Top Remote Data Science Jobs

Apply quantitative modeling, experimentation, and ML to product and business problems. Build analysis pipelines, prototype models, instrument experiments, manipulate large web-scale datasets, and communicate insights to cross-functional partners to improve Pinterest products for millions of users.

Senior data scientist role focused on industrial manufacturing analytics: build scalable cloud data pipelines, develop and deploy ML models for predictive maintenance, anomaly detection, and forecasting; integrate OT/IT systems (MES/SCADA/ERP) using industrial protocols; apply statistical methods, A/B testing, and lean/Six Sigma practices to improve yield and uptime; communicate findings to operations and executives.

The Data Scientist II will oversee data management, design study protocols, train staff, lead report development, and manage client activities using project frameworks.

Partner with product engineering to design and analyze experiments, apply causal reasoning and statistical modeling to marketplace data, and drive data-informed product decisions across the engineering org.

Develop frameworks and tooling to estimate long-term impacts of product changes from short-term experiment outcomes. Build causal inference methods (experimental, econometric, quasi-experimental), evaluate heterogeneous treatment effects, own major estimation problems, and communicate results to technical and non-technical stakeholders to drive better long-term decisions.

Develop and operationalize graph-based analytics for fraud detection and investigative analysis. Build graph models, apply graph algorithms and ML to detect anomalies, support entity resolution, create visualizations and dashboards, and collaborate with investigators and stakeholders to deploy and maintain production solutions.

As a Staff Data Scientist, you will address complex business challenges using AI and optimization algorithms, collaborate with engineering, define strategies, and implement data product frameworks.

Lead end-to-end data science for a priority vertical (Insurance), building, validating, and deploying ML models that drive ROAS and lead quality. Partner with stakeholders and ML engineering to monitor live performance, iterate models, and identify new modeling opportunities to improve revenue and media efficiency.

Lead end-to-end analytics for Customer Success: perform deep-dive diagnostics, build predictive models (churn/upsell), apply causal inference and experimentation, measure impact of initiatives, self-serve data pipelines and dashboards, and deliver AI/LLM solutions and actionable insights to reduce churn and drive revenue.

Senior individual-contributor data scientist partnering with Product, Operations, Finance and executives to run advanced analytics, design experiments, build predictive models and dashboards, develop ETL/ELT pipelines, and translate insights into business recommendations to improve customer experience, operational efficiency, and performance.

The Sr. Football Data Scientist will build, deploy, and refine analytical models for football insights, managing large datasets and collaborating with teams for high-quality analytics solutions.

Founding Data Scientist for Procure-to-Pay working cross-functionally to build models, metrics, simple data pipelines, dashboards, and reports. Analyze complex datasets, produce actionable insights through data storytelling, and partner with leadership to turn business problems into data-driven solutions that impact operations and growth.

This role involves leading data science projects, designing technical solutions, implementing machine learning methodologies, and developing data workflows for cybersecurity applications.

The Data Scientist II will develop machine learning algorithms, architect data solutions, and support cybersecurity initiatives by analyzing complex data sets.

Design and build cloud-based analytical products across the Pharmacy Benefits Management ecosystem. Develop predictive and prescriptive models, perform feature engineering and EDA, collaborate with product, data engineering, and business teams, and modernize legacy systems to support forecasting, pricing, and decision-making.

The Data Scientist will develop AI/ML models to enhance healthcare processes, ensuring compliance and quality in clinical documentation.

Lead a data science team to build scalable analytics and predictive models for pricing decisions. Partner with business and engineering to analyze large datasets, prioritize work, ensure model quality, and present data-driven optimization recommendations.

Lead Revenue Analytics by developing data-driven strategies to maximize revenue, own and improve data pipelines, run advanced experiments and causal analyses, lead cross-functional initiatives, mentor junior analysts, and embed AI tools into workflows to scale impact.

The Senior Data Scientist will partner with multiple departments to deliver impactful data solutions, champion data adoption, and evolve data science standards.

The Lead Data Scientist will develop advanced analytical solutions for pricing and customer insights in pet insurance, mentor teams, and collaborate with stakeholders.

Drive data-science for people- and business-facing products: establish experimentation and instrumentation best practices, apply statistical and ML methods to web-scale data, build scalable analysis pipelines, and partner cross-functionally to turn insights into product improvements.

Design and analyze experiments and causal studies, build retention measurement and predictive models, develop scalable PySpark/SQL data pipelines, create dashboards, and partner with product and growth teams to improve user engagement and retention.

The Data Scientist will analyze data for risk and trading in Daily Fantasy Sports, develop predictive models, and monitor real-time data.

Develop and maintain predictive pricing, retention, and conversion models using GLMs and advanced analytics; build monitoring tools for rating performance; support rate filings, product pricing analyses, competitor research, and cross-functional collaboration to inform pricing and enterprise risk decisions.

Lead Fraud Data Scientist to define ML strategy, build and productionize fraud detection models (including lending fraud), run advanced analyses and A/B tests, mentor the team, integrate ML with rule engines, and create dashboards to monitor fraud KPIs and model performance.