Principal Data Scientist - Discovery & Recommendation System

Location : Hydrabad / Bangalore, India

Type : Full-Time | Immediate Joining Preferred

CTC : Competitive

About YAL.ai

YAL.ai (Your Alternative Life) is a next-generation communication and discovery platform that redefines how people connect, interact, and collaborate. We integrate cutting-edge AI into every layer of interaction from multilingual ASR and fraud detection to personalized discovery and recommendation systems. Built on a Zero Trust Architecture, YAL.ai is designed for security, privacy, and intelligence at scale. With our tagline “Where AI Meets Integrity”, we’re creating an ecosystem where discovery is trustworthy, real-time, and hyper-personalized, powering millions of meaningful connections.

Role Overview

We are seeking a Principal Data Scientist (Discovery & Recommendation Systems) to lead the design, research, and scaling of YAL.ai’s core recommendation engine. This is a senior, leadership-level role where you will architect and implement end-to-end discovery pipelines, spanning profile recommendations, semantic matching, community discovery, and real-time personalization. You will be responsible for strategy + execution, driving innovation in graph-based learning, deep retrieval, personalization algorithms, and scalable re-ranking pipelines to deliver state-of-the-art discovery experiences inside YAL.ai’s platform.

Key Responsibilities

• Architect and lead the development of YAL.ai’s recommendation engine for user-user, user-group, and topic discovery.
• Design scalable retrieval + ranking pipelines, integrating dense embeddings, sparse signals, and graph-based features.
• Drive innovation in graph learning (Node2Vec, DeepWalk, GNNs) and semantic similarity (Twin-BERT, SBERT, cross-encoders) for matching and personalization.
• Build cold-start solutions using hybrid approaches (metadata + embeddings + activity signals).
• Develop approximate nearest neighbor (ANN) search at scale (FAISS, HNSW, ScaNN) for sub-100ms retrieval latency.
• Lead re-ranking strategies combining behavioral signals, diversity, trust, and multilingual fairness.
• Integrate multilingual NLP and cross-lingual embeddings to enable code-mixed and Indic language discovery.
• Own offline evaluation metrics (NDCG, Recall@K, MAP) and online A / B testing frameworks for recommendation quality.
• Mentor data scientists, set research direction, and collaborate closely with engineering / product to bring research-grade models into production at scale.

Required Technical Skills

Qualifications

Experience

Bonus If You Have

How to Apply