Top Data Science Jobs in Los Angeles, CA

Apply quantitative analysis, data mining, and statistical modeling to generate actionable product and business insights. Design and track core metrics, build dashboards and visualizations, collaborate with product, engineering, and design teams, and use AI tools and scalable analytical workflows to accelerate decision-making while ensuring methodological rigor and data quality.

The Data Scientist I role involves statistical analysis, ML model development, and collaboration to improve ad performance using large datasets.

The Data Scientist will analyze user behavior, create segmentation strategies, design experiments, and track business metrics to drive growth in a SaaS company.

Lead end-to-end data science projects, develop machine learning models, collaborate with stakeholders, mentor team members, and communicate insights effectively.

As a Product Data Scientist, you will analyze user behavior, drive experimentation, build data tools, and collaborate across teams to influence product strategy and innovation at Whatnot.

Lead product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

Work with clients and engineers to audit marketing datasets, build analytics pipelines, and apply predictive models (segmentation, attribution, propensity) using SQL, Python, cloud big-data tools, and ML frameworks. Support implementations, develop analytic applications, and communicate findings to drive marketing outcomes.

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.

As a Data Scientist at SentiLink, you'll develop fraud detection models using machine learning, engage in product development, and support analysis across teams. This role emphasizes hands-on technical work and end-to-end project ownership in a fast-paced environment.

As a Data Scientist, you will develop algorithms, collaborate on product requirements, and communicate data-driven insights, while enhancing product offerings and delivering actionable insights.

Own the full analytics lifecycle: define problems, extract data, build and deploy models, design experiments, and create Power BI dashboards. Track business metrics, apply statistics and AI/LLM tooling, partner with cross-functional teams, and present findings to stakeholders to inform decisions.

Own statistical methodology for mission engineering: design experiments for large-scale simulations, apply Bayesian and regression methods, build scalable Python data pipelines and surrogate models, quantify uncertainty and sensitivity, produce decision-quality visualizations and reports, mentor teams, and support DoD/DARPA/intelligence programs.

As a Senior Data Scientist, you will lead high-complexity projects, develop ML and NLP solutions, and collaborate across teams to drive business impact through statistical modeling and data analysis.

Lead and deliver end-to-end data science projects: design, build, deploy, and monitor ML/statistical models for credit risk, pricing, collections, and fraud. Partner with cross-functional teams to integrate models into production, set model development standards, mentor engineers, and maintain reliable ML pipelines and monitoring systems.

Lead data science initiatives to develop and deploy AI solutions that improve business outcomes. Collaborate with cross-functional teams to analyze data, create models, and evaluate performance.

Senior individual contributor who drives product analytics, experimentation, and applied modeling. Owns growth and performance analyses, designs experiments with robust statistical evaluation, builds repeatable reporting, and partners with Product, Engineering, Operations, Clinical, and Finance to translate data into actionable product and operational decisions.

Assist senior data scientists with data collection, preprocessing, cleaning, and basic statistical analysis. Support model development, perform basic visualizations, and document processes and findings while learning and applying data science techniques.

Lead Oracle-focused healthcare data quality and analytics: validate transactional data, build standardized KPIs and dashboards, develop predictive inventory and revenue cycle models, and collaborate with clinical and finance teams to ensure compliance and optimize operations.

Build and deploy predictive and time-series models using HR and clinical data (Oracle HCM, GCP) to forecast staffing, identify flight-risk employees, analyze pay/burnout relationships, and create interactive dashboards and analyses to inform HR strategy and operational workforce decisions.

Turn multi-model signals into measurement, forecasts, and optimizations. Clean and label large datasets, extract structure with LLMs/NLP, define product-grade metrics, run experiments, build predictive models, create evals and monitors, and translate insights into product and business decisions.

Perform data cleansing, ETL, and preprocessing; analyze Retail CPG sales, inventory, and customer data; build predictive models for forecasting and segmentation; research and deploy Agentic frameworks and intelligent agents; produce visualizations and communicate insights to cross-functional stakeholders to optimize supply chain, marketing, and customer experience.

As a Data Scientist II at Oscar, you'll drive data science projects, develop models, collaborate with cross-functional teams, and ensure quality outputs with minimal oversight.

The Senior Data Scientist will build models and analyses, design experiments, integrate datasets, and leverage AI for improved workflows and insights in data science.

The Data Scientist II will drive insights, develop models, and analyze product experiments to inform business decisions and improve member experiences in healthcare.

The Data Scientist II will design, build, and deploy machine learning models and data products, translating business problems into data-driven solutions, while collaborating with various stakeholders and ensuring reliable, production-ready outcomes.