Data Science Jobs in Santa Clara, CA

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.

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

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.

Define value propositions, positioning, messaging, and GTM for CoreWeave's AI researcher and developer tools. Drive product launches, sales enablement, customer advocacy, market insights, and metrics-driven measurement in close partnership with Product, Engineering, Sales, and Marketing teams.

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.

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.

Lead development of next-generation entity resolution ML solutions using transformers, graph ML, and large-scale data pipelines. Partner with cross-functional teams to design, train, validate, and deploy models on cloud platforms, leverage agentic AI workflows, and translate technical work into business outcomes.

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.

Lead the Seller Risk product organization to define vision, strategy, and roadmap for onboarding, identity verification, lifecycle monitoring, and enforcement. Build ML- and graph-powered risk detection and decisioning platforms, mentor product managers, partner with engineering, data science, and compliance, and drive metrics like fraud loss, false positives, and decision speed while anticipating emerging risk and regulatory changes.

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.

Install, qualify, maintain, and optimize photolithography equipment in a semiconductor fab. Drive yield, uptime, and process stability; troubleshoot and repair tools; partner with process engineers; manage vendors and tool metrics; write purchase requisitions; implement TPM; work in a cleanroom and follow EHS and ISO quality standards.

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

Lead product strategy and roadmap for PayPal's global P2P payments. Use data-driven insights and AI/ML to optimize web and mobile user experiences, run A/B experiments, prioritize features, and manage international launches. Collaborate with engineering, design, data science, legal/privacy/compliance, and stakeholders to balance customer needs, growth, monetization, and regulatory requirements while communicating results to senior leadership.

As a Manager in Data Science, you'll lead the development of machine learning models, partner with engineering and product teams, and analyze large datasets to enhance customer experiences and prevent fraud.

Maintain and improve analytics solutions and financial/marketing models (primarily in Excel); perform ETL/data wrangling; analyze market and business data; communicate concise insights to stakeholders; drive data-informed strategic decisions and build production-ready data products or pipelines.

The role involves leading data science initiatives, building machine learning models, conducting experiments, and collaborating with teams to enhance customer experiences and business outcomes.

The Product Development Engineer drives testability and manufacturability of circuits, performs yield analysis using data science techniques, and interfaces with various engineering teams to optimize silicon yield and quality.

As a founding Data Scientist/Machine Learning Engineer, you'll develop AI/ML models, enhance product capability, and drive impactful user outcomes while working closely with product teams.

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.

The Market Intelligence Manager will drive strategic analysis, support CEO initiatives, and deliver business insights by analyzing market data and trends.

The Engineering Leader will oversee software and data teams, ensuring product delivery and operational excellence in robotics and IoT systems.

Lead and grow a homeowners insurance modeling team to build by-peril loss models, demand and retention analyses, and weather/catastrophe models. Develop reusable Python/SQL tooling and implement models in an Airflow-based pipeline, partnering with Pricing, Underwriting, Claims, Product, and Data Engineering to deploy and translate insights into business recommendations.

The role involves designing and developing data applications, working with large data sets, and requires expertise with various data processing technologies. Candidates should have strong analytical skills and experience in prototyping software applications.