Top Remote Data Science Jobs

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

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

Senior individual-contributor leading end-to-end analytics for Medicare Stars: designing forecasting and performance models, building enterprise dashboards (Tableau/Looker), implementing GCP data solutions, ensuring data quality, and delivering executive insights to drive Stars performance and business impact across clinical, product, and data teams.

Maintain and enhance risk models, data fields, code, and dashboards to identify at-risk agency partners. Calculate health scores, validate automated rankings, aggregate and analyze findings, prepare materials for risk meetings, track action items, and present results to management and non-technical stakeholders.

As a Lead Data Scientist, you will design and implement scalable data pipelines and analytics capabilities while leading technical decisions across data workflows, ETL processes, and statistical modeling in the GCP environment.

Lead risk-focused data science work by analyzing transactions and user behavior, developing automated risk controls and decisioning systems, advising leadership on risk strategy, and partnering cross-functionally to improve risk processes and scalability in a fast-paced fintech environment.

The Senior Data Scientist will develop machine learning models, conduct A/B tests, and work with behavioral data to enhance customer engagement and partner ROI.

Lead data science for Yahoo Mail subscriptions: analyze acquisition, conversion, pricing, retention, and churn; design and analyze A/B and multivariate experiments; build predictive and clustering models; partner with product, engineering, design, and business teams to deliver data-driven growth and monetization insights.

Design and implement forecasting models and algorithms for pricing and promotions, build features in the forecasting platform, clean and validate data, produce visualizations, write maintainable code, and collaborate with subject-matter experts and clients to translate data into retail business solutions.

The Sr. Data Scientist focuses on price optimization using deep learning and data science to enhance profitability and efficiency, while managing projects and mentoring junior staff.

Own analytics projects end-to-end: write production Python code, design and analyze experiments to optimize CTV ad campaigns, build reporting and analytics tools, and translate insights into the Data Science product to support stakeholders and improve campaign performance.

Lead data science work for a CTV/adtech platform: write production Python code, design and analyze experiments, build reporting and analytics tools, translate insights into product, and collaborate with stakeholders to optimize ad campaigns.

Proactively explore RevenueCat's data to identify product opportunities, translate ambiguous problems into analyses and production-grade predictive/descriptive models, partner with Product and Engineering to ship data-powered features (LTV prediction, experimentation, benchmarking), define statistical approaches, iterate on models in production, and communicate insights to technical and non-technical audiences.

The Senior Data Scientist will manage data collection, design study protocols, train juniors, lead report development, and support innovative analytics tools.

The Senior Data Scientist oversees data collection and preparation, designs study protocols, leads report development, trains junior employees, and manages project workstreams while collaborating with team members to optimize business performance.

The Senior Data Scientist will integrate data from various sources, analyze web performance, and provide insights for strategic decision-making. Responsibilities include developing automated systems for content coverage, tracking metrics, and designing experiments to assess new initiatives' impact.

Own end-to-end data science for a priority vertical (starting with Insurance): frame business problems, build and validate models, partner with ML engineering for deployment, monitor live ROAS performance, and drive measurable revenue and lead quality improvements.

The Lead Data Scientist leads data science initiatives to enhance decision-making and systems development, managing complex models and enterprise operations, while building client relationships and integrating best practices.

The Data Scientist collaborates with stakeholders to develop machine learning applications, analyze data, and enhance team operations, improving processes with data-driven insights.

The Senior Data Scientist will develop models and analyses for risk and revenue decisions, collaborating with various teams to optimize funding for eCommerce.

The Lead Data Scientist will build, deploy, and improve machine learning models for a rental marketplace, managing projects from inception to launch and working collaboratively with cross-functional teams.

Lead end-to-end data science projects including predictive modeling, algorithm design, and data pipelines. Build campaign-focused models (turnout, partisanship), perform exploration and visualization, translate insights into memos/presentations, collaborate with engineering to productionize models, mentor junior data scientists, and prototype emerging AI solutions for product impact.

Lead data science work embedded in the newsroom: identify high-impact problems, build and productionize predictive and explanatory models, own core metrics/measurement, apply causal inference and experimentation, translate findings for senior stakeholders, and mentor junior data team members to improve analytic standards and adoption.

Lead end-to-end data science initiatives for commercial property or homeowners insurance: acquire and prepare data, build and validate predictive models, deploy and monitor production models, ensure governance and documentation, advise teams, and evaluate emerging ML/AI techniques to improve pricing, risk selection, and underwriting outcomes.

Lead methodology and modeling for causal inference, experimentation, and predictive analytics. Design and evaluate experiments, build production-ready models and evaluation pipelines, collaborate with Product/Economics/Research/Engineering, communicate findings to varied audiences, and document institutionalized analytical practices.