
Principal Data Scientist
6 hours ago
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.
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