Top Data Science Jobs in Los Angeles, CA

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

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 predictive failure models, real-time anomaly detection systems, diagnostic tools, and operational dashboards for spacecraft manufacturing, testing, and integration. Apply statistical modeling, causal inference, reliability analysis, and data mining to identify root causes, bottlenecks, quality issues, supplier risks, and process improvements. Develop reproducible Python and SQL analyses that support engineering decisions and mission reliability.

Build predictive models, anomaly detection systems, diagnostic tools, dashboards, and statistical quality-monitoring solutions for spacecraft manufacturing and testing. Analyze telemetry, production, supplier, and reliability data to identify failures, bottlenecks, delays, and root causes. Develop reproducible Python and SQL analyses and communicate actionable findings to engineering and operations stakeholders.

Perform quantitative analysis, data mining, and statistical modeling to generate actionable product and business insights. Design and track core metrics, build dashboards and visuals, collaborate with cross-functional teams, use AI tools to scale analyses, and drive projects to completion to inform product decisions.

The role involves analyzing fraud and risk data, defining KPIs, collaborating on anti-fraud strategies, and creating tools and dashboards for data accessibility at Whatnot.

Leads data engineering for the Sentinel program by developing, deploying, and maintaining production ETL/ELT pipelines from structured and unstructured sources. Oversees analytics data strategy, storage, governance, and quality; partners with stakeholders and visualization specialists; mentors junior engineers; conducts code reviews; and establishes engineering best practices for organization-wide analytics and decision-making.

Supports the U.S. Space Force by analyzing model-based systems engineering data, extracting and transforming structured and semi-structured information, and developing scripts, dashboards, visualizations, reports, and data collection frameworks. The role requires Python, Java, SQL, and dashboarding experience, collaboration with systems engineers, on-site work in El Segundo, and an active TS/SCI clearance with SAP eligibility.

Develops advanced analytics, dashboards, data visualization tools, and automated data solutions supporting operational strategy and execution. Leads data quality initiatives, performance reviews, continuous improvement efforts, and cross-functional analytics projects across program management, engineering, and manufacturing. Translates complex data into actionable insights, coaches stakeholders on data literacy, and supports data-driven decision-making across Southern California facilities.

The Senior Data Scientist supports Customer Success through deep-dive analysis, predictive modeling, causal inference, experimentation, and KPI development. The role analyzes churn, retention, renewals, upsell opportunities, and revenue trends; diagnoses metric shifts; builds data pipelines and dashboards; and develops AI/LLM-powered solutions. The scientist collaborates with stakeholders, communicates insights, improves data quality, and measures the impact of customer success initiatives and product changes.

Lead development of statistical and machine learning models for time-series, geospatial, kinematic, and radar data. Build analytic solutions, training datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and performance metrics. Integrate multi-source sensor data, enhance algorithms, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders in an agile environment.

Analyze marketing and business performance using SQL, statistics, econometric modeling, Bayesian regression, causal inference, and A/B testing. Build marketing measurement models, integrate customer and event data, develop reporting infrastructure and dashboards, evaluate marketing spend, and provide actionable recommendations to optimize growth. Partner with Marketing, Product, Engineering, Finance, and executives across multiple brands.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

Analyze structured and unstructured data for U.S. Space Force stakeholders. Responsibilities include sourcing datasets, defining KPIs, developing analytical scripts and machine learning models, automating processes, building Tableau or Power BI dashboards, and communicating insights to senior government and military leaders. The role also involves applying generative AI tools and supporting data strategy and decision-making.

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 lead data science and machine learning initiatives, including exploratory analysis, predictive modeling, experimentation, recommendation systems, user classification, model monitoring, and production deployment. Partner with product, marketing, analytics, and engineering teams to convert user insights into automated services. Manage projects throughout their lifecycle, support strategic ML planning, and deliver solutions for personalization, cross-selling, and retargeting.

Consult as a fractional data scientist for hedge funds, private equity firms, and family offices. Lead data science initiatives: build infrastructure, develop and deploy models, create portfolio dashboards, monitor post-deployment performance, and partner with investment teams to turn data into actionable investment insights. Advise on vendor selection, data quality, technology stacks, KPIs, and governance.

Builds a talent network of experienced data scientists for potential future AI evaluation projects. Responsibilities may include creating grading criteria, evaluating AI-generated or human-produced data science work, writing evidence-based justifications, applying consistent judgment, and incorporating reviewer feedback. Candidates should have professional data science experience, strong Python and SQL skills, expertise in statistical modeling, machine learning, experimentation, causal inference, and excellent technical communication.

Develop marketing analytics and data science models to measure campaign effectiveness, ROI, attribution, customer lifetime value, and marketing impact. Apply causal inference, experimentation, and Marketing Mix Modeling; automate model pipelines and monitoring; build insight-ready datasets and self-service dashboards; and communicate actionable findings to marketing and executive stakeholders. The role also supports campaign analysis, data requirements, training, and strategic planning.

Lead end-to-end pricing experimentation and strategy for consumer and business products. Design and analyze A/B tests, build pricing models and production-grade data pipelines, partner cross-functionally, and present actionable recommendations to senior leadership.

Lead product-focused quantitative analyses and machine learning efforts to generate actionable insights, drive product decisions, and optimize features. Collaborate with cross-functional teams to design metrics, build scalable statistical solutions, create dashboards, and communicate findings while using AI tools responsibly.

Develop and deploy machine learning models for manufacturing use cases including predictive quality, machine failure prediction, and time-series pattern discovery. Work directly with customers to scope problems, validate solutions in live deployments, and translate successful analyses into product features. Collaborate with engineering, product, and customer success teams while applying statistics, optimization, generative AI, and agentic AI to deliver end-to-end solutions.

Lead pricing modeling and experimentation to optimize margin, conversion, and retention. Build elasticity and willingness-to-pay models, design A/B and bandit experiments, develop pricing automation (DealBot) and monitoring for AI-driven rate recommendations, own ETL/analysis/visualization, and partner with Finance, Risk, Product, and GTM teams to operationalize pricing intelligence.