Principal Data Scientist

About Us: Salesforce is the world's #1 AI CRM platform. Our Cloud Economics and Capacity Management team plays a crucial role in developing thoughtful, data-driven tools that empower internal partners to optimize infrastructure cost and utilization at a global scale, spanning both 1st Party and Public Cloud providers. We use sophisticated data science techniques to transform petabytes of data into actionable predictions, providing business insights to internal partners.

Position Overview: We are seeking a versatile and motivated Data Scientist with experience deploying, monitoring, and maintaining predictive models to join our dynamic Cloud Economics and Capacity Management team. In this role, you will collaborate with internal partners to understand their requirements, design innovative time series forecasting solutions, and chip in to the development, release, and maintenance of time series forecasting models. As a technical lead within our team, you will have the unique opportunity to directly impact the efficiency of Salesforce's global infrastructure.

Responsibilities:

• Partner with cross-functional teams to understand business problems, produce insights, and inform infrastructure strategy at Salesforce
• Lead and participate in the requirement, design, and development discussions driving improvements to the data science lifecycle.
• Develop and deploy production-ready models that contribute to actionable insights for capacity planners, financial analysts, service owners, and technical leaders.
• Continuously improve algorithmic performance with a focus on complex time series forecasting in the capacity management and FinOps space.
• Mentor team members and suggest improvements to reduce time-to-insight and mature our data science lifecycle.

Qualifications:

• A related technical degree required
• 10+ years of industry experience and a passion for crafting, analyzing and deploying machine learning-based solutions in production environments
• Experience working as part of a team with mature data science products
• Consistent record in building data science products using modern development lifecycle methodologies: CI/CD, QA, and Agile Methodologies
• Experience deploying, monitoring and maintaining data science products in cloud environments such as AWS or Microsoft Azure
• Good understanding of Machine Learning methods and Statistics, including data science project lifecycle and associated challenges at each stage of development
• Proficient at writing good quality, well-documented and tested, scalable code - Python preferred. Experience with tools like mlFlow, Airflow, Docker and Cloud Platforms such as AWS/Google Cloud Platform is ideal
• Solid understanding of data transformations and analytics functions using tools/languages like Pandas, Sklearn, SQL and Spark
• Proven experience in machine learning engineering with a focus on time series forecasting.
• Excellent communication skills with the ability to interact directly with internal stakeholders.

Preferred Skills:

• Experience working with data technologies that allow effective storage and analysis of large amounts of data (e.g. Spark, Presto, Hive, etc.)
• Experience in time series forecasting methods