Top Data Science Jobs in Los Angeles, CA

Analyze large datasets using quantitative methods, data mining, statistical modeling, and AI tools to generate actionable product and business insights. The role designs and tracks product metrics, builds dashboards and reports, identifies trends and opportunities, and supports data-driven decisions. Responsibilities include collaborating with product managers, engineers, marketers, and designers, applying statistical techniques such as A/B testing and causal analysis, and independently driving projects to completion.

Develop and lead complex AI and data science solutions, shape AI technical strategy, research emerging technologies, resolve large-scale technical problems, and guide cross-functional teams. The role focuses on scalable data solutions, machine learning, deep learning, large language models, generative AI, NLP, computer vision, Python, and Vertex AI. It requires a bachelor's degree and at least five years of progressive data analytics and data science experience.

Owns credit policy development and governance for Square Financial Services, including underwriting strategy, policy experimentation, portfolio monitoring, regulatory documentation, and risk reporting. Designs rigorous champion-challenger tests, randomized holdouts, and staged rollouts while balancing growth, losses, profitability, fair lending, and bank oversight requirements. Partners with Risk, Compliance, Internal Audit, and senior leadership to scale credit products responsibly.

The Data Scientist will use SQL, Python, statistics, machine learning, and AI/LLMs to solve business problems involving customer behavior, marketing effectiveness, conversion, revenue, and forecasting. Responsibilities include data preparation, feature engineering, predictive modeling, experimentation, workflow automation, model deployment and monitoring, and communicating actionable recommendations to technical and business stakeholders.

Develops statistical and machine-learning models for pricing, revenue optimization, demand forecasting, segmentation, and experimentation. Prepares large datasets using Python or R and SQL, validates and documents models, and partners with data engineering teams on scalable pipelines and production deployment. Translates analytical findings into pricing recommendations, dashboards, and executive insights while supporting governance, monitoring, and analyst enablement.

The Data Scientist will partner with Sales, Revenue Operations, and Finance to analyze GTM performance, build and productionize machine learning models, conduct experiments, forecast outcomes, and identify growth opportunities. Responsibilities include statistical analysis, causal inference, Python and SQL development, MLOps, agentic workflow automation, semantic-layer contributions, executive reporting, and communicating insights to nontechnical stakeholders in a fully remote environment.

Develop predictive models and metrics supporting player evaluation, roster construction, player development, and in-game strategy. Responsibilities include exploratory analysis, model validation and deployment, large-scale data processing, research integration, vendor data evaluation, and translating analytical results into basketball recommendations. The role collaborates with software development and data engineering teams to build applications and maintain production solutions.

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.

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

Leads AI and GenAI data science workstreams for client engagements. Develops machine learning, deep learning, and NLP models; applies statistical and predictive modeling; designs scalable data science workflows and pipelines; evaluates model performance; communicates insights through visualizations and executive narratives; coaches teams; and supports responsible AI governance.

Develop predictive, prescriptive, forecasting, valuation, and portfolio-performance models supporting collections, acquisitions, and business strategy. Build production data pipelines, transformations, analytical workflows, data models, quality checks, and monitoring frameworks using SQL, Python, and/or R. Perform exploratory analysis, create dashboards and reports, translate business needs into technical solutions, and collaborate with analytics, engineering, and business stakeholders.

Collects, cleans, analyzes, and visualizes structured and unstructured data to solve business problems and improve product performance. Builds basic machine learning models, conducts exploratory analysis, experimentation, A/B testing, and statistical validation. Collaborates with Product, Engineering, and data engineering teams on data requirements and quality, while documenting methods, findings, and model outputs.

Develop predictive, statistical, machine learning, simulation, and optimization models supporting staffing, financial forecasting, resource allocation, and operational decisions. Build scalable analytical tools and production deployments, analyze complex datasets, validate and monitor model performance, and improve methodologies. Partner with Operations, Finance, Marketing, and Legal teams to solve business problems and communicate recommendations to stakeholders and executives. Use Python, SQL, and cloud technologies to automate and scale analytics.

Develops fraud detection models using client data, including data cleaning, feature engineering, model tuning, and scalable pipeline development. Collaborates with clients and internal product and research teams, communicates technical findings to diverse audiences, guides project execution, and contributes to generalized product enhancements and fraud prevention solutions.

Develop and evaluate machine learning models for fraud detection across account opening, account takeover, and identity risk. Responsibilities include exploring large datasets, identifying fraud patterns, engineering features, testing models, writing production-quality Python code, supporting model monitoring, and communicating analytical findings. The role also applies data privacy, explainability, validation, documentation, and governance standards while collaborating with senior data scientists and engineering teams.

Develop advanced statistical and machine-learning solutions for fraud and other data-intensive business problems. Translate ambiguous needs into hypotheses, experiments, models, and prototypes; shape data strategies; evaluate emerging generative and agentic AI technologies; and partner with Product, Engineering, Analytics, and business teams to operationalize solutions. The role also includes mentoring junior team members and fostering experimentation and innovation.

Build and deploy machine learning models for credit and fraud risk, using large-scale financial data to improve lending decisions and business performance. Monitor production models, validate algorithms and features, conduct risk analyses, follow regulatory and responsible AI practices, and collaborate with engineers and cross-functional stakeholders. Develop productivity tools, document models, and communicate technical findings to senior management.

Leads data science initiatives by analyzing complex structured and unstructured datasets, developing machine learning algorithms and systems, and delivering analytical products and dashboards. Works with clients and stakeholders to define needs, communicate development plans, brief senior leadership, and provide data-driven recommendations. Mentors team members, identifies opportunities for data science solutions, and manages competing priorities in complex environments.

Lead design, execution, and interpretation of A/B tests and quasi-experiments; apply causal inference methods; partner with product, engineering, and marketing; mentor data scientists; build experimentation frameworks; extract and analyze data using SQL and Python; communicate results to stakeholders.

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Conduct workforce analytics and applied research on attrition, engagement, mobility, performance, and planning. Perform statistical analyses, regression modeling, data extraction and validation using SQL and R or Python. Translate HR business questions into actionable insights, communicate findings to stakeholders, and protect sensitive workforce data.

Serves as the quantitative partner to Growth Marketing, developing attribution, lifetime value, acquisition cost, retention, and predictive models. Leads A/B tests, geo-experiments, holdout tests, and causal inference studies to optimize marketing spend and growth. Partners with Growth, Product, Finance, analytics, and engineering teams on unit economics, data pipelines, event tracking, dashboards, and actionable business strategy.

Leads and mentors junior developers while designing, modifying, testing, and deploying data solutions in Palantir Foundry. Builds data models and reports from ERP and legacy systems, translates business requirements into technical capabilities, and supports IT modernization. Requires Python, PySpark, cloud architecture, CI/CD, database design, Army domain knowledge, and an active DoD Secret clearance.

Develop and validate machine learning and predictive models, perform statistical and data analysis, conduct ETL across repositories, and assess model quality. Communate findings through visualizations and written reports, automate analytical workflows, and collaborate directly with nontechnical customers to refine requirements and deliver data-driven solutions. The role requires expertise in ML/AI, programming, cloud environments, CI/CD, and government or safety-critical systems.

Drive revenue growth and advertiser value through data science for Nextdoor’s Ads Marketplace. Responsibilities include developing ranking and optimization models, designing experiments, analyzing auction performance, building explainability feedback loops, and delivering strategic insights. The role partners with Product, Engineering, Marketing, Sales, Finance, and Operations to improve advertising products and explore AI and large language model applications.