Data Scientist - Generative AI

As an AI/MLGen AI Developer, your role involves collaborating with business and data teams to define, design, and develop cutting-edge AI algorithms to solve real-world business problems. You will be responsible for analyzing structured and unstructured data from various sources in order to address specific questions and problems within different functional areas. Your key responsibilities include: - Communicating findings and results to non-technical stakeholders, providing actionable insights. - Optimizing and fine-tuning existing ML/AI models to enhance performance and accuracy. - Ensuring data privacy and security practices are maintained throughout the AI development process. - Developing/Supporting GenAI solutions for NLP involving LLM (Large Language Models) such as GPT, Flacon, and LLaMA, or for Computer Vision involving GANs, Diffusion models, etc. - Tracking data modeling and analytics tools like SQL, Python, and possessing good knowledge in advanced analytics, Machine learning, NLP, statistical, data mining, and text mining techniques. - Delivering industry best practices, creating code standards, presentations, and other relevant documents based on stakeholder requirements. - Working closely with the Client team to establish relationships with stakeholders, identify new opportunities, and areas of growth. - Providing Project, Client & Team Management, monitoring deliverable progress regularly, and ensuring timely issue resolution. - Brainstorming pain areas, identifying reasons for issues, diagnosing inefficiencies in current processes, and finding resolutions. - Offering technical direction to project team members across data modeling, data manipulation, visualization, predictive modeling, and reporting in projects across the Insurance Value Chain. - Facilitating client working sessions and leading recurring project status meetings. Your qualifications should include experience in using Python and hands-on experience in building models with machine learning frameworks or algorithms. Additionally, you should have a track record in data modeling, data analytics tools like SQL, Python, and a good understanding of advanced analytics, Machine learning, NLP, statistical, data mining, and text mining techniques. As an AI/MLGen AI Developer, your role involves collaborating with business and data teams to define, design, and develop cutting-edge AI algorithms to solve real-world business problems. You will be responsible for analyzing structured and unstructured data from various sources in order to address specific questions and problems within different functional areas. Your key responsibilities include: - Communicating findings and results to non-technical stakeholders, providing actionable insights. - Optimizing and fine-tuning existing ML/AI models to enhance performance and accuracy. - Ensuring data privacy and security practices are maintained throughout the AI development process. - Developing/Supporting GenAI solutions for NLP involving LLM (Large Language Models) such as GPT, Flacon, and LLaMA, or for Computer Vision involving GANs, Diffusion models, etc. - Tracking data modeling and analytics tools like SQL, Python, and possessing good knowledge in advanced analytics, Machine learning, NLP, statistical, data mining, and text mining techniques. - Delivering industry best practices, creating code standards, presentations, and other relevant documents based on stakeholder requirements. - Working closely with the Client team to establish relationships with stakeholders, identify new opportunities, and areas of growth. - Providing Project, Client & Team Management, monitoring deliverable progress regularly, and ensuring timely issue resolution. - Brainstorming pain areas, identifying reasons for issues, diagnosing inefficiencies in current processes, and finding resolutions. - Offering technical direction to project team members across data modeling, data manipulation, visualization, predictive modeling, and reporting in projects across the Insurance Value Chain. - Facilitating client working sessions and leading recurring project status meetings. Your qualifications should include experience in using Python and hands-on experience in building models with machine learning frameworks or algorithms. Additionally, you should have a track record in data modeling, data analytics tools like SQL, Python, and a good understanding of advanced analytics, Machine learning, NLP, statistical, data mining, and text mining techniques.