Top Remote Data Science Jobs in Los Angeles, CA

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.

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.

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

As a Data Scientist/Machine Learning Engineer, you'll finetune models, improve data quality, add new signals, and push solutions to production.

The Staff Data Scientist will own the analytical strategy for Firefox, driving data-informed decisions through measurement frameworks, experiments, and user growth analysis, while mentoring other data scientists.

Design and evaluate causal inference experiments and causal ML models to measure product impact, optimize resource allocation, and inform support operations. Build, deploy, and iterate statistical/ML models, analyze tradeoffs, collaborate cross-functionally, and communicate insights to influence decisions and scale data science capabilities.

Develop and apply causal inference and experimentation methods to measure and optimize marketing impact. Pull and analyze data, create features, design experiments, build end-to-end models, and collaborate with cross-functional teams. Communicate findings, lead internal white papers, and advance measurement science to inform marketing decisions.

The Data Scientist will build machine learning models to enhance claim audits, optimize performance, and improve healthcare decision-making by analyzing complex data sets.

The Staff Data Scientist will advance machine learning models to optimize claim audits and improve business outcomes in healthcare.

The Staff Data Scientist will lead the development and deployment of ML and GenAI systems to detect billing errors and fraud in healthcare payments, collaborating with cross-functional teams.

The Data Scientist will develop tools for enhancing signature management capabilities, integrate diverse data holdings, and leverage AI/ML solutions for operational support.

Customer-facing data scientist advising customers on experiment design, statistical methodology, and experimentation architecture; running and analyzing A/B tests; building scalable experimentation programs; creating repeatable frameworks and technical enablement; partnering with Sales, Solutions Engineering, Product, and Engineering to drive customer success.

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.

The Data Scientist will build predictive models, drive forecasting, design experiments, and translate findings for stakeholders, optimizing healthcare operations.

Perform statistical analysis, predictive modeling, and data visualization on workforce and training data. Build dashboards, produce ad hoc analyses and decision-support products, advise on data governance/privacy, and collaborate with stakeholders to define metrics, deliverables, and reproducible analytic workflows.

Use statistical and causal inference methods to build, implement, and monitor marketing models (Bayesian MMM, multi-touch attribution), analyze A/B and uplift tests, partner with marketing to test and drive data-informed decisions from large web-product datasets.

Lead and grow a 4–6 person data science team, set the roadmap, deliver marketplace forecasting and production ML (matching, scoring), embed data science cross-functionally, and establish modeling, experimentation, and monitoring best practices.

Embed with a business unit to define problems, run analyses, design experiments, build reports, surface insights, and drive data-informed decisions across growth, product, AI, or partnerships.

Embed with a core business unit to define problems, design experiments, build reports, run analyses, surface insights, and drive data-informed decisions end-to-end across growth, product, AI/automation, or partnerships.

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.

Contract data scientist to remotely gather, extract, and analyze technical information about AI models, benchmarks, data centers, and companies. Tasks include reading ML papers, tracking leaderboards, processing satellite images/permits, compiling infrastructure details, and producing data insights for research and policy. Part-time (10–30 hrs/week), flexible schedule with occasional Pacific Time meetings.

As a Senior Data Scientist, you'll optimize search relevance, retrieve content, analyze data, and collaborate with teams to enhance product strategies.

Lead a data science team developing data-driven solutions for CMS Medicare quality reporting. Build and optimize predictive models and backend jobs using Python, PySpark, and Databricks. Conduct code reviews, debugging, documentation, ML monitoring, and stakeholder collaboration. Ensure deliverable quality, participate in Agile ceremonies, and drive process and performance improvements.

The role involves deploying data science solutions in cybersecurity, collaborating with customers and teams to operationalize models and enhance security insights.