Data Scientist

We’re looking for a data scientist (minimum 2+ years of experience) who is passionate about Natural Language Processing (NLP), Generative AI, and traditional machine learning—and who knows how to ship high-impact, production-grade models. This is a hands-on role where you’ll work across the full ML lifecycle : from prototyping to deployment, with a strong emphasis on production-readiness, APIs, and scalable architecture.

You’ll collaborate with AI engineers, product managers, and domain experts to develop intelligent systems that power next-generation insights for the pharma industry.

What You’ll Do

• Design and develop NLP and generative AI solutions using LLM frameworks like LangChain, LlamaIndex, CrewAI, or direct model provider SDKs / APIs (e.g., OpenAI, Anthropic, HuggingFace).
• Build and fine-tune traditional ML models (e.g., classification, regression, clustering) to support data-driven applications.
• Create robust and scalable AI pipelines and APIs using Python and FastAPI.
• Deploy models to production using AWS services such as ECS, Lambda, and S3, with attention to CI / CD, observability, and cost-effectiveness.
• Apply strong system design principles to architect scalable, maintainable, and secure ML systems.

Who You Are

• Minimum 2 years of industry experience in data science or machine learning.
• Strong background in NLP, LLMs, and generative AI—comfortable with both the theory and tooling.
• Familiarity with modern LLM stacks such as LangChain, LlamaIndex, CrewAI, or similar.
• Skilled in traditional ML methods using libraries like scikit-learn, XGBoost, etc.
• Expert-level Python programmer (beyond notebooks)—you write clean, maintainable, testable code.
• Experience exposing models as production-ready APIs using FastAPI (or similar frameworks).
• Strong understanding of AWS services—especially ECS, Lambda, and S3.
• Experience with MLOps and DevOps best practices is a plus (e.g., Docker, Terraform, Azure DevOps, Github Actions).