Data Science Jobs in Sunnyvale, CA

Leads advanced data science initiatives for last-mile delivery, using machine learning, statistical modeling, optimization, and visualization to solve complex business problems. Oversees model development, validation, tuning, and deployment while translating business requirements into actionable solutions. Mentors data science teams, guides feature engineering and coding practices, collaborates cross-functionally, and drives data strategy and continuous improvement across delivery operations.

Leads a team developing and deploying production AI systems for Spark Driver pricing and incentives. Oversees machine learning, reinforcement learning, optimization, experimentation, deployment, monitoring, and continuous improvement. Partners with Business, Product, and Engineering to define strategy, architect scalable systems, and deliver measurable marketplace impact. Provides technical direction, establishes engineering and MLOps best practices, mentors data scientists and machine learning engineers, and drives reliable production AI solutions.

Lead data science strategy and a rapid-prototyping team to build production-grade ML and GenAI solutions for contractor earnings, tax, and compliance. Drive model governance, automation, and cross-functional delivery to reduce errors, mitigate risk, and scale measurable ROI across product, engineering, and operations.

Lead global product strategy and lifecycle for large-scale network orchestration, telemetry, and analytics products. Define technical use cases and architecture requirements, guide cross-functional teams, engage customers and partners, and deliver scalable datacenter networking solutions aligned with NVIDIAs AI infrastructure roadmap.

Lead a team of data scientists to enhance infrastructure through data science, focusing on capacity planning, performance optimization, and customer experience.

Lead and grow a data science team to develop, deploy, and govern ML and advanced analytics solutions. Define strategy, align stakeholders, build scalable processes, mentor staff, and translate technical work into business impact while promoting responsible AI and cross-functional collaboration.

Lead and grow a Product Data Science team to drive modeling, experiments, and data analysis that improve Waymo Driver performance and operational efficiency. Partner with senior leadership and cross-functional teams to prioritize resources, support geographic expansion, and translate vehicle and operational data into scalable product decisions.

Lead causal measurement and analytics for media investments: design quasi-experimental and observational approaches, apply interpretable ML and statistics, write reproducible Python/SQL analysis, surface actionable insights for marketing partners, and uphold data ethics and stewardship.

Lead and grow a data science team focused on electric grid visibility and resilience. Oversee data curation, annotation, and quality control; define metrics and experimentation frameworks; translate business goals into data science roadmaps; deliver production-grade ML evaluation tooling; and communicate strategic recommendations to executives while collaborating with engineering, product, and power systems experts.

Lead a multidisciplinary engineering and data science team in a forward-deployed model to deliver production-ready, AI-powered solutions. Own portfolio delivery, coach and develop talent, act as player‑coach on engagements, drive engineering excellence and responsible AI practices, and partner with leaders to ensure operational health and reusable accelerators.

Design and build data models and pipelines to produce research-ready multi-modal surgical datasets. Drive data collection and quality frameworks, manage annotation workflows, partner with AI/ML researchers, build researcher tooling, and collaborate across clinical, robotics, and systems teams to support embodied AI research.

As a Data Science and Machine Learning Software Engineer at NetApp, you will develop algorithms for real-time personalized search and recommendation using IoT data, while working on cloud-based services and analytics.

Perform data analysis, build statistical models, and manage machine learning pipelines for product experiments, focusing on large datasets.