Principal Data Scientist

7 days ago

Bengaluru, India PW (PhysicsWallah) Full time

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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