Top Remote Data Science Jobs in Los Angeles, CA

Own the reliability and quality of an AI copilot for trading platforms. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement loops. Partner with engineering and product on safe tool interactions, auditability, and user trust. Develop trading-domain expertise and measurement systems covering correctness, safety, latency, regression risk, and workflow performance.

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.

Performs data science projects involving statistical modeling, machine learning, forecasting, data cleaning, exploratory analysis, and AI-assisted workflows. Builds and maintains models, pipelines, reporting, and analytics-ready datasets using Python, SQL, Snowflake, and dbt. Supports validation, documentation, testing, governance, and stakeholder communication while developing proficiency in modern data science tools and deployment practices.

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 and deploy statistical, AI, and machine learning models for demand forecasting, inventory optimization, and Sales, Inventory, and Operations Planning. Analyze forecast accuracy, develop scenarios, manage data pipelines, create KPI reporting, and translate insights into operational recommendations. Partner cross-functionally with Sales, Marketing, Engineering, Purchasing, Production, Finance, and business units to support rolling 12–24-month planning and continuous process improvement.

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.

Develop and deploy data science solutions for pharmacy benefits and member experience, including predictive models, generative AI pipelines, and rules-based logic. Extract and analyze large-scale data with Python and SQL, manage model lifecycles, monitor production performance, and communicate insights to clinical and business leaders. Collaborate with data scientists, engineers, and operational stakeholders to solve complex healthcare and PBM problems.

Develop marketplace intelligence, forecasting, ranking, recommendation, personalization, and experimentation models for Airbnb’s search and marketplace products. Partner with product and engineering teams to launch AI-driven improvements, build scalable modeling pipelines, evaluate product changes, and guide technical direction. The role requires causal inference, machine learning, strong SQL and Python or R skills, cross-functional communication, and autonomous leadership of data science roadmaps.

Develop machine learning and statistical models to identify, measure, and manage credit and fraud risk. Partner with stakeholders to translate requirements into scalable solutions, analyze large application and transaction datasets, deploy end-to-end models, and improve risk decision-making. The role requires advanced Python or R and SQL skills, expertise in fraud and underwriting models, model deployment, statistical methods, and mentoring junior data scientists.

Build and deploy statistical and machine-learning models for forecasting, risk prediction, retention, no-show prediction, experimentation, causal inference, and clinical outcomes. Partner with analytics engineering and technology teams to productionize models using monitored data pipelines. Translate ambiguous clinical and operational questions into rigorous analyses, communicate recommendations to cross-functional stakeholders, and establish data science and responsible AI practices.

Supports the design, validation, and operationalization of predictive, machine learning, and AI models. Analyzes structured and unstructured data, develops algorithms, validates outcomes, creates visualizations, and communicates findings to stakeholders. Collaborates cross-functionally to integrate solutions and prepares model pipelines for business use. The role also involves statistical analysis, NLP, exploratory data analysis, feature engineering, and research into emerging technologies.

Partner with product, engineering, and business teams to analyze player behavior, product performance, growth opportunities, and investment decisions. Apply experimentation, forecasting, behavioral analytics, and statistical methods to improve acquisition, engagement, retention, monetization, and lifecycle outcomes. Build reusable analytical tools, influence product roadmaps, define performance metrics, and collaborate with data engineering on telemetry, data quality, and measurement infrastructure.

Develop and maintain enterprise databases, data models, metadata frameworks, governance processes, ETL workflows, and data quality systems. Perform forecasting, scenario analysis, and risk assessment using complex datasets. Build automation tools, support data integration, recommend improvements, and collaborate with technical and nontechnical stakeholders. The role supports U.S. Navy data systems and requires active Secret clearance.

Build and own marketing measurement frameworks using media mix modeling, incrementality testing, and multi-touch attribution. Design experiments, conduct power analyses, validate models, reconcile measurement sources, and translate findings into client budget recommendations. Clean and integrate advertising, analytics, and commerce data, develop reusable code and templates, present statistical results to technical and nontechnical audiences, and support multiple clients across varied measurement environments.

Leads data science strategy for a federal healthcare data platform. Designs data collection, validation, and analysis tools; develops predictive models; manipulates large datasets; and collaborates with scientists, trainees, and IT teams on cloud, interoperability, integration, security, and compliance solutions. Communicates technical recommendations to varied stakeholders and supports federal data security, ATO, and information management requirements.

Develop and deploy statistical and machine learning models, conduct exploratory analysis, forecasting, segmentation, experimentation, and AI initiatives. Translate business needs into analytical frameworks, KPIs, predictive insights, and recommendations. Collaborate with data engineering, BI, QA, and business teams to ensure data readiness, model integration, quality, governance, and auditability. Support revenue, marketing, financial, and operational decision-making through scalable analytical solutions.

The Data Scientist will transform real-world, clinical, and preclinical healthcare data into reproducible datasets, analyses, and evidence supporting medical-device research and regulatory needs. Responsibilities include cohort development, data-quality validation, statistical programming, study design support, analysis plans, clinical and post-market analyses, documentation, and communicating findings to technical and nontechnical stakeholders. The role collaborates with Scientific, Clinical, R&D, Regulatory, Quality, IT, and external data partners.

Conduct actuarial and predictive analysis supporting product development, rating, reserving, pricing, financial analysis, and process improvements. Develop predictive models, multivariate analyses, tools, workflows, and scalable data processes. Collaborate with actuaries, product managers, vendors, and business units to translate complex data into strategic business solutions while ensuring accuracy and regulatory compliance.

Evaluates AI-generated data science and quantitative reasoning outputs; develops prompts, datasets, reference solutions, and objective evaluation benchmarks. Identifies statistical errors, flawed methodologies, and unsupported conclusions while providing structured feedback to improve model accuracy. Responsibilities include dataset validation, statistical and quantitative assessment, quality assurance, technical documentation, and clear communication of analytical findings.

Designs challenging data science and machine learning tasks, builds reproducible environments and verified reference solutions, and develops automated tests and evaluation frameworks for AI-generated code. The role analyzes AI agent performance, identifies bugs, leakage, overfitting, and methodological weaknesses, documents technical requirements, and iteratively improves task reliability and complexity.

Develop and implement data science strategies for machine learning applications including lead scoring, churn modeling, pricing, personalization, and fraud detection. Own end-to-end initiatives spanning data analysis, feature engineering, model training, inference, and monitoring. Collaborate with engineering, infrastructure, analytics, and business teams to deliver measurable impact, improve data capabilities, and communicate findings to technical and non-technical 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.

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.