Senior Data Scientist - Demand Planning and Forecasting

Oracle•Seattle, WA

About The Position

Develop data-driven forecasting and analytics solutions for networking hardware and data center infrastructure planning. The role combines data science, demand forecasting, planning, and cloud infrastructure, using large and complex datasets to identify demand drivers, develop forecasting models, improve demand signals, and support hardware procurement, capacity planning, and data center build decisions. This is a hands-on role that partners with Demand Planning, Networking Engineering, Data Center Planning, Supply Chain, Product, and Finance. The Senior Data Scientist translates business and engineering questions into analytical models and actionable insights, and contributes to scalable data science and forecasting capabilities for demand planning.

Requirements

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, including data exploration, feature engineering, modeling, and analysis.
Experience translating business or operational problems into quantitative models and actionable recommendations.
Experience with planning, supply chain, capacity planning, demand forecasting, or other operational decision-making processes.
Strong communication skills and ability to work effectively with technical and non-technical stakeholders.

Nice To Haves

Experience with demand planning or forecasting for hardware, cloud infrastructure, networking, semiconductor, or data center environments.
Experience with SKU-level demand, BOMs, product lifecycles, NPI, EOL/EOS, or hardware product transitions.
Experience connecting data center build plans or deployment schedules to hardware demand forecasts.
Experience with networking hardware such as switches, NICs, DPUs/SmartNICs, optical transceivers, cables, or related components.
Experience with advanced forecasting methods such as probabilistic or hierarchical forecasting, causal modeling, gradient boosting, deep learning, or other machine learning techniques.
Experience with scenario modeling, optimization, simulation, or capacity planning.
Experience building production-quality data pipelines, analytical datasets, or forecasting platforms.
Experience with cloud data platforms, distributed data processing, or large-scale analytics.
Experience applying GenAI/LLMs or AI agents to analytics, forecasting, or planning workflows.
Experience communicating analytical results and recommendations to senior engineering, business, or executive audiences.

Responsibilities

Develop and maintain short- and long-term demand forecasting models for networking hardware and data center infrastructure.
Analyze historical demand, deployment trends, data center build plans, engineering inputs, product roadmaps, and other signals to identify demand drivers.
Develop time-series, statistical, machine learning, and other forecasting approaches suited to products and planning scenarios.
Measure and improve forecast accuracy, bias, stability, and confidence across products and planning horizons.
Build analytical models for base, upside, downside, and what-if scenarios, and quantify their impact on hardware demand.
Partner with demand planners to turn analytical outputs into practical planning recommendations and improve forecasting processes.
Develop models connecting data center builds, deployment plans, network capacity, product requirements, bills of materials (BOMs), and SKU-level demand.
Investigate anomalies and demand-signal changes, including unexpected shifts, duplicate signals, missing data, and inconsistent planning assumptions.
Build scalable datasets and analytical pipelines using SQL and Python for forecasting and planning workflows.
Develop reusable analytical frameworks, dashboards, and tools that improve visibility into demand trends, forecast performance, and planning risks.
Work with Data Engineering and other technical teams to improve data quality, availability, and reliability of planning data.
Automate recurring analysis, forecasting, reconciliation, and reporting processes.
Apply machine learning, optimization, simulation, and AI/ML techniques when they meaningfully improve planning or decision-making.
Communicate analytical findings clearly to planners, engineers, business partners, and senior leadership.
Independently define analytical approaches to ambiguous problems and drive projects from problem definition through implementation and adoption.
Contribute to modern, scalable data science and forecasting capabilities for demand planning.

Benefits

Medical, dental, and vision insurance, including expert medical opinion.
Short-term and long-term disability insurance.
Life insurance and AD&D.
Supplemental life insurance for employees, spouses, and children.
Health care and dependent care Flexible Spending Accounts.
Pre-tax commuter and parking benefits.
401(k) Savings and Investment Plan with company match.
Paid time off, including flexible vacation for eligible salaried non-overtime positions or accrued vacation for other eligible employees.
11 paid holidays.
Paid sick leave: 72 hours upon hire, refreshed each calendar year, with carryover up to a maximum of 112 hours.
Paid parental leave.
Adoption assistance.
Employee Stock Purchase Plan.
Financial planning and group legal benefits.
Voluntary auto, homeowner, and pet insurance.
May be eligible for bonus and equity.