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We're Salesforce, the Customer Company, inspiring the future of business with AI Data CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And, we empower you to be a Trailblazer, too - driving your performance and career growth, charting new paths, and improving the state of the world. If you believe in business as the greatest platform for change and in companies doing well and doing good - you've come to the right place.

Salesforce is the global leader in Cloud-based (SaaS) solutions that enable the world's premier brands to maintain a robust online presence with high ROI. We do that by providing a highly scalable, integrated cloud platform that allows our clients to rapidly launch and manage multiple e-commerce stores, initiate unique marketing campaigns, and drive customer traffic across a global footprint. We are looking for a top-notch machine learning scientist/data scientist to join the machine learning science team of the copilot and personalization org. Our team is tasked with producing performant machine learning models and copilot systems to enhance and personalize the shopper experience through search, product recommendations, content personalization, promotion selection, and much more. In addition, we augment and automate the work of the merchandiser and admins to enable them to react faster and better to changing markets, trends, and customer demands. You will be responsible for turning business needs into machine learning solutions that we can deploy to production in collaboration with our engineering teams. To achieve this, you will research, design, prototype, and build machine learning systems, with large-scale training and offline/online tuning and experimentation.The successful candidate will work with world-class database gurus, software and machine learning engineers, research scientists, and fellow machine learning scientists. We are looking for someone who gets a kick out of staying on top of the latest machine learning and AI literature/tools/techniques and figuring out the best way to apply these for practical solutions. We encourage contribution to tools and knowledge sharing within the team, the company, and the industry.Responsibilities

• Work with product management and leadership to translate business problems into data science problems
• Research, prototype, and build demonstrations of machine learning ideas for quick validation and feedback
• Design and build machine learning systems to hit accuracy and performance metric targets
• Productize machine learning systems in collaboration with engineering teams
• Design and conduct experiments to ensure optimal performance of machine learning systems
• Educate engineering and product teams about data science during collaboration
• Mentor and collaborate with other members of the data science team
• Share technical innovations with the team and across the company
• Create innovations and contribute to conferences and/or open-source tools

Qualifications
Must-Have

• Fluent in prototyping/building machine learning models and algorithms and wrangling large datasets
• Ph.D. in a quantitative discipline with 3 years of relevant experience or a BS/MS in a quantitative discipline with 6 - 10 years of relevant experience.
• Expertise in classical machine learning, deep learning, and large-language-models (LLMs)
• Proficient in using Python scientific stack (e.g. Numpy, Pandas, PyTorch, SciPy, Jupyter)
• Proficient in shell scripting, Unix/Linux command-line tools, working with cloud infrastructure (AWS)
• Great communication skills: ability to discuss with scientists, engineers, designers, and product managers

Nice to have

• Ph.D. with 5-10 years of relevant experience
• Experience in shipping machine learning models into large-scale production systems
• Deep Experience in reinforcement learning, deep learning, causal inference, and information retrieval
• Experience in building machine learning models for e-commerce problems like search and recommendations
• Open-source machine learning code from one's paper, contributions to any projects, implementations of papers
• Publications in any of {cvpr, iccv, eccv, neurips, iclr, icml, acl, emnlp, recsys, kdd}

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