Data Science Jobs in Santa Clara, CA

Leads a team of Product Sales Specialists responsible for product adoption, cross-sell, expansion, pipeline, and revenue outcomes. Coaches consultative selling, manages forecasting and performance, develops scalable sales plays, and partners with Account Executives, Account Managers, Product, Marketing, Operations, Data Science, and Enablement. The role also engages in strategic seller opportunities, improves product expertise across sales teams, and builds repeatable commercial motions for Square’s products.

The Group Product Manager for AV Capabilities at GM leads the strategy and roadmap for autonomy products, managing a team while collaborating cross-functionally to ensure quality delivery and innovation.

As a Staff Machine Learning Engineer, you will enhance credit modeling, manage full credit modeling stacks, and support innovations within regulated banking frameworks.

The Senior Product Manager for Evaluation Applications will define and implement strategies for a testing platform, ensuring cohesive workflows. This role requires strong technical leadership, stakeholder alignment, and a focus on data-driven insights for autonomous vehicle evaluation.

Lead a data science team building scalable AI and machine learning solutions for customer-facing financial products. Develop and fine-tune NLP and large language models, manage model training, evaluation, validation, and production deployment, and partner with engineering, product, and design teams. Apply emerging generative AI technologies at scale while mentoring talent, translating technical findings into business outcomes, and delivering resilient systems for millions of customers.

Lead data science for AI Foundations, developing and deploying large-scale NLP, machine learning, and generative AI solutions. Partner with software engineering, ML engineering, product, and design teams to build customer-facing applications. Train, fine-tune, evaluate, and operationalize language models and other advanced models using technologies including PyTorch, AWS, Hugging Face, LangChain, and VectorDBs. Provide technical leadership, communicate complex findings, and develop team talent.

Teach undergraduate and graduate information systems and analytics courses, including course preparation, student assessment, grading, advising, and office hours. Participate in departmental, university, professional, and community service, contribute to student learning assessment, and maintain professional activity through scholarship, conferences, pedagogy, or industry practice.

The Senior Director of Engineering leads engineering strategy, mentors teams, oversees project delivery, collaborates with stakeholders, and champions best practices for operational excellence.

Lead product strategy and roadmap for threat detection content across endpoint, cloud, and related domains. Partner with research, data science, engineering, marketing, sales, and support to deliver detections, competitive analysis, go-to-market plans, and AI-enhanced defenses. Drive cross-functional collaboration to prioritize features, influence product direction, and support customers and releases.

Lead product definition and roadmap for internal web tools that surface and audit autonomous vehicle log events. Partner with engineers, data scientists, safety, and operations to translate complex AV workflows into requirements, prioritize features, improve reviewer productivity, define success metrics, and deliver scalable, reliable visualization and triage tooling.

Lead and grow a Data Science team for Provider Products, set analytical direction, own experimentation and measurement, review methods, partner with product and engineering, hire and develop data scientists, standardize analytics and integrate generative AI to improve workflows and outputs.

Lead and build a greenfield marketing measurement function across B2C and B2B. Own attribution, incrementality, MMM, MTA, LTV, and experimentation; hire and mentor data scientists; partner with marketing leadership to turn statistical results into investment decisions; integrate measurement with data platform and GenAI tools.

Lead a team to deliver AI powered products, leveraging machine learning and NLP for customer applications, while collaborating with cross-functional teams.

The role involves leading a team to develop AI and ML solutions, manage customer interactions, and analyze large data sets using various technologies.

Leads technical, process, and application initiatives supporting supply chain, planning, manufacturing, quality, and service operations. Responsibilities include defining requirements and roadmaps, managing scope, risks, resources, schedules, and multiple concurrent projects; improving and reengineering business processes; coordinating engineering teams; developing business use cases; and communicating decisions and progress across cross-functional stakeholders. The role requires supply chain expertise, technical program leadership, and experience with IT applications, infrastructure, data analytics, and Agile Scrum delivery.

Lead research and development of emerging ML (embeddings, foundation models, transformers). Build and productionize machine learning models at scale, partner with product and engineering, analyze large-scale transactional and clickstream data, and run experiments to improve customer experiences across marketing, servicing, and fraud domains.

Lead end-to-end product development for SaMD neurodiagnostics (EEG) products. Define vision, write requirements and acceptance criteria, manage backlog and releases, integrate AI features, ensure FDA and global regulatory compliance, drive VOC and competitive analysis, and coordinate cross-functional execution from design through verification and launch.

Lead cross-functional planning and execution for the IR Data Product team, managing schedules, risks, dependencies, releases, and resource planning for data processing pipelines. Coordinate software, hardware, science, and product teams; provide reporting, process improvements, and ensure delivery readiness in a hybrid San Jose environment.

The Sr. Staff Software Engineer, Automation will develop tools for automating reliability testing and analysis, collaborating with various teams to optimize workflows and improve product reliability.

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.

Ten-week, full-time paid Data Science internship (June–August 2027) working on machine learning, deep learning, and large-scale data analysis. Interns evaluate modeling tools, integrate internal and external data/APIs, design data science models, and present insights to stakeholders. In-person attendance at an assigned U.S. location is required; interns may be considered for full-time roles after graduation.

Ten-week, full-time paid Data Science internship for current PhD students. Interns will build and evaluate modeling and analytics tools, integrate internal and external data sources, design data science models, and communicate results. Candidates must continue their PhD after the internship and may be evaluated for full-time roles upon graduation.

Join Hiring.Cafe as a Founding Machine Learning/AI Search Engineer to develop AI recommender systems and scalable infrastructure, contributing to a transformative job search engine.

Analyzes business and data requirements, converts them into machine learning and generative AI problems, integrates data from multiple sources, builds data models and dashboards, performs statistical analysis, develops predictive and AI models, and benchmarks model improvements. The role requires Python expertise, knowledge of data science and visualization tools, generative AI technologies, and relational and non-relational databases.

As an Automation Engineer, you'll build automation pipelines, collaborate on experimental needs, program instrumentation, and integrate with data science, enhancing research capabilities.