This role pays less than
65%
of similar roles. Most pay
$137,987–$193,375
— the shaded band above.
At the midpoint, this role pays about
$155k
versus about
$166k
for comparable roles.
Based on 240 similar postings.
Employer
About Oracle
Oracle Corporation is a leading multinational technology company specializing in database software, cloud computing, and enterprise software.
Oracle currently has
513 open roles
on FindRole.
Listed pay typically runs
$97,800–$209,500
across 384 roles with salary data.
Senior Data Scientist - Demand Planning and Forecasting joins the team at the intersection of data science, demand forecasting, and cloud infrastructure. This role focuses on developing data-driven forecasting and analytics solutions for networking hardware and data center infrastructure planning. The successful candidate will build and maintain demand forecasting models across various horizons, analyze historical trends to identify key drivers, and develop time-series, statistical, and machine learning approaches to improve forecast accuracy. Day-to-day responsibilities include building scalable datasets and analytical pipelines using SQL and Python, automating reporting processes, and creating tools to provide visibility into planning risks. The role addresses the complex challenge of aligning data center build plans and network capacity with SKU-level demand for components like switches and transceivers. Required skills include advanced programming in Python and SQL, statistical modeling, and experience with large datasets.
What does a Data Scientist earn?
Median $162000 from 273 postings across 56 companies.
Develop and maintain demand forecasting models for networking hardware and data center infrastructure across various planning horizons.
Build analytical models to evaluate base, upside, downside, and what-if scenarios to quantify impacts on hardware demand.
Create scalable datasets and automated analytical pipelines using SQL and Python to support forecasting workflows.
Measure and improve forecast accuracy, bias, stability, and confidence across different products and timeframes.
Develop reusable dashboards and tools to provide visibility into demand trends and planning risks.
Identify key demand drivers by analyzing historical data, deployment trends, and product roadmaps.
Translate complex analytical outputs into practical recommendations for procurement, capacity planning, and infrastructure decisions.
Automate recurring reporting processes and investigate anomalies in the demand signal to ensure data reliability.
What we're looking for
Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, Operations Research, Economics, or a related quantitative field.
5+ years of experience in data science, applied science, forecasting, demand planning analytics, operations research, or a related quantitative discipline.
Strong hands-on programming experience with Python and advanced SQL.
Experience developing and deploying forecasting or predictive models using real-world business data.
Strong understanding of statistical modeling, time-series forecasting, model evaluation, and experimental or analytical methodologies.
Experience working with large and complex datasets to perform data exploration, feature engineering, modeling, and analysis.
Experience translating business or operational problems into quantitative models and actionable recommendations.
Experience working with planning, supply chain, capacity planning, demand forecasting, or other operational decision-making processes.
Experience with hardware, cloud infrastructure, networking, semiconductor, or data center environments (preferred).
Experience with advanced forecasting methods like probabilistic forecasting, gradient boosting, deep learning, or other machine learning techniques (preferred).
Experience building production-quality data pipelines, analytical datasets, or forecasting platforms (preferred).
Experience applying GenAI/LLMs or AI agents to analytics, forecasting, or planning workflows (preferred).
Python
IBM watsonx
SQL
Pandas
NumPy
Scikit-learn
Machine Learning
Statistical Modeling
Data Visualization
Matplotlib
Seaborn
Cognos
Git
Version Control
AWS
Azure
IBM Cloud