Principal Data Scientist - GenAI

About the Company

PhysicsWallah (PW) is India’s leading edtech platform, founded in 2016 by Alakh Pandey with a mission to make high-quality education affordable and accessible for every student.

What began as a YouTube channel has now grown into a multi-billion-dollar unicorn serving millions of learners through online courses, offline hybrid centres, and test-prep programs for JEE, NEET, UPSC, state boards, and beyond.

With 15M+ app downloads, 2M+ daily learners, and 120+ offline centres across India, PW has transformed the education landscape by combining technology, pedagogy, and affordability. Recognised as a Great Place to Work, the company continues to expand globally, partner with state governments, and pioneer AI-driven innovation in education.

Position Details

• Profile Name: Principal Data Scientist – GenAI

Qualification & Eligibility

• Bachelor’s or higher degree in a quantitative discipline (Computer Science, Statistics, Engineering, Applied Mathematics).

Work Experience

• Minimum 8+ years of experience.
• Startup experience preferred; Edtech work experience is a plus.

Roles & Responsibilities

• Lead the roadmap for Generative AI (GenAI) solutions across product and platform teams.
• Architect and optimise RAG pipelines (retrieval, embeddings, hybrid search, re-ranking, caching, latency-cost tradeoffs).
• Establish robust evaluation frameworks (automatic + human-in-the-loop) to measure LLM outputs on factuality, reasoning, hallucinations, coverage, and safety.
• Lead research and applied innovation around foundation model fine-tuning (instruction tuning, LoRA, adapters, PEFT, multi-task fine-tuning).
• Mentor senior scientists/engineers, setting standards for model development, experimentation, and research rigour.
• Partner with Product, Engineering, and Business stakeholders to deliver AI systems with measurable business impact.

Skill Sets Required

• Deep understanding of foundational LLMs (architectures, tokenization, pre-training vs fine-tuning, context window optimization).
• Strong experience with RAG systems (vector DBs, hybrid retrieval, embeddings, ranking models).
• Proven ability to design and implement evaluation pipelines (truthfulness, reasoning quality, safety, alignment).
• Hands-on expertise in fine-tuning approaches: instruction tuning, LoRA, PEFT, model merging, distillation, prompt-tuning.
• Strong background in representation learning, transformers, IR/retrieval models, and embeddings.
• Proficiency in Python, PyTorch/TensorFlow, and production ML pipelines.
• Experience with MLOps practices: model serving, monitoring, observability for LLM systems.