Top Data Science Jobs in San Luis Obispo, CA

Lead pricing modeling and experimentation to optimize margin, conversion, and retention. Build elasticity and willingness-to-pay models, run causal and bandit experiments, decompose merchant economics, and develop agentic deal tooling. Own end-to-end data pipelines, ETL, monitoring, and visualization. Evaluate AI systems for accuracy, bias, and drift, and partner with cross-functional teams to translate results into pricing strategy and automation.

Own the reliability and quality of an AI copilot for a trading platform. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement workflows. Partner with engineering and product to create safe, auditable tool interactions and improve assistant behavior. Develop domain expertise in trading, risk, margin, execution, and portfolio reasoning while operating across model, backend, and frontend systems.

Develop data science solutions for payments across Square and Cash App. Build AI-powered tools, ETL pipelines, dashboards, machine learning models, experiments, and cloud data systems. Partner with Product, Engineering, Finance, and Operations to improve payment success, cost efficiency, risk mitigation, and decision-making. Translate business needs into practical solutions, research questions independently, and operationalize data processes and infrastructure.

Develop data tools, AI automations, ETL pipelines, dashboards, and machine learning models for payments. Analyze experiments, optimize payment performance and costs, mitigate risk, maintain data systems and cloud infrastructure, and translate business needs into actionable solutions while partnering with Product, Engineering, Finance, and Operations.

Own product analytics for a healthcare platform by translating business questions into research plans, uncovering insights, modeling feature impacts, defining success criteria, and leading A/B testing. Partner with Product, Data Engineering, Finance, Clinical, and Operations teams to influence roadmap decisions, improve provider experience and quality, and optimize marketplace performance.

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 statistical and machine learning model development for time-series and geospatial data supporting the FALCON mission. Responsibilities include feature engineering, forecasting, anomaly detection, ETL, dataset creation, algorithm evaluation, simulation, data visualization, reporting, documentation, and stakeholder collaboration. The role also contributes to software applications, APIs, data pipelines, code reviews, troubleshooting, and iterative agile development.

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.

Support data-driven decision-making by collecting, cleaning, and analyzing structured and unstructured data, performing EDA, assisting in building and validating ML and statistical models, creating visualizations and dashboards, documenting findings, and collaborating with cross-functional teams.

Develops and deploys analytical and machine learning solutions, leading exploratory data analysis, statistical modeling, feature engineering, scalable data pipelines, validation frameworks, and model optimization. Collaborates with product and engineering teams on proofs of concept and production integration, evaluates emerging technologies, and communicates insights through visualizations and presentations. Requires Python, SQL, cloud or containerized environments, ML pipeline experience, U.S. citizenship, and the ability to obtain and maintain Public Trust clearance.

Lead and build Neural Earth's geospatial data science function: design and deploy Python-based geospatial models, architect production geospatial data pipelines, validate reproducible methods, translate scientific outputs into customer-ready products, and manage and mentor at least one direct report while advising customers and stakeholders.

Develops machine learning models and production data science pipelines to improve subscription conversion, content recommendations, user understanding, workflow automation, and product decisions. Leads analytical projects and experiments, applies statistical methods to web-scale data, and collaborates with data, product, growth, and engineering teams. The role also communicates complex findings to diverse audiences and helps foster a strong data science culture.

Own product-led growth analytics across activation, engagement, conversion, monetization, and retention. Lead experimentation, statistical analysis, causal inference, metric development, and proactive opportunity discovery. Build scalable analytical systems, monitoring frameworks, semantic models, and AI-powered tools. Partner with Product, Engineering, and leadership to shape product bets, investment decisions, and roadmap priorities. Work autonomously in a remote-first environment and communicate complex findings through clear, actionable recommendations.

Build predictive and machine learning models for patient needs, operational forecasting, churn, advocate quality, and risk. Design A/B tests and causal inference studies, apply advanced statistical methods, productionize models with data engineers, and communicate insights to business stakeholders. The role requires Python, advanced SQL, forecasting expertise, statistical proficiency, data privacy awareness, and the ability to work independently in a fast-paced healthcare startup.

Develop measurement frameworks for offline and out-of-home marketing campaigns, including geo holdout tests, matched-market tests, media mix modeling, and incrementality studies. Combine offline spending with digital and product signals to assess marketing effectiveness. Partner with Marketing leadership and Growth Data scientists to select test markets and channels, size investments, align online and offline measurement, and communicate uncertainty clearly.

Lead end-to-end data science and AI projects: translate business problems, prepare large datasets, build and deploy predictive/prescriptive and generative AI models (including LLM fine-tuning and prompt engineering), communicate insights to stakeholders, and promote responsible, explainable AI practices across consulting engagements.

Own product analytics for ElevenLabs products, defining health metrics across activation, adoption, retention, and engagement. Design and analyze A/B tests, feature launches, and onboarding changes. Partner with Product, Growth, Analytics Engineering, and Data teams to convert ambiguous questions into trusted analysis and certified KPI layers. Apply SQL, Python, experimentation, and product analytics expertise to support growth and product decisions.

Develop and deploy LLM and machine learning models for Airbnb Community Support. Responsibilities include automating LLM evaluation, curating synthetic datasets, analyzing customer issues, identifying model failure modes, building personalization models, exploring data, prototyping solutions, and communicating insights to cross-functional stakeholders. The role focuses on scalable, production-ready AI products and operational optimization.

Evaluate migration technologies, build quantitative models for migration risk and data quality, coordinate customer migration projects, develop data pipelines and analytical frameworks, implement Python and SQL validation workflows, assess cloud environments, and create dashboards and reporting for product, sales, and operations teams.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

Manage NASA aerospace portfolio data through cataloging, metadata standards, taxonomies, controlled vocabularies, data lineage, provenance, classification, and quality documentation. Evaluate and implement an enterprise data catalog, integrate it with Dagster, establish metadata and freshness metrics, onboard and retire datasets, and drive adoption across technical and program teams. Translate between data professionals and program stakeholders while improving data discoverability, trust, and usability.

Lead the design, development, deployment, monitoring, and improvement of interconnected customer lifetime value models covering conversion, retention, premiums, and claim losses. Serve as a hands-on technical leader, guide modeling strategy, evaluate experiments and business impact, partner with engineering and business teams, support production ML systems, establish technical standards, and coach other data scientists.

Lead client engagements from data onboarding through production, plan studies that demonstrate business value, and build automated machine learning workflows using Python, scikit-learn, pandas, and Spark. Translate client business questions into data solutions, manage stakeholder relationships, evaluate model performance, and inform product planning. Requires healthcare or life sciences data experience and end-to-end delivery of sophisticated data or machine learning solutions.