Top Remote Data Science Jobs in Santa Cruz, CA

Build and deploy production machine-learning models for member identification, personalization, provider search, and healthcare AI tools. Lead projects from problem framing and prototyping through product handoff, design experimentation and causal analysis, define product metrics, and develop reliable feature pipelines and experiment infrastructure. Partner with product, engineering, care operations, marketing, and analytics engineering while communicating recommendations clearly and ensuring models are monitored, tested, and impactful.

Own marketing data science strategy, including campaign experimentation, causal inference, multi-touch attribution, monitoring, and predictive models for LTV, drop-off, segmentation, propensity, and personalization. Build reusable data models and semantic-layer assets, ensure analytical rigor, and partner with marketing and data teams to guide campaign decisions. Work remotely with occasional travel.

Develop and deploy machine learning, AI, causal inference, and statistical solutions for advertising measurement products. Build prototypes, conduct experiments, analyze large-scale data, and advance attribution capabilities from early concepts through production. Collaborate with engineering, product, analysts, and account teams to improve measurement and marketing ROI. Responsibilities include owning measurement products, applying Bayesian and marketing models, writing production code, and communicating methodologies and findings to technical and nontechnical audiences.

Develop AI and machine learning solutions for fraud, anomaly detection, semantic search, entity resolution, and investigative analytics. Build and operationalize models across structured and unstructured data using AWS data lakehouse services, generative AI, NLP, graph analytics, and document intelligence. Evaluate model performance, explainability, monitoring, and risk scoring while supporting production integration, dashboards, and investigative workflows. Collaborate with technical teams, investigators, stakeholders, and federal clients in an Agile environment.

Develop, train, evaluate, deploy, and monitor machine learning and statistical models using Databricks, Python, SQL, Spark, Delta Lake, and MLflow. Prepare datasets, engineer features, manage model lifecycles, validate data, and support governance practices. Translate client requirements into scalable analytics solutions, analyze trends, and communicate findings to technical and non-technical stakeholders while collaborating across data science, engineering, cloud, security, and business teams.

Lead growth data science initiatives by partnering with Marketing, Sales, and Customer Experience leaders. Develop analyses, experimentation frameworks, and machine learning models for acquisition, engagement, retention, monetization, targeting, segmentation, uplift modeling, and lifetime value prediction. Productionize reliable models with engineering teams, communicate insights to senior stakeholders, establish experimentation best practices, and mentor junior data scientists. The role requires strong business judgment, advanced technical expertise, and experience operating in ambiguous, zero-to-one environments.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Lead the development of statistical and machine learning models for time-series and geospatial data. Build analytic solutions, datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and reports. Evaluate algorithm performance, optimize classifiers, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders to deliver reliable mission-focused software and data capabilities.

Develops statistical and machine learning models for kinematic behavior detection using live, historical, time-series, and geospatial data. Responsibilities include data integration, ETL, feature engineering, forecasting, anomaly detection, algorithm evaluation, simulation, visualization, reporting, documentation, and stakeholder collaboration. The role also contributes to mission-focused software applications, APIs, data workflows, code reviews, troubleshooting, and agile development in a remote-first environment.

Lead a small data science team while remaining hands-on in building advertising audience, measurement, ranking, and data-feed models. Set technical direction, apply causal inference and experimental design, build scalable production systems, mentor scientists, and partner with product, engineering, sales, and business development teams. Translate campaign data into trusted measurement products, influence senior leaders and customers, and establish Fetch’s methodology as a market standard.

Lead end-to-end data science for customer-embedded, production solutions: acquire and prepare messy datasets, develop and evaluate models tied to business outcomes, run experiments, deploy operational models at scale, coach cross-disciplinary teams, and drive strategy, ethics, and privacy discussions.

Build reliability and quality models for consumer hardware, analyze manufacturing and telemetry data, develop predictive and decision-support systems, and translate findings into product, quality, firmware, warranty, and supply-chain decisions. The role combines statistical modeling, machine learning, experimentation, data engineering, and cross-functional technical leadership.

Own analytical relationships with travel technology partners, creating scalable reporting on supply, pricing, and performance. Conduct exploratory analysis, apply machine learning and statistical methods, build dashboards, automate recurring workflows with AI tools, and translate complex findings into recommendations for product, engineering, commercial, and executive audiences.

Leads data science research from question framing through modeling, validation, and recommendations. Partners with executives, business leaders, ML engineers, and data engineers to identify opportunities in risk, fraud, retention, pricing, and compliance. Builds forecasts and production-ready models, evaluates data as a strategic asset, and translates insights into product features, offerings, and measurable business outcomes.

Develops predictive models, machine learning solutions, data pipelines, statistical analyses, and internal data applications. The role uses Python and SQL Server to analyze structured and unstructured data, applying NLP, classification, regression, clustering, automation, and AI/LLM techniques. Responsibilities include maintaining data science platforms, supporting cross-functional decision-making, and communicating technical findings to non-technical audiences.

Unpaid remote internship focused on data extraction, scraping, mining, processing, cleaning, pipeline development, AI model training, and containerized microservices. The intern will use Django, Puppeteer, Selenium, Docker, Pandas, NumPy, Scikit-learn, Git, and GitHub while applying testing and data engineering practices. The role requires at least 30 hours weekly and may convert to full-time employment.

Leads development and deployment of statistical, machine learning, and causal inference models for customer decisioning. Designs experiments, estimates treatment effects and incremental value, evaluates targeting performance, and integrates analytical solutions into governed production systems. Partners with business and technology teams, communicates complex findings, and continuously improves models using experimental and operational results.

Build and scale first-line risk control monitoring systems using statistical analysis, automated workflows, and reusable data science modules. Define risk metrics, investigate control performance, establish thresholds and guardrails, and translate analyses into monitoring applications. Partner with risk, compliance, product, and engineering teams to improve governance, identify monitoring gaps, and support decision-making. Lead ambiguous, cross-functional initiatives while communicating findings, limitations, and recommendations to technical and executive stakeholders.

Build and scale first-line risk control performance monitoring for financial products. Develop metrics, key risk indicators, statistical baselines, thresholds, automated workflows, and reusable analytical modules. Partner with risk, compliance, product, and engineering teams to improve governance, identify monitoring gaps, evaluate control effectiveness, and support decisions. Lead ambiguous initiatives from definition through adoption while communicating findings, limitations, and recommendations to technical and executive stakeholders.

Early-career Data Scientist supporting supply chain transformation through machine learning, forecasting, statistical modeling, data preparation, dashboards, and AI solutions. The role addresses inventory optimization, demand forecasting, production planning, delivery performance, and cost reduction. Responsibilities include developing analytical datasets and workflows with Python and SQL, evaluating models, integrating enterprise data, supporting cloud-based analytics, and communicating insights to business stakeholders in a manufacturing environment.

Lead a data science team to design, build, and deploy large-scale, real-time AI/ML solutions (forecasting, diagnostics, LLM chatbot, retention models, optimization) to optimize GPU cloud gaming performance and user experience.

Build synthetic data pipelines, evaluation systems, benchmarks, verifiers, rubrics, and quality metrics for legal AI models and agentic workflows. Partner with researchers, legal experts, and customers to investigate model failures, validate metrics, maintain data quality and provenance, and convert expert feedback and product usage into training signals and improved evaluations.

Develop marketing effectiveness analytics using Marketing Mix Modeling, attribution, customer lifetime value, experimentation, and causal inference. Build automated, scalable model pipelines, insight-ready datasets, and self-serve dashboards. Analyze campaigns, support marketing strategy, communicate findings to executives, and lead high-impact projects independently while collaborating cross-functionally.

Own analytical strategy and develop AI/ML models for Zip’s financial products, including cash and rent advances. Lead ambiguous 0-to-1 initiatives using bank transaction data, establish evaluation frameworks, guide risk and growth decisions, and deliver models from experimentation through production. Provide technical direction to data scientists, mentor junior team members, collaborate with engineering, and advise senior stakeholders on model trade-offs. The role requires strong financial services, statistical, research, and machine learning expertise.

Build, train, evaluate, and deploy deep learning models for credit modeling and loss prediction. Conduct representation learning experiments, integrate models into ML pipelines, and own projects from hypothesis through measurable business outcomes. Collaborate with data scientists, engineers, and product partners, communicate findings, and develop agentic tools that accelerate data science workflows.