Generative AI Data Scientist Lead

S&C Global Network - AI - FS Banking & Capital-Consultant

Management Level: 07 - Manager

Location: Gurgaon/Bangalore/Mumbai

Must have skills: Python Programming, Machine Learning, Generative AI (GPT, DALL·E, GANs), Model Deployment, Cloud (AWS/Azure/GCP), Statistical Analysis, Data Science, API Development, Docker/Kubernetes

Good to have skills: PyTorch, TensorFlow, NLP, Computer Vision, Performance Optimization, Ethical AI Practices, Cloud Integrations, Open-Source Contributions

Experience: Minimum 6+ years of experience in Data Science, Machine Learning, or a related field with a focus on Generative AI models (LLMs) and leadership in AI solutions.

Educational Qualification: Master’s or Ph.D. in Computer Science, Data Science, Statistics, or related field.

Job Summary:

Accenture AI is seeking a talented and motivated Generative AI Data Scientist Lead to join our Banking team. The ideal candidate will leverage their expertise in Generative AI models, Machine Learning, Data Analysis and Cloud Platforms to drive and lead the development & delivery of the innovative solutions and enhance our AI capabilities. This role will involve collaborating with cross-functional teams to design, develop, and implement generative AI solutions that meet our business needs.

Roles & Responsibilities:

• Independent Project Leadership: Take ownership of end-to-end project delivery, from problem definition to solution deployment, with minimal supervision.
• Pod Leadership: Lead and mentor a small development pod (team) of data scientists and engineers, ensuring collaboration, innovation, and timely delivery of high-quality solutions.
• Model Development: Design, develop, and deploy state-of-the-art generative AI models (e.g., GPT, DALL·E, Stable Diffusion, GANs) for applications such as text generation, image synthesis, content creation, and more.
• Strategy Development: Provide strategic recommendations to clients on how to integrate generative AI into their workflows and business processes.
• Ethical AI Practices: Ensure responsible use of generative AI by addressing biases, ethical concerns, and compliance with regulations.
• Performance Evaluation: Develop evaluation metrics and methodologies to assess the performance of generative models and ensure they meet quality standards.
• Collaboration: Work closely with product managers, engineers, and other stakeholders to understand requirements and deliver impactful AI solutions.
• Research and Innovation: Stay up-to-date with the latest advancements in generative AI and machine learning, applying that knowledge to improve existing models and techniques.
• Mentorship: Guide and mentor junior team members, fostering a culture of innovation and continuous learning.