Top Remote Data Science Jobs

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.

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.

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.

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.

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.

Build, validate, and operationalize analytics, dashboards, and analytics applications for Property & Casualty and Employee Benefits on Azure Databricks. Collect, cleanse, integrate, and analyze insurance data; prototype analytical solutions; create visualizations and reports; and collaborate with solution engineers and stakeholders to deploy data-driven insights and improve processes.

Build, validate, and operationalize analytics and dashboards for Property & Casualty and Employee Benefits. Collect, clean, and integrate insurance data, perform exploratory analysis, prototype models, and collaborate with solution engineers to deploy analytics on Azure Databricks while communicating insights to stakeholders.

Seeking a skilled Data Scientist/Machine Learning Engineer to develop machine learning models, design AI systems, and collaborate in data analytics. Responsibilities include productionizing models, data engineering, and cross-functional collaboration.

Analyze large-scale educational data to inform product design and efficacy. Develop, evaluate, and maintain statistical and AI models, collaborate with cross-functional teams, communicate findings to stakeholders, and leverage AI tools for analysis and feature engineering while following software development practices.

Drive product and business analyses across Quora: design and evaluate experiments, build models and visualizations, analyze cross-product data to surface root causes, and partner with cross-functional teams to implement data-informed solutions and frameworks.

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 founding Data Scientist/Machine Learning Engineer, you'll develop AI/ML models, enhance product capability, and drive impactful user outcomes while working closely with product teams.

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

Build, validate, and productionize clustering and attribution heuristics on large multi-chain on‑chain datasets. Own end-to-end data pipelines, investigate edge cases (mixers, bridges, consolidation), benchmark against ground truth, and partner with investigations and product to define and defend precision/coverage metrics.

Lead the fraud data science team to design, build, deploy, and monitor real-time fraud detection models. Perform feature engineering, unsupervised analysis, A/B testing, stakeholder collaboration, and dashboarding to reduce fraud losses and improve model performance and explainability.

Lead measurement and analysis for Notifications: define metrics, size opportunities, and communicate product recommendations. Partner with product, engineering, and ML to prioritize projects, improve notification ML models, and iterate via A/B experimentation. Mentor other data scientists and drive high-impact, cross-functional initiatives.

The Data Scientist will analyze geospatial data, communicate insights, collaborate with teams, and enhance data processes to support national security missions.

Build, maintain, and improve decision systems and predictive models; explore internal and external data; design and run in-market experiments; prototype solutions; recommend data-driven strategic changes; implement advanced analytics methods.

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

As a Senior Data Scientist, you'll develop fraud detection models, perform detailed analyses, and work closely with teams to drive product innovation and business outcomes.

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

Design, validate, and deploy scalable statistical and distributional models for enterprise consumer analytics. Implement rigorous methodologies, convert ad-hoc analyses into version-controlled analytical libraries, and partner with Data Engineering to optimize performance and scale compute workloads (including cloud/Databricks). Deliver reproducible analytics powering dashboards and LLM-driven applications, with a focus on panel and transaction-level consumer data.

Lead a 5–8 person Data Science team to develop and operationalize FWA detection models using SAS and Python on multi‑payer healthcare claims. Oversee model development, data pipelines, quality reviews, stakeholder presentations, and collaboration with SMEs and BI developers. Split role ~40% management, 60% hands-on coding/analysis.