Senior Data Scientist

About the Role:

Generative AI

This is a hands-on, highly collaborative role for someone who thrives at the intersection of data science, AI research, and engineering.

Key Responsibilities:

• Design, develop, and fine-tune Generative AI models for tasks such as text generation, summarization, code generation, image synthesis, and more.
• Leverage large language models (LLMs) (e.g., GPT, LLaMA, Claude, Mistral) to solve business problems and enhance user experiences.
• Collaborate with cross-functional teams including product, engineering, and design to translate business needs into AI-driven features.
• Conduct data exploration, preprocessing, and feature engineering on large, complex datasets.
• Deploy models in production environments using ML Ops best practices.
• Stay updated on the latest research in generative AI and proactively identify opportunities to adopt state-of-the-art techniques.
• Develop and maintain documentation, model cards, and evaluation metrics for all models.
• Mentor junior data scientists and contribute to team knowledge sharing.

Required Qualifications:

• 5-8 years of professional experience in data science or machine learning, with at least 2 years working on generative AI or LLM-based applications.
• Strong programming skills in Python, including libraries like PyTorch, TensorFlow, Hugging Face Transformers, LangChain, etc.
• Deep understanding of machine learning algorithms, NLP techniques, and deep learning architectures.
• Experience deploying models at scale using tools like Docker, Kubernetes, MLflow, SageMaker, or similar.
• Familiarity with prompt engineering, fine-tuning, and retrieval-augmented generation (RAG).
• Experience with cloud platforms such as AWS, Azure, or GCP.

Preferred Qualifications:

• Master's or PhD in Computer Science, Data Science, AI, or related fields.
• Experience working with vector databases (e.g., FAISS, Pinecone, Weaviate).
• Publications or contributions to open-source AI/ML projects.
• Understanding of ethical AI, model bias, and explainability techniques.