Roles & Responsibilities
Leadership & Strategy
Lead the development of cutting-edge Search and recommendation algorithms that improve relevance, personalization, and user engagement.
Define and execute the strategy for semantic understanding and recommendations, aligning with overall business goals.
Work cross-functionally with product managers, engineers, and data scientists to drive the roadmap for search and recommendation improvements.
•Search & NLP Expertise
Lead the design and implementation of Search/Recommendation models that go beyond traditional keyword matching to understand user intent and context.
Apply advanced NLP techniques such as transformers (BERT, GPT, T5), word embeddings, and contextualized word representations to enhance search relevance.
Use techniques like sentence embeddings, document embeddings, and similarity measures to build scalable search systems that understand semantic meaning.
•Model Development & Experimentation
Conduct research and experiments to design, develop, and validate new Search models and Recommendation techniques.
Continuously test, measure, and optimize models through A/B testing, real-time metrics, and user feedback loops.
Develop approaches to handle large-scale data, ensuring the models can be deployed efficiently in production environments.
•Collaboration & Mentorship
Lead and mentor a team of data scientists, guiding them in the development of semantic models and complex recommendation algorithms.
Foster a culture of knowledge sharing and collaboration across teams to ensure that the best practices are followed in model development and deployment.
Collaborate with engineers & product managers to ensure the effective integration of models into scalable production systems.
•Thought Leadership
Stay up to date with the latest research in Search, NLP, and recommendation systems.
Evangelize the use of cutting-edge techniques within the company to drive innovation in search and recommendations.
Years of Experience
• 8 years of experience executing and deploying data science, machine learning, deep learning, and generative AI solutions, preferably in a large-scale enterprise setting (fewer years may be accepted with a master’s or doctorate degree)
• 8 years of programming experience (fewer years may be accepted with a master's or doctorate degree)
• 5 years of SQL experience and knowledge of various statistical modeling or machine learning techniques
• Bachelor's degree in mathematics, statistics, physics, economics, engineering, computer science, data or information science, or related quantitative analytic field (or equivalent work experience in lieu of degree)
Candidates with Doctorate or Master’s degree are preferred
Education Qualification & Certifications
•Bachelor’s degree (Required): Mathematics, Statistics, Physics, Economics, Engineering, Computer Science, Data or Information Science, or related quantitative analytic field (or equivalent work experience in a related field)
•Doctorate Degree (Preferred): Mathematics, Statistics, Physics, Economics, Engineering, Computer Science, Data or Information Science, or related quantitative analytic field
Skill Set Required
Machine Learning & AI
•Supervised/unsupervised learning (e.g., regression, clustering)
•Deep learning (CNNs, RNNs, Transformers)
•Natural Language Processing (NLP) for search relevance
•Experience with generating vector embeddings
Statistical & Mathematical Expertise
•Probability, statistics, and A/B testing
Leadership & Collaboration
•Cross-functional team collaboration (engineering, product, design)
•Mentoring junior data scientists
•Communicating technical concepts to non-technical stakeholders
Tools & Frameworks
•Programming (Python, R, Scala)
•ML frameworks (TensorFlow, PyTorch, scikit-learn)
•Version control (Git)
Performance Evaluation
•Model evaluation using metrics (precision, recall, NDCG)
•Online learning and incremental model updates
Understanding of Data Engineering & Infrastructure
•Big data technologies (Apache Spark, Hadoop)
•Data pipeline management (ETL processes)
•Database management (SQL, NoSQL, Elasticsearch)
•Cloud platforms (AWS/GCP/Azure)