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Position Overview: We are seeking an innovative and driven Data Scientist with a strong focus on AI-driven solutions for large-scale infrastructure management to join our Cloud Economics and Capacity Management (CECM) team. In this role, you will apply advanced AI, machine learning, and time series forecasting techniques to optimize the performance, capacity, and cost-efficiency of Salesforce's global cloud infrastructure.

A key responsibility will be leveraging data science to address critical cloud cost management challenges, such as predicting future resource needs, identifying inefficiencies, and automating cost-saving strategies across our infrastructure. You will collaborate closely with cross-functional teams to design, implement, and maintain predictive models that enable Salesforce to scale operations while minimizing waste and maximizing return on infrastructure investments.

As a technical lead within the team, you will directly impact cloud cost efficiency and capacity planning through the development of cutting-edge AI solutions that support automation and enhance the scalability of our infrastructure. Your contributions will help ensure that our cloud resources are utilized effectively, enabling Salesforce to meet the evolving needs of millions of users worldwide while keeping costs under control.

Check out our team's latest blog post for more insights on our approach.

Responsibilities:

• Design and Develop AI/ML Solutions: Lead the design, development, and deployment of AI-driven models, focusing on cloud infrastructure management, capacity planning, and cost optimization across Salesforce's global infrastructure.
• Build and Maintain Predictive Models: Develop robust time series forecasting models and machine learning algorithms to predict infrastructure demand, resource utilization, and to inform cloud cost management strategies.
• Optimize Cloud Costs and Efficiency: Apply data science techniques to drive automation in cloud cost management, improving infrastructure utilization while reducing waste, and providing insights for capacity and budget planning.
• Collaborate Cross-Functionally: Work closely with internal teams such as Cloud FinOps, Capacity Planning, and Product Engineering to translate business needs into data-driven solutions that improve infrastructure scaling and operational performance.
• Monitor and Iterate on Models: Ensure models remain accurate and reliable by continuously monitoring performance and making updates based on new data, system behavior, and evolving business requirements.
• Influence and Drive Innovation: Act as a technical leader in AI and machine learning, promoting best practices and helping drive innovation in the use of AI for cloud management at scale.

Qualifications:

• 10+ Years of Data Science Experience: Proven track record of leading complex AI and machine learning projects, preferably in cloud infrastructure or a related domain.
• Advanced Degree in a Relevant Field: PhD or Master’s degree in Data Science, Machine Learning, Computer Science, Engineering, or a related technical field.
• Experience in Cloud Infrastructure: Strong understanding of cloud platforms (AWS, GCP, or Azure), with experience in applying AI and data science to infrastructure management, capacity planning, or cloud cost optimization.
• Demonstrated Leadership: Experience guiding teams and mentoring junior data scientists, with the ability to influence strategy and communicate technical concepts to stakeholders.
• Strong Analytical and Problem-Solving Skills: Expertise in identifying key business challenges and developing data-driven solutions that improve performance and efficiency.

Preferred Skills:

• Machine Learning Engineering: Strong background in ML engineering, including experience deploying models into production environments, and managing model lifecycle (monitoring, retraining, version control).
• Cloud Platforms and Tools: Hands-on experience with cloud platforms like AWS, GCP, or Azure.
• Programming and Data Tools: Proficient in Python, SQL, and libraries such as TensorFlow, PyTorch, or scikit-learn, with experience in managing large datasets and distributed computing frameworks (e.g., Spark, Kubernetes).
• Automation and Infrastructure as Code: Experience with infrastructure automation tools (e.g., Terraform, CloudFormation) and an understanding of DevOps principles in the context of deploying AI/ML models.
• Strong Communication and Collaboration Skills: Ability to translate complex technical ideas into business value and effectively collaborate with both technical and non-technical stakeholders.

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