Top Remote Data Science Jobs

Build and deploy production machine learning and AI solutions on Databricks and Azure. Responsibilities include data discovery, feature engineering, model development, experiment tracking, model serving, monitoring, retraining, LLM and RAG use cases, responsible AI documentation, and modernization of legacy R, Stata, and SAS workloads. The role also involves client consultation, cloud adoption, training, mentoring, and communicating results to technical and executive audiences.

Analyzes enterprise incident, problem, change, API performance, and latency trends using Splunk, cloud observability tools, statistical modeling, AI, and machine learning. Develops predictive models, prototypes, dashboards, and remediation strategies to identify anomalies, improve automation, and resolve systemic issues across enterprise platforms. Collaborates with application, infrastructure, and network teams while providing technical guidance to stakeholders.

Lead performance marketing data science initiatives focused on lead segmentation, bidding optimization, targeting, and marketing spend allocation. Develop and deploy production-ready machine learning systems, establish research and ML operations roadmaps, build monitoring frameworks, and identify high-impact opportunities. Partner with engineering, marketing, vendors, and executives while mentoring data scientists and machine learning engineers and promoting rigorous, maintainable, and reproducible practices.

Lead data science for Mozilla’s advertising business by identifying high-impact opportunities, owning complex analyses, developing measurement and experimentation frameworks, analyzing revenue and performance, and translating findings into recommendations for technical partners and executives. Partner across Product, Engineering, Sales, Finance, and leadership to support business decisions in a fast-moving environment while balancing analytical rigor, pragmatism, and user privacy.

Lead data science partnership for Mozilla’s advertising business, identifying high-impact opportunities and owning complex analytics end to end. Develop measurement frameworks, experimentation strategies, instrumentation, and reporting for advertising products. Analyze revenue and performance, evaluate trade-offs, and communicate clear recommendations and uncertainty to technical teams and senior executives.

Develops retention, churn, and customer lifetime value models using statistical analysis and machine learning. Responsibilities include extracting and preparing structured and unstructured data, performing ad hoc analyses, creating dashboards and reports, automating analytical procedures, monitoring implemented models, and presenting findings to cross-functional business, IT, and management teams.

Staff Data Scientist responsible for owning ambiguous, high-impact product and business problems; designing experiments; analyzing product data; developing analytical tools and frameworks; partnering with product, engineering, design, and leadership; and mentoring data scientists. The role requires advanced statistical expertise, strong product judgment, production programming experience, and the ability to translate complex analyses into strategic recommendations.

Conducts statistical analysis, data mining, predictive modeling, data visualization, and AI/ML development using large intelligence datasets. Builds automated data ingestion and analytics workflows, improves data quality and architecture, and supports intelligence planning and operational decision-making. Collaborates with analysts, engineers, and mission partners to develop tools, models, and capabilities that enhance situational awareness and intelligence operations.

Conduct workforce analytics and applied organizational research on attrition, mobility, engagement, performance, and planning. Analyze and validate HR data using SQL, R, or Python; apply statistical testing and regression; translate business questions into actionable insights; and communicate findings through visualizations, reports, and presentations. Partner with HR stakeholders while maintaining the accuracy, documentation, confidentiality, and integrity of sensitive workforce data.

Develops advanced analytics for national security using large, disparate datasets. Responsibilities include designing data exploitation methods, researching sponsor inquiries, developing custom searches, applying classification, regression, clustering, and anomaly detection algorithms, and supporting threat hunting and malicious-activity detection. The role also involves statistical analysis, data visualization for nontechnical audiences, and prototyping data engineering pipelines.

Develop and deploy machine learning algorithms that improve real-time device identification and fraud detection. Own data science projects from experimentation through production, including supervised, semi-supervised, and unsupervised learning, real-time inference, training automation, exploratory analysis, and model-to-service integration. Contribute to engineering best practices, collaborate within a global remote team, and participate in a shared on-call rotation.

Develop and deploy traditional machine learning, GenAI, and NLP solutions for healthcare, insurance, and life sciences applications. Responsibilities include model research, validation, documentation, deployment, maintenance, experimentation, and collaboration with product, business development, ML engineering, and IT teams. The role also contributes to data science best practices, internal tooling, peer review, coaching, and translating client analytical needs into commercially viable products.

Lead the design, development, deployment, and maintenance of predictive models and decision systems. Explore data sources, run experiments, prototype solutions, apply advanced analytics, and drive strategic business improvements. Communicate findings to senior stakeholders, own projects end to end, mentor junior data scientists, and contribute to coding standards and technical direction.

Assist senior data scientists with data collection, preprocessing, cleaning, and basic statistical analysis. Support model development, perform basic visualizations, and document processes and findings while learning and applying data science techniques.

Lead data science projects from ideation through enterprise impact, building scalable and well-tested solutions that improve healthcare outcomes. Apply probability, statistics, machine learning, SQL, and Python to develop data-driven products and insights. Partner with stakeholders, assess product performance, identify strategic improvements, mitigate risks, and mentor teammates while aligning initiatives with business objectives.

Build and productionize traditional machine learning models, owning feature engineering, validation, deployment, monitoring, drift detection, and retraining. Develop LLM-powered agentic tools, dashboards, self-serve analytics products, and automated insight pipelines. Partner with engineering and business stakeholders, write production-grade Python and SQL, drive adoption, improve data quality, and establish responsible AI workflows for a regulated industry.

Leads analytical and scientific strategy for Fetch’s Play and Rewarded Apps businesses. Develops predictive and machine learning models, experimentation frameworks, causal analyses, feature pipelines, and measurement systems using Python and SQL. Partners with Product, Marketing, Engineering, and business leaders to improve monetization, engagement, retention, personalization, and marketplace outcomes. Translates complex findings into strategic recommendations, mentors data scientists and analysts, and establishes analytical best practices.

Develop and maintain credit risk models for origination, behavioral risk, underwriting, and portfolio management across Latin America. Own the full modeling lifecycle, including feature engineering, validation, backtesting, monitoring, calibration, and implementation. Analyze large financial datasets using Python and SQL, monitor portfolio and model performance, address data quality and selection bias, and translate analytical findings into credit strategies. Collaborate with Risk, Data, Engineering, Finance, and Operations teams.

Develops and enables clinical data assets for analytics and AI/ML across healthcare use cases. Responsibilities include designing clinical data models, taxonomies, feature stores, documentation, quality frameworks, dashboards, and reusable datasets. The role partners with clinical, business, and engineering stakeholders, supports data governance and HIPAA compliance, translates clinical concepts into analytical frameworks, and trains downstream data consumers. Expertise in clinical coding systems, healthcare datasets, SQL, cloud platforms, and clinical informatics standards is required.

The Lead Data Scientist will enhance analytics through predictive modeling and data engineering, focusing on quality, feature generation, and continuous learning.

Lead advanced research and predictive modeling on residential housing and energy performance data. Apply causal inference, anomaly detection, regression, and model validation to improve Pearl’s SCORE and energy models. Develop new performance metrics, evaluate climate-risk data, collaborate with external research partners, and communicate findings through white papers, publications, and clear stakeholder explanations.

Lead end-to-end data science for customer-embedded, production solutions: acquire and prepare messy datasets, develop and evaluate models tied to business outcomes, run experiments, deploy operational models at scale, coach cross-disciplinary teams, and drive strategy, ethics, and privacy discussions.

Develops data-driven solutions for government and strategic customers by acquiring and preparing datasets, evaluating and improving statistical models, building scalable operational models, researching industry trends, and collaborating with software engineers, technical program managers, designers, and customers on cloud-based AI projects.

Develop and migrate reporting assets from SAP BusinessObjects to QlikView and Qlik Sense. Analyze and rebuild Web Intelligence, Crystal Reports, and universes; debug legacy and new reports; optimize SQL, scripts, expressions, data models, and load processes; validate output accuracy; document migration logic; and collaborate with stakeholders on requirements and report specifications.

Lead development and productionization of telematics pricing and underwriting models. Responsibilities span statistical modeling, feature engineering, dataset and pipeline development, model deployment, monitoring, data quality validation, troubleshooting, regulatory support, and collaboration with pricing, actuarial, product, engineering, and compliance teams.