Data Science Jobs in San Jose, 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.

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

Analyzes workforce and healthcare data to identify trends, build predictive and machine learning models, and deliver actionable recommendations. Uses Python, Alteryx, and Power BI for analytics, visualization, and reporting. The role supports workforce strategy, employee engagement, organizational effectiveness, talent management, and healthcare cost insights. Responsibilities also include client collaboration, relationship management, team guidance, and applying risk adjustment and medical economics concepts to inform strategic decisions.

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.

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 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.

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.

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.

Supports data science projects by preparing and validating datasets, maintaining metadata, conducting exploratory analysis, developing and validating analytical models, researching data science solutions, and visualizing model insights. Works with team members to provide analysis and recommendations for business decision-making. The role requires a bachelor’s degree in data science, statistics, operations research, computer science, or an equivalent field, and typically targets candidates with 0–2 years of experience.

Manage and grow NVIDIA's networking business with Oracle by identifying opportunities, qualifying solutions with engineering and PM teams, driving forecasting and NPI, acting as the customer advocate, and supporting deployment of networking products in cloud and data centers.

Own strategy and roadmap for content conversion across Roku, drive conversion metrics, optimize supporting media and watch options, lead end-to-end A/B experimentation, identify and close conversion gaps with qualitative and quantitative signals, and collaborate with engineering, design, data science, and monetization while communicating progress and trade-offs to leadership.

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

Leads supply chain data science and AI operationalization for Global Service Operations. Converts manual analytics into governed, automated AI-assisted workflows, taking solutions from prototypes to production with lineage, monitoring, lifecycle management, and ownership. Manages analysts, delivers against SLAs, develops statistical and machine-learning models, and communicates actionable insights across procurement, fulfillment, reverse logistics, inventory, and supplier operations. Measures cycle time, quality, and business impact while mentoring the team and advancing enterprise AI governance.