Data Scientist Ml Ai Python

2 weeks ago

Hyderabad, India YAL.ai Full time
Hydrabad / Bangalore, India Type: Full-Time | Immediate Joining Preferred
ai

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.

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.

ai’s recommendation engine for user-user, user-group, and topic discovery.
Build cold-start solutions using hybrid approaches (metadata + embeddings + activity signals).
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, , 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.

8–12 years of experience in applied ML/DS with at least 5+ years in recommendation systems.
~ Proven expertise in recommender algorithms (collaborative filtering, content-based, hybrid, contextual bandits, sequence-aware recommenders).
~ Hands-on with ANN search libraries (FAISS, HNSW, Milvus, ScaNN) and vector databases.
~ Deep experience with transformer-based embeddings for semantic retrieval.
~ Experience with ranking/re-ranking systems (pairwise, listwise ranking, LambdaMART, DLRM, neural rankers).
~ Solid foundation in evaluation metrics (NDCG, MRR, AUC, diversity, coverage) and online experimentation (A/B, interleaving).
~ Expert in Python, PyTorch/TensorFlow, HuggingFace, and scalable ML pipelines.
~ Experience optimizing models for real-time inference (distillation, quantization, batching, async serving).

Master’s or PhD in Computer Science, AI, Data Science, or related field.
Strong academic or research background in recommendation systems, ranking models, or graph learning.
Experience

~Experience handling multilingual and user-generated data in production.
~ Bonus If You Have

Experience with bandit algorithms or reinforcement learning in recommendations.
Hands-on with Indic and code-mixed language recommendation pipelines.
Contributions to open-source recsys/NLP projects or strong Kaggle performance in recsys/NLP.

Apply directly via LinkedIn or DM us or
with Subject: (Principal Data Scientist – Discovery & Recommendation | Your Name)

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