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Sr. Manager, Data Science/Machine Learning - Search Relevance

The Sr. Manager, Data Science/Machine Learning - Search Relevance will lead data science efforts for Salesforce's cloud-scale search system, focusing on developing and deploying machine learning models for search relevance, query understanding, and ranking. This role involves managing a team of data scientists, partnering with engineering and product teams, and driving the full lifecycle of model development to enhance the search experience across all Salesforce products.

About the role

About the Role

The Search Relevance team at Salesforce is looking for a proven technical leader to drive data science efforts for search intelligence within their multi-tenant cloud-scale search system. The team builds large-scale data processing pipelines and a variety of machine learning algorithms, including learning to rank, query understanding, intent classification, question-answering, and semantic search features, to deliver intelligent query processing at scale. This role involves developing relevance and ranking functions that enhance the search experience across all Salesforce products.

Responsibilities

Partner with product and engineering teams to define the vision and roadmap for data scientists and engineers applying Information Retrieval and Machine Learning techniques to optimize search results and ranking.
Drive the implementation of strategies utilizing natural language processing, intent analysis, machine learning, personalization, and question-answering to achieve measurable relevance improvements.
Own the complete lifecycle of model development, from ideation and data exploration to algorithm design, testing, development, deployment, monitoring, and tuning in production.
Help design and operationalize usage metrics and patterns to identify opportunities for search relevance improvement.
Analyze and understand the diverse products, devices, and use cases driven by search across Salesforce, developing strategies for enhancing relevance.
Contribute to a data-driven decision-making culture through A/B experimentation, including interpreting and presenting results to various audiences.
Hire and lead a team of high-caliber data scientists with strong academic credentials and industry experience in developing machine learning algorithms for search relevance.
Foster cross-functional collaboration by building deep relationships with key partners and coordinating with working teams.
Continuously learn and stay informed about innovations in enterprise search, AI/ML, and data science communities.

Required Skills

MS or PhD in Computer Science or a related field, or equivalent work experience.
5+ years of hands-on experience developing production-ready machine learning driven products such as recommender systems or search ranking systems.
Experience using advanced statistical and machine learning techniques including clustering, linear and logistic regressions, PCA, gradient boosting machines (GBM), support vector machines (SVM), learning-to-rank, neural networks (e.g., RNN, CNN), and other deep learning algorithms (e.g., Wide & Deep) to solve business problems on large-scale data sets.
3+ years of experience as a people manager managing data scientists and/or machine learning engineers, covering recruitment, role design, work design, and talent management.
Solid foundation in machine learning and statistics.
Track record of leading highly impactful projects from conception to finish.
Expert communicator in written and verbal form; ability to work well with executives, technologists, and influence across all organizational levels.
Superb quantitative reasoning and interpretation skills with a strong ability to provide analysis-driven insights and recommendations.
Understanding of A/B testing, expertise in metric definition and analysis.
Practical experience with Hadoop/Spark (or similar systems).
Strong grasp of Python, Java, or other comparable Object-Oriented Programming Language.
Proficient in SQL with strong data exploration and manipulation skills.
Experience with Lucene/Solr or similar search systems.

Desired Skills

Strong academic/research connection with industry publications in the Search/Recommendations space.
Experience with deep learning techniques for natural language processing and/or information retrieval.
Experience using cloud platforms such as GCP and AWS for model development and operationalization.
Experience with Agile software development and Test Driven Development methodologies.

About the role

About the Role

Responsibilities

Partner with product and engineering teams to define the vision and roadmap for data scientists and engineers applying Information Retrieval and Machine Learning techniques to optimize search results and ranking.
Drive the implementation of strategies utilizing natural language processing, intent analysis, machine learning, personalization, and question-answering to achieve measurable relevance improvements.
Own the complete lifecycle of model development, from ideation and data exploration to algorithm design, testing, development, deployment, monitoring, and tuning in production.
Help design and operationalize usage metrics and patterns to identify opportunities for search relevance improvement.
Analyze and understand the diverse products, devices, and use cases driven by search across Salesforce, developing strategies for enhancing relevance.
Contribute to a data-driven decision-making culture through A/B experimentation, including interpreting and presenting results to various audiences.
Hire and lead a team of high-caliber data scientists with strong academic credentials and industry experience in developing machine learning algorithms for search relevance.
Foster cross-functional collaboration by building deep relationships with key partners and coordinating with working teams.
Continuously learn and stay informed about innovations in enterprise search, AI/ML, and data science communities.

Required Skills

MS or PhD in Computer Science or a related field, or equivalent work experience.
5+ years of hands-on experience developing production-ready machine learning driven products such as recommender systems or search ranking systems.
Experience using advanced statistical and machine learning techniques including clustering, linear and logistic regressions, PCA, gradient boosting machines (GBM), support vector machines (SVM), learning-to-rank, neural networks (e.g., RNN, CNN), and other deep learning algorithms (e.g., Wide & Deep) to solve business problems on large-scale data sets.
3+ years of experience as a people manager managing data scientists and/or machine learning engineers, covering recruitment, role design, work design, and talent management.
Solid foundation in machine learning and statistics.
Track record of leading highly impactful projects from conception to finish.
Expert communicator in written and verbal form; ability to work well with executives, technologists, and influence across all organizational levels.
Superb quantitative reasoning and interpretation skills with a strong ability to provide analysis-driven insights and recommendations.
Understanding of A/B testing, expertise in metric definition and analysis.
Practical experience with Hadoop/Spark (or similar systems).
Strong grasp of Python, Java, or other comparable Object-Oriented Programming Language.
Proficient in SQL with strong data exploration and manipulation skills.
Experience with Lucene/Solr or similar search systems.

Desired Skills

Strong academic/research connection with industry publications in the Search/Recommendations space.
Experience with deep learning techniques for natural language processing and/or information retrieval.
Experience using cloud platforms such as GCP and AWS for model development and operationalization.
Experience with Agile software development and Test Driven Development methodologies.