Principal Data Scientist

About the opportunity:
Role: Principal Data Scientist -
GenAI, Recommendation & Personalization
Location: Bangalore
Experience Level: 8-10 years
About the function:
At MakeMyTrip (MMT), technology is at the heart of
everything we do. As a leading player in the travel industry, we leverage
cutting-edge solutions like AI, machine learning, and cloud infrastructure to
create seamless and innovative travel experiences. We prioritize a tech-first
mindset, where our engineering teams lead the charge in transforming the way
people travel.
Our culture encourages collaboration, creativity, and
thought leadership, empowering engineers to challenge the status quo and
develop impactful, game-changing solutions. At MMT, you'll work with the latest
technologies, continually push boundaries, and have the chance to shape the
future of travel.
Join us and become part of a forward-thinking, tech-driven
organization that thrives on innovation and continuous learning.
About the Role:
We are looking for a Principal Data
Scientist to drive advanced AI/ML initiatives for large-scale consumer products
while providing technical leadership to a team of Data Scientists. This is a hands-on technical leadership role for someone
with deep expertise across Generative AI, Large Language Models, Recommendation
Systems, Personalization, Search, NLP, Information Retrieval, and
Representation Learning. The successful candidate will remain deeply involved
in research, experimentation, modeling, and system design while leading the
technical direction, architecture, and execution of complex AI/ML initiatives.
The role operates at the intersection of AI research and
production engineering. The ideal candidate should be comfortable evaluating
state-of-the-art research, designing novel ML architectures, running
large-scale experiments, and translating successful approaches into reliable
and scalable production systems.
In addition to hands-on technical contributions, the
candidate will mentor Data Scientists, drive architecture and design reviews,
establish ML best practices, and help raise the overall technical bar of the
Data Science organization.
What will you be doing:
Architect, develop, and productionize large-scale Machine
Learning, Deep Learning, and Generative AI systems for consumer-facing
applications.
Build personalization and recommendation systems using
techniques such as Two-Tower architectures, neural retrieval, sequential
modeling, representation learning, and learning-to-rank.
Design, fine-tune, and post-train Large Language Models
(LLMs) and Small Language Models (SLMs) using techniques such as Supervised
Fine-Tuning (SFT), LoRA/PEFT, preference optimization, GRPO, RLHF, and RLVR.
Design custom reward functions, reward models, and
evaluation signals for reinforcement-learning-based LLM post-training,
incorporating task-specific, quality, factuality, and preference signals.
Develop embedding and representation-learning systems
using contrastive learning, self-supervised learning, sequence modeling, and
related deep-learning techniques.
Build semantic search, retrieval, and ranking systems
leveraging dense retrieval, vector search, multi-vector retrieval, reranking,
and modern neural Information Retrieval techniques.
Explore and develop RAG, Graph RAG, Knowledge Graph,
Agentic AI, tool-calling, and multi-step reasoning systems for complex AI
applications.
Build scalable synthetic-data generation, data
augmentation, and model-training pipelines for LLM and ML applications.
Design robust offline and online evaluation frameworks for
Generative AI, retrieval, ranking, recommendation, and personalization systems,
including A/B experimentation.
Optimize AI/ML systems across model quality, inference
latency, throughput, scalability, reliability, compute, and serving cost.
Translate ambiguous business problems into well-defined ML
problems and independently drive them through problem formulation, research,
experimentation, architecture, productionization, and measurement.
Partner closely with Product, Engineering, Analytics, and
Data teams to deliver AI/ML capabilities with measurable customer and business
impact.
Provide technical leadership to a team of Data Scientists
through architecture reviews, technical guidance, mentoring, experimentation
strategy, and ML engineering best practices.
Continuously evaluate emerging AI/ML research and
determine its applicability to real-world problems and production systems.
Qualification & Experience:
8+ years of industry experience in Machine Learning, Data
Science, Applied AI, NLP, Information Retrieval, Recommendation Systems, or
related areas.
Strong hands-on expertise in Python and modern
deep-learning frameworks such as PyTorch and/or TensorFlow.
Deep understanding of Transformers, attention mechanisms,
embeddings, representation learning, and modern deep-learning architectures.
Strong expertise across multiple areas such as Generative
AI/LLMs, Recommendation Systems, Personalization, Search & Ranking, NLP,
Information Retrieval, and Representation Learning.
Hands-on experience across the modern LLM lifecycle,
including SFT, PEFT techniques such as LoRA, preference optimization, reward
modeling, RL-based post-training approaches such as GRPO/RLHF/RLVR, evaluation,
and inference.
Experience designing and deploying production-grade ML
systems at scale and working with large-scale behavioral, interaction, or
transactional datasets.
Strong system-design skills with the ability to make
practical trade-offs across model quality, latency, compute, scalability,
reliability, and cost.
Demonstrated ability to provide technical direction and
mentor Data Scientists and ML Engineers.
Strong communication and collaboration skills, with the
ability to work effectively across Product, Engineering, Data, Analytics, and
business stakeholders.
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