribute to their professional development.

Requirements

• A minimum of 5 years of professional experience as a GenAI Business Analyst, with a strong focus on the intersection of AI and genetics. • Deep understanding of genetics and genomics, including GWAS and NGS data. • Proficiency in translating complex genetic concepts into actionable business requirements. • Strong analytical skills and proficiency in data analysis and interpretation. • Excellent communication and collaboration skills, with the ability to bridge technical and non-technical teams. • Strong problem-solving and critical thinking abilities. • Knowledge of AI and machine learning concepts. • Familiarity with data analysis and visualization tools (e.g., Python, R, Tableau). • Ability to work independently and in a team. • Strong project management and organizational skills. • Research publications or contributions to the field are a plus.