Salesforce, Inc..
About Us : Salesforce is the world's #1 AI CRM platform. Our Cloud Economics and Capacity Management team plays a crucial role in developing intelligent, data-driven tools that empower internal stakeholders to optimize infrastructure cost and utilization at a global scale, spanning both 1st Party and Public Cloud providers. We leverage advanced data science techniques to transform petabytes of data into actionable predictions, providing business insights to internal stakeholders. Position Overview: We are seeking a talented 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 stakeholders to understand their requirements, design innovative time series forecasting solutions, and contribute 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.You will also play a key role in advancing our use of large language models (LLMs) to accelerate insight generation. By integrating LLMs with our forecasting and cost data, you'll help automate root cause analysis, generate proactive recommendations, and reduce the time from data to decision for infrastructure stakeholders. Responsibilities: Partner with cross-functional teams to understand business problems, produce insights, and inform infrastructure strategy at Salesforce 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 Apply LLMs and prompt engineering to automate the generation of insights and explanations from time series forecasts and anomalies Continuously improve algorithmic performance with a focus on complex time series forecasting in the capacity management and FinOps space Collaborate effectively with team members and suggest improvements to reduce time-to-insight and mature our data science lifecycle Qualifications: A related technical degree required 6+ years of industry experience and a passion for crafting, analyzing, and deploying machine learning-based solutions 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 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/GCP 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 Familiarity with LLM applications in data science, including prompt engineering and generative insight workflows, is a plus Excellent communication skills with the ability to interact directly with internal stakeholders #J-18808-Ljbffr