Data Scientist

• Design and implement customized AI solutions for manufacturing
• Analyze manufacturing data to uncover opportunities and develop AI models
• Collaborate with customers to understand requirements and deliver clear, data-driven solutions
• Work with internal teams to ensure solutions are feasible, scalable, and aligned with product strategy
• Present findings to technical and non-technical stakeholders
• Lead development of state-of-the-art AI applications addressing manufacturing challenges
• Mine, interpret, and analyze customer data to identify opportunities
• Develop tailored data models and tools for customer needs
• Collaborate cross-functionally to design and develop AI products and identify new product features

Senior Data Scientist – Manufacturing Data

• Develop, optimize, and implement marketing strategies for the client's credit card business
• Drive growth of business and member products through data analysis, model creation, strategy development, and performance monitoring across channels
• Collaborate with marketing managers, channel owners, business leads, and external partners on new opportunities and initiatives
• Analyze marketing strategy effectiveness, including funnel efficiency, CPA, and ROI, and provide data-driven recommendations
• Design, analyze, and interpret A/B tests and quasi-experiments
• Design and validate campaign lifecycles, including process flow, data pipeline, model scoring, execution, campaign selection, and file ingestion
• Define KPIs, create reporting frameworks, and develop self-service dashboards
• Manage end-to-end direct mail campaigns, including prescreened and invitation-to-apply campaigns
• Perform deep-dive analyses across funnels, user behavior, monetization, and retention
• Translate business objectives into actionable data-driven tactics and campaigns
• Fulfill partner analysis requests and navigate ambiguity with solution-oriented approaches
• Communicate insights and data narratives to senior leadership and partners

Senior Marketing Data Scientist

• Source and validate relevant data from multiple business domains
• Develop and test advanced analytical models using statistical and machine learning techniques
• Perform exploratory data analysis and feature engineering
• Collaborate with stakeholders to translate business requirements into data-driven solutions
• Create data visualizations and presentations to communicate findings and influence decisions
• Mentor junior team members on analytical methods, coding best practices, and data storytelling
• Leverage in-house agentic and generative AI tools to enable partner self-service and improve efficiency
• Guide analytical strategy and communicate findings to support business objectives and continuous improvement
• Collaborate with product management, business strategy, engineering, and design teams across eCommerce business areas

Senior Data Scientist

• Identify suitable data sources and perform initial data quality checks
• Apply data strategy, data management, data quality, and data governance principles
• Support deployment, evaluation, scalability, and sustainability of analytical models
• Develop and test code using SQL, Java, C, Python, and other recommended languages
• Assess and validate models using fit, tuning, and validation techniques
• Create data visualizations using tools such as Python, R, GGplot, Matplotlib, Plotly, Tableau, and Power BI
• Present insights to business and stakeholder audiences and tailor communication to stakeholders
• Guide and mentor junior associates on visualization, modeling, and analytics techniques
• Translate business problems and requirements into analytical or mathematical solutions
• Conduct exploratory data analysis, hypothesis testing, statistical inference, trend analysis, and cluster analysis
• Develop custom analytical models and provide business recommendations and insights
• Build stakeholder relationships, implement business solutions, monitor results, and identify improvement opportunities
• Support Walmart’s mission, values, ethics, compliance, and continuous improvement objectives

• Lead, coach, and develop a high-performing team of Product Analysts supporting Data Ventures & Advertising
• Advise Product, Engineering, and Business teams on data-informed product strategy and decision-making
• Identify product opportunities through customer insights, hypothesis-driven analysis, and product performance deep dives
• Define product KPIs, measurement strategies, and event instrumentation requirements
• Design, execute, and analyze A/B tests using statistical methods
• Build scalable dashboards, reporting, and self-service analytics solutions
• Leverage clickstream and behavioral data to improve customer experience and influence product roadmaps
• Deliver actionable insights and strategic recommendations that drive product innovation and business outcomes
• Lead measurement, insights, and experimentation for Data Ventures & Advertising products

Senior Manager, Product Management – Product Analytics

• Apply machine learning and artificial intelligence to analyze large datasets and derive insights
• Lead diverse data projects and provide insights and recommendations for data-driven decisions
• Apply statistical rigor, metric alignment, transparency, and effectiveness to decision-making
• Help refine recommendation models to elevate user experiences
• Identify strategies to optimize new and returning user experiences
• Evaluate experimental designs, including pre-test planning, in-flight monitoring, and post-experiment analysis for graduation decisions
• Collaborate with cross-functional teams to build pipelines and tools and make business-impacting decisions

• Define and own Vapi's core metric set and build the canonical data model
• Make the GTM funnel measurable from signup and first call through activation, qualification, PoC, closed won, adoption, expansion, and retention
• Own data, dashboards, and narratives for weekly and monthly business reviews and instrument OKRs
• Build and scale the modern data stack, including warehouse, ingestion, transformation, orchestration, and BI
• Ship documented and tested dbt models and enforce data contracts
• Design pipelines for call telemetry, transcript events, usage metering, billing signals, and model performance traces
• Set freshness SLAs, alerting, and on-call coverage
• Build AI/LLM monitoring, model telemetry, and prompt-to-performance observability
• Build canonical and semantic layers and function-specific self-serve dashboards
• Own governance, data quality, definitions, dictionary, lineage, and access controls
• Partner with Security and Legal on retention, residency, PII, voice-data handling, HIPAA, GDPR, CCPA, and customer DPAs
• Support pricing, market expansion, product decisions, customer health, revenue recognition, usage-based billing, investor reporting, pipeline health, territory design, and retention modeling
• Plan headcount, hire the first data engineers and analysts, set quality standards, make build-versus-buy decisions, and manage vendor relationships
• Success milestones include publishing metric definitions and a roadmap in 30 days, canonical ARR/NRR/billable-call models in 90 days, end-to-end GTM measurement and self-serve dashboards in six months, and trusted company-wide data within one year

Head of Data

• Build, maintain, and enhance enterprise spend intelligence models across electrical commodities
• Develop and maintain statistical forecasting models for procurement spend
• Perform spend classification, taxonomy alignment, and anomaly detection across ERP, invoicing, and purchase order datasets
• Identify cost-saving opportunities, leakage, rebate capture, and volume aggregation points
• Develop predictive price models and index-tracking algorithms for volatile electrical raw materials
• Analyze tariff impacts, market surcharges, and macroeconomic trends
• Design and deploy automated supplier scorecards
• Model supplier financial health, capacity constraints, and risk profiles
• Analyze transactional procurement workflows to identify bottlenecks and cycle-time reduction opportunities
• Partner with IT and systems teams to integrate machine learning algorithms into enterprise procurement tools
• Track inventory recirculations, warehouse pilot metrics, and excess material usage
• Design, build, and maintain interactive dashboards and reports for procurement and executive leadership
• Build predictive TCO and asset depreciation models for vehicles and heavy equipment
• Translate statistical findings into executive summaries, strategic recommendations, and field guidance
• Serve as the primary analytics subject matter expert within the Procurement organization

Data Scientist II

• Lead the full lifecycle of fraud detection features and models, from ideation and data exploration to prototyping, productionizing, and monitoring.
• Develop predictive features from large-scale, multi-dimensional data, including user behavior, device intelligence, network graphs, and transaction records.
• Process massive, noisy, and imbalanced datasets using Spark, SQL, and the proprietary AI platform.
• Leverage agentic AI to automate analytic pipelines and develop reusable tools for fraud investigation, feature generation, and reporting.
• Lead investigations into complex fraud cases across identities, accounts, devices, and transaction surfaces.
• Reconstruct attacker sequences and hypothesize actor intent and tooling.
• Produce evidence-backed technical reports and case studies for product, engineering, operations, legal, and executive stakeholders.
• Generate customer-facing fraud trend reports by synthesizing case-level findings and aggregate data.

Senior Data Scientist – Fraud Detection

• Architect and implement large-scale data pipelines supporting model training and research workflows for text, image, audio, and video datasets
• Partner with research and engineering teams to curate, clean, and manage diverse sensory-rich datasets for pre-training and mid-training of multimodal models
• Develop strategies and tools for scalable data ingestion, labeling, filtering, augmentation, and storage
• Ensure data quality, reliability, and compliance, including privacy and ethical considerations throughout the data lifecycle
• Optimize data processing, transformation, and delivery for large-scale distributed training pipelines
• Prototype and productionize methods for dataset creation, management, and continuous improvement
• Integrate research-driven data advancements into production-ready systems
• Stay informed about emerging data engineering and ML data management developments and apply best practices

Data Scientist jobs in California

Senior Data Scientist – Manufacturing Data

Senior Marketing Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Manager, Product Management – Product Analytics

Data Scientist

Head of Data

Data Scientist II

Senior Data Scientist – Fraud Detection

Research Scientist, Data