Senior Staff Demand Planning

ServiceNow•Orlando, FL

14h•Hybrid

About The Position

NOTE: This position requires passing a ServiceNow background screening, USFedPASS (US Federal Personnel Authorization Screening Standards). This includes a credit check, criminal/misdemeanor check and taking a drug test. Any employment is contingent upon passing the screening. Due to Federal requirements, only US citizens, US naturalized citizens or US Permanent Residents, holding a green card, will be considered. This role will require at min 2 days a week in the office. The Demand Planning team within ServiceNow’s FinOps organization is an innovative, customer-focused group responsible for capacity demand planning, forecasting, cost measurement, reporting, and analysis for ServiceNow's top-tier infrastructure. We aim to provide industry-leading work experiences for our customers. As an ML/AI engineer on the capacity planning team, you will collaborate with a data-centric group of data scientists, engineers, and capacity planners. Our mission is to scale ServiceNow's global rack, server, and data center capacity as and when required. The team plays a critical role in managing and forecasting customer demand while overseeing capacity planning and analytics for ServiceNow’s global infrastructure. This includes adjusting forecasting models to incorporate hybrid cloud strategies and fine-tuning them to adapt to new, multi-dimensional metrics—ensuring that ServiceNow’s global rack, server, and data center capacity is efficiently scaled as business needs evolve. Additionally, you will be instrumental in building and refining capacity forecasting models that utilize AI and Machine Learning (ML) techniques, as well as designing analytics tools to predict resource demand across diverse infrastructure environments. This includes proactively identifying capacity-related issues, leading experiments to determine scaling and utilization parameters for different service tiers, and incorporating multi-dimensional metrics into the models. The role requires close collaboration with hardware, capacity, infrastructure, platform engineering, and cloud analytics teams to develop comprehensive capacity roadmaps and deliver actionable insights for a variety of cloud capacity engineering initiatives, with a strong emphasis on supporting both traditional and hybrid cloud strategies.

Requirements

Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry.
12 years of related experience with a Bachelor's degree; or 8 years and a Master's degree; or a PhD with 5 years experience in Computer Science, Data Analytics or related field; or equivalent experience.
Technical Expertise: Programming: Python, Java; strong OOP and algorithmic skills.
AI/ML: Time series modeling, regression, Bayesian forecasting, econometrics.
Tools: TensorFlow, PyTorch, Spark, Hive-Hadoop, Big Data frameworks.
Data Visualization: Python, Tableau, Excel, Lightdash, BI tools
Experience in developing new products that utilize challenging high-performance algorithms.
Expertise in coding efficient, object-oriented, modularized, and high-quality software.
Expertise in core AI/ML techniques and algorithms.
Understanding of unit testing, profiling, and code tuning.

Nice To Haves

ServiceNow platform architecture (CMDB, instance topology), unit testing, profiling, and code optimization.
Strong communication and cross-functional collaboration skills.

Responsibilities

Design, implement, and deploy AI/ML-driven capacity forecasting models for long-range demand and supply planning.
Convert demand forecasts into actionable server and rack requirements.
Enhance forecasting accuracy by incorporating multi-dimensional metrics and hybrid cloud strategies.
Run forecast scenarios to optimize scaling and utilization across service tiers.
Collaborate with hardware, infrastructure, and cloud analytics teams to develop capacity roadmaps.
Track and report forecast variability, presenting insights to leadership.
Apply advanced techniques (ARIMA, RNN, LSTM, NOW AI) to improve predictive models.
Build analytics tools to identify bottlenecks and proactively address capacity issues.