About the Role
We are looking for a Senior Data Scientist to develop data-driven forecasting and analytics solutions for networking hardware and data center infrastructure planning.
This role sits at the intersection of data science, demand forecasting, planning, and cloud infrastructure. You will work with large and complex datasets to understand demand drivers, develop forecasting models, improve the quality of the demand signal, and provide analytical insights that support hardware procurement, capacity planning, and data center build decisions.
The role is highly hands-on. You will work closely with Demand Planning, Networking Engineering, Data Center Planning, Supply Chain, Product, and Finance teams to turn business and engineering questions into analytical models and actionable insights.
Internal Responsibilities
Develop and maintain demand forecasting models for networking hardware and data center infrastructure across short- and long-term planning horizons.
Analyze historical demand, deployment trends, data center build plans, engineering inputs, product roadmaps, and other signals to identify key demand drivers.
Develop time-series, statistical, machine learning, and other forecasting approaches appropriate for different products and planning scenarios.
Measure and improve forecast accuracy, bias, stability, and confidence across products and planning horizons.
Build analytical models to evaluate base, upside, downside, and what-if scenarios and quantify their impact on hardware demand.
Partner with demand planners to translate analytical outputs into practical planning recommendations and improve forecasting processes.
Develop models that connect data center builds, deployment plans, network capacity, product requirements, BOMs, and SKU-level demand.
Investigate anomalies and changes in the demand signal, including unexpected demand shifts, duplicate signals, missing data, and inconsistent planning assumptions.
Build scalable datasets and analytical pipelines using SQL and Python to support 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, data availability, and the reliability of planning data.
Automate recurring analysis, forecasting, reconciliation, and reporting processes.
Apply machine learning, optimization, simulation, and AI/ML techniques where they provide meaningful improvements to planning or decision-making.
Communicate analytical findings clearly to planners, engineers, business partners, and senior leadership.
Work independently on ambiguous problems, define the appropriate analytical approach, and drive projects from problem definition through implementation and adoption.
Contribute to the development of modern, scalable data science and forecasting capabilities for demand planning.
Required Qualifications
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 and performing 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.
Strong communication skills and the ability to work effectively with both technical and non-technical stakeholders.
Preferred Qualifications
Experience with demand planning or forecasting for hardware, cloud infrastructure, networking, semiconductor, or data center environments.
Experience working 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 including probabilistic forecasting, 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.
External Responsibilities
Develop and maintain demand forecasting models for networking hardware and data center infrastructure across short- and long-term planning horizons.
Analyze historical demand, deployment trends, data center build plans, engineering inputs, product roadmaps, and other signals to identify key demand drivers.
Develop time-series, statistical, machine learning, and other forecasting approaches appropriate for different products and planning scenarios.
Measure and improve forecast accuracy, bias, stability, and confidence across products and planning horizons.
Build analytical models to evaluate base, upside, downside, and what-if scenarios and quantify their impact on hardware demand.
Partner with demand planners to translate analytical outputs into practical planning recommendations and improve forecasting processes.
Develop models that connect data center builds, deployment plans, network capacity, product requirements, BOMs, and SKU-level demand.
Investigate anomalies and changes in the demand signal, including unexpected demand shifts, duplicate signals, missing data, and inconsistent planning assumptions.
Build scalable datasets and analytical pipelines using SQL and Python to support 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, data availability, and the reliability of planning data.
Automate recurring analysis, forecasting, reconciliation, and reporting processes.
Apply machine learning, optimization, simulation, and AI/ML techniques where they provide meaningful improvements to planning or decision-making.
Communicate analytical findings clearly to planners, engineers, business partners, and senior leadership.
Work independently on ambiguous problems, define the appropriate analytical approach, and drive projects from problem definition through implementation and adoption.
Contribute to the development of modern, scalable data science and forecasting capabilities for demand planning.
Required Qualifications
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 and performing 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.
Strong communication skills and the ability to work effectively with both technical and non-technical stakeholders.
Preferred Qualifications
Experience with demand planning or forecasting for hardware, cloud infrastructure, networking, semiconductor, or data center environments.
Experience working 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 including probabilistic forecasting, 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.
