Data Scientist - Generative AI & Advance Analytics (8-12 yrs)

Job Title : Data Scientist (Generative AI & Advanced Analytics)

About HGS Digital :

At HGS Digital, we are at the forefront of digital transformation, delivering innovative AI-driven solutions that create business value. Our 500+ strong team of experts in strategy, data science, software engineering, and creative solutions is dedicated to driving impact at scale.

Job Summary :

As a Generative AI Data Scientist, you will focus on designing and deploying state-of-the-art generative models (such as Stable Diffusion, GPT, and GANs) and integrating them with Retrieval-Augmented Generation (RAG) techniques. This role combines advanced AI with traditional statistical methods, enabling cutting-edge business solutions.

Roles & Responsibilities :

- Model Development : Build and fine-tune generative models (Stable Diffusion, GANs, transformers) for text, image, and multimodal data generation.

- RAG Implementation : Implement RAG-based approaches to enhance model performance by incorporating external knowledge into generative processes.

- Advanced Analytics : Integrate traditional machine learning and statistical methods for model optimization and evaluation.

- Cloud & Deployment : Scale and deploy models using cloud platforms (AWS, Azure, GCP) and manage integration with APIs.

- Collaboration & Communication : Work with stakeholders to define business objectives and communicate findings through clear, actionable insights.

Key Qualifications & Skills :

- Education : Bachelor's or Masters in Mathematics, Computer Science, Data Science, or a related field.

- Experience : Hands-on experience with Generative AI (e.g., Stable Diffusion, GPT, GANs), including working with RAG models.

Technical Skills :

- Proficiency in Python, R, SQL, and cloud platforms (AWS, GCP, Azure).

- Familiarity with generative models (Stable Diffusion, GPT, GANs, VQ-VAE) and RAG techniques.

- Strong foundation in traditional ML algorithms (regression, classification, clustering).

- Experience with MLOps and model deployment pipelines.

Tools : Experience with model training frameworks like Hugging Face, TensorFlow, PyTorch, and tools for text-to-image and multimodal AI applications.

Preferred Qualifications :

- RAG : Practical experience with Retrieval-Augmented Generation for improving model accuracy and contextual understanding.

- Multimodal AI : Expertise in multimodal model development (e.g., combining text, image, and video generation).

- Industry Experience : Familiarity with business applications of generative AI in domains like pharmaceuticals, media, or e-commerce.

- Additional Skills : Knowledge in advanced statistical techniques, NLP, and data integration.