Senior Data Scientist – Sunnyvale, CA (Hybrid) | Google AI & Carbon Modeling Career

📍 Location: us

🏷 Type: Not specified

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Job Overview

This role at Google sits within Research, Operations, and Infrastructure Data Science, focused on building advanced AI/ML energy consumption and carbon footprint models. The ideal candidate is a Senior-Level data scientist with strong experience in statistical modeling, optimization, and large-scale systems analysis. You will work at the intersection of cloud infrastructure, sustainability, and machine learning to quantify emissions and identify reduction opportunities. This position is designed for professionals who can translate complex data into actionable climate and operational insights at global scale.

🌍 Work Eligibility & Location

This role is primarily anchored in the United States, requiring onsite or hybrid presence in Sunnyvale. However, Google’s global structure often allows international candidates with strong profiles to be considered, especially when visa sponsorship aligns with business needs.

💰 Salary Intelligence

This is a highly competitive compensation package. While the base salary is strong at $147K–$211K, total compensation significantly increases through equity and performance bonuses. Compared to industry benchmarks, this sits in the top tier for data science roles focused on AI infrastructure and climate analytics.

📊 Role Breakdown

This role focuses on building and scaling complex models that estimate AI/ML energy consumption and associated carbon emissions across Google Cloud infrastructure. You will design statistical and probabilistic frameworks that integrate signals such as training data volume, model architecture complexity, deployment patterns, and hardware efficiency. A key responsibility is selecting the right level of model sophistication—balancing accuracy with scalability in production systems. You will apply Python, R, SQL, and advanced statistical methods to extract insights from distributed datasets. Additionally, you will collaborate with engineering and climate experts to translate model outputs into actionable decarbonization strategies. Expect to work with optimization techniques, forecasting systems, and simulation-based modeling approaches. This role demands strong ability to connect abstract environmental metrics with real-world infrastructure behavior, ensuring emissions reduction efforts are measurable, prioritized, and scientifically grounded.

🧩 Required Skills & Fit

📈 Difficulty & Competitiveness

This is a demanding role requiring mastery of both advanced statistical systems and large-scale infrastructure analytics. Candidates are expected to operate at senior technical depth, often with PhD-level research experience or equivalent industry impact.

🚀 Career Impact

This role offers exceptional long-term value by positioning you at the forefront of AI sustainability engineering. Experience gained here directly translates into leadership roles in AI infrastructure, climate tech, and advanced analytics organizations.

📋 Key Responsibilities

You will lead the development of energy-use forecasting models across AI/ML systems and build scalable frameworks that estimate carbon emissions from cloud workloads. Responsibilities include designing statistical and probabilistic models, analyzing infrastructure-level datasets, and integrating hardware performance signals with emissions metrics. You will collaborate closely with engineering, climate science, and operations teams to identify reduction opportunities. Additional duties include building optimization systems, evaluating emissions reduction initiatives, and producing actionable insights for sustainability decision-making at scale using Python, SQL, and advanced ML pipelines.

🎯 Application Strategy

🧠 Application Optimization (Adaptive)

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🧠 Fit & Positioning Analysis

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📅 Application Signals

This role shows high urgency due to its alignment with Google’s Net Zero 2030 strategy and infrastructure scaling needs. Expect extremely high competition from PhD-level researchers and senior ML engineers globally. Early applications with strong portfolio evidence in AI systems or energy modeling significantly increase interview probability.

AI Energy Modeling: Highlight ML models estimating AI workload energy consumption

Carbon Forecasting: Showcase emissions prediction using statistical or probabilistic models

Cloud Analytics: Demonstrate Google Cloud or large-scale infrastructure data analysis

Optimization Systems: Emphasize building optimization or simulation decision frameworks

Cross-Functional Impact: Quantify collaboration with engineering and sustainability teams

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✅ Job Source & Verification

This listing is based on an official Google career posting and reflects verified role responsibilities and compensation structure. The information is consistent with Google’s publicly available hiring framework, last updated at the time of extraction. Always verify final details on the official Google Careers page before applying.