Senior Data Scientist - IT

Role Overview: You are a Senior Data Scientist joining the Search Squad to enhance search relevance and semantic retrieval systems for a vast product catalog. Your focus will be on refining the mapping of user queries to products through advanced ranking, retrieval, and machine learning techniques. Key Responsibilities: - Build and fine-tune search ranking and retrieval models utilizing Learning-to-Rank (LTR), LambdaMART, neural ranking, and hybrid retrieval methods. - Enhance query comprehension by implementing tokenization, normalization, semantic matching, and embedding-based retrieval. - Develop semantic search and vector search pipelines employing embeddings and dual encoder architectures. - Utilize user behavior and implicit feedback signals like clicks, CTR, dwell time, add-to-cart, and conversions. - Design and assess ranking metrics such as NDCG, MAP, CTR, CVR, and Recall. - Execute A/B experiments to enhance search relevance and the online user experience. - Collaborate closely with product, engineering, and business teams to ensure a balance between relevance quality and business objectives. - Analyze search performance and pinpoint opportunities for enhancing discovery and personalization. Qualification Required: - Strong proficiency in Python and SQL. - Experience with Search Ranking / Search Relevance systems. - Knowledge of Learning-to-Rank (LTR), LambdaMART, LambdaRank, or Neural Ranking. - Proficiency in Semantic Search, Embeddings, Vector Search, or Information Retrieval. - Hands-on experience with Elasticsearch / OpenSearch / Solr or similar technologies. - Sound understanding of Machine Learning algorithms including XGBoost or LightGBM. - Familiarity with A/B testing and ranking evaluation metrics like NDCG, MAP, CTR, and CVR. - Good grasp of NLP and query understanding techniques. Additional Company Details: - Experience in E-commerce or Marketplace search is preferred. - Exposure to Recommendation Systems and Personalization is beneficial. - Knowledge of Deep Learning for retrieval or ranking is advantageous. - Experience with RAG or LLM-powered search systems is a plus. - Familiarity with vector databases and hybrid search architectures is a bonus. (Note: The experience required for this role is 4-8 years. The location is Bengaluru (Hybrid), and the employment type is Full Time / Permanent.) Preferred Candidate Profile: - Candidates from E-commerce, Retail Tech, Consumer Internet, Marketplace, or Product-based companies are preferred. - Possess a strong analytical and problem-solving mindset. - Demonstrate product thinking with a focus on enhancing search user experience and relevance. Education: - B.Tech / BE / M.Tech / MS in Computer Science, Data Science, Artificial Intelligence, Mathematics, Statistics, or related disciplines. Role Overview: You are a Senior Data Scientist joining the Search Squad to enhance search relevance and semantic retrieval systems for a vast product catalog. Your focus will be on refining the mapping of user queries to products through advanced ranking, retrieval, and machine learning techniques. Key Responsibilities: - Build and fine-tune search ranking and retrieval models utilizing Learning-to-Rank (LTR), LambdaMART, neural ranking, and hybrid retrieval methods. - Enhance query comprehension by implementing tokenization, normalization, semantic matching, and embedding-based retrieval. - Develop semantic search and vector search pipelines employing embeddings and dual encoder architectures. - Utilize user behavior and implicit feedback signals like clicks, CTR, dwell time, add-to-cart, and conversions. - Design and assess ranking metrics such as NDCG, MAP, CTR, CVR, and Recall. - Execute A/B experiments to enhance search relevance and the online user experience. - Collaborate closely with product, engineering, and business teams to ensure a balance between relevance quality and business objectives. - Analyze search performance and pinpoint opportunities for enhancing discovery and personalization. Qualification Required: - Strong proficiency in Python and SQL. - Experience with Search Ranking / Search Relevance systems. - Knowledge of Learning-to-Rank (LTR), LambdaMART, LambdaRank, or Neural Ranking. - Proficiency in Semantic Search, Embeddings, Vector Search, or Information Retrieval. - Hands-on experience with Elasticsearch / OpenSearch / Solr or similar technologies. - Sound understanding of Machine Learning algorithms including XGBoost or LightGBM. - Familiarity with A/B testing and ranking evaluation metrics like NDCG, MAP, CTR, and CVR. - Good grasp of NLP and query understanding techniques. Additional Company Details: - Experience in E-commerce or Marketplace search is preferred. - Exposure to Recommendation Systems and Personalization is beneficial. - Knowledge of Deep Learning for retrieval or ranking is advantageous. - Experience with RAG or LLM-powered search systems is a plus. - Familiarity with vector databases and hybrid search arc