Top Remote Data Science Jobs

Lead end-to-end data science projects: define problems, build/test hypotheses, deliver scalable solutions for productization. Perform advanced statistical and time-series analyses, collaborate cross-functionally, and translate insights into customer value while supporting deployment and system optimization.

This role involves analyzing EHR and insurance claims data to define clinical outcomes, developing AI models, and collaborating with technical experts for manuscript preparation.

Consult as a fractional data scientist for hedge funds, private equity firms, and family offices. Lead data science initiatives: build infrastructure, develop and deploy models, create portfolio dashboards, monitor post-deployment performance, and partner with investment teams to turn data into actionable investment insights. Advise on vendor selection, data quality, technology stacks, KPIs, and governance.

Lead end-to-end data science initiatives across pay TV, digital products, media sales, and customer lifecycle. Design, deploy, monitor ML/AI solutions (churn, propensity, personalization, recommendation, forecasting), partner with stakeholders, build data pipelines, run experiments (A/B, uplift), mentor teams, and translate analytics into measurable commercial impact.

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.

Lead marketplace measurement, experimentation, and causal analysis; build pricing, recommendation, and retention models; operationalize models with engineering; maintain dashboards and monitor supply-demand balance to guide product and growth decisions.

The Senior Data Scientist will leverage advanced analytics, machine learning, and AI to develop analytics engines and collaborate with teams to enhance business value and communication of results.

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

Lead healthcare-focused data science projects from concept to deployment. Develop and deploy advanced ML, NLP, and generative AI models using Python and GCP (Vertex AI, BigQuery, Dataflow). Collaborate with clinicians, present findings to stakeholders, mentor junior data scientists, and ensure team follows AI/data science best practices.

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.

Lead and deliver complex data science and engineering projects: design data models and scalable pipelines, build and productionize statistical and ML models (Python/R), perform data integration and quality work, generate actionable insights for executive stakeholders, and provide domain leadership in Payment Integrity/FWA.

Hands-on senior data scientist splitting work between SQL Server data engineering (ETL, pipeline modernization, data quality) and analytics delivery (Power BI semantic models, dashboards, statistical and predictive modeling). Duties include designing migration options, implementing Python-based cleansing and reproducible analytics, building ETL/deployment processes, responding to ad hoc leadership data requests, and documenting systems for stakeholders. Must obtain VA background/PIV and be a U.S. citizen.

Remote multi-role data position covering Data Analyst, Data Engineer, and Data Scientist duties: analyze and visualize data, write complex SQL, build dashboards, perform A/B testing and segmentation; design scalable ETL/ELT pipelines, maintain data lakes/warehouses, enforce data quality and monitoring; and develop machine learning models and predictive analytics to drive business decisions while collaborating across teams.

Lead Data Scientist will support advanced analytics, predictive modeling, and machine learning initiatives for clinical decision-making in healthcare. Responsibilities include algorithm design and compliance with VA governance standards.

As a Lead Data Scientist, you'll design experiments, build predictive models, and lead a team while shaping data-driven strategies to enhance AI deployments.

The role involves building and validating Marketing Mix Models and geo-experiments, managing measurement infrastructure, and communicating insights to stakeholders.

Lead product-focused analyses across growth, retention, creator and economy verticals. Define metrics and experiments, analyze user and creator behavior, build dashboards and reusable analytical assets, use AI-assisted workflows to accelerate analysis, collaborate with engineering to improve data quality, and communicate insights to influence product decisions.

Lead product and program analytics for Microsoft skilling experiences: design learner journey/funnel analyses, define success metrics, run experiments and causal analyses, build proficiency/competency models, shape telemetry and scalable reporting, recommend content and curriculum improvements, and mentor other data scientists to drive product decisions and learning outcomes.

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.