841 Data Scientist jobs available for remote work from home. Apply for positions such as Graph Data Scientist, Senior Data Scientist - Bees Frontline , Health IT Data Scientist and more! Discover the best work-from-home or hybrid, full- and part-time jobs.
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Develop graph-based analytics solutions to support fraud prevention and investigative analysis within a federal environment. Design graph models and apply network algorithms to identify hidden relationships and suspicious patterns across complex datasets.
Design and deploy machine learning models and optimization solutions to improve sales force effectiveness and customer engagement globally. Collaborate with cross-functional teams to translate commercial problems into scalable data science pipelines and production-grade code.
Act as a liaison between government stakeholders and technical teams to gather and validate business requirements. Support system modernization efforts through process modeling, Agile backlog development, and User Acceptance Testing coordination.
Lead the design and implementation of agentic AI systems and optimization algorithms to solve complex business and logistical problems. Partner with cross-functional teams to build production-ready systems and define metrics to measure business impact.
Lead the Data Science strategy for Product by integrating advanced analytics and AI methodologies to improve customer experiences. Serve as a strategic advisor to senior leadership while mentoring other data scientists and driving the adoption of scalable data tooling.
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Own the end-to-end data science engine for priority verticals to drive measurable revenue and media efficiency. Focus on building and deploying models for Insurance and Advertiser Quality to improve ROAS and lead quality.
Own the end-to-end data science engine for priority verticals to drive measurable revenue and media efficiency. This involves framing business problems with stakeholders and building models to improve ROAS and lead quality.
Design and implement advanced ML models and statistical methods to optimize forecasting and decision-making for a DoD customer. Manage data provenance, lineage reporting, and resource utilization within an Agile development framework.
Develop and deploy statistical and machine learning models to support business-critical forecasting and P&L prediction frameworks. Collaborate with cross-functional stakeholders to communicate insights and optimize model outcomes in a cloud environment.
Own the end-to-end data science lifecycle for production-ready models solving marketplace problems, from problem formulation to deployment and monitoring. Partner with product and engineering teams to ship solutions and mentor junior data scientists.
Design and deploy scalable AI, Generative AI, and agentic solutions to solve complex supply chain challenges for enterprise customers. Partner directly with clients to define requirements and lead the implementation of production-ready AI pipelines.
The role involves collecting, cleaning, and validating data from various sources to generate reports and dashboards. Additionally, the candidate will support automation workflows and AI-powered data processes using APIs from platforms like Google, Meta, and TikTok.
Own the analytical and modeling foundations, focusing on causal inference and predictive modeling to influence product direction. Collaborate cross-functionally with Product, Economics, and Engineering to translate business questions into production-ready analytical frameworks.
Develop and productionize machine learning models and anomaly detection systems to identify and mitigate fraud threats. Collaborate with cross-functional teams to design experiments and translate model outputs into real-world risk mitigations.
Define the technical strategy and roadmap for credit risk and fraud detection while building production-grade ML models. Lead the technical direction for risk assessment and mentor other data scientists to ensure measurable business impact.
Build and productionize ML systems to enhance personalization, search, and recommendation experiences. Focus on utilizing advanced algorithms to improve user growth and business conversion rates.
Analyze business needs to implement Machine Learning and Deep Learning models for data valuation. Contribute to the Data Science community through technical reviews, POCs, and internal training.
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Lead a team of Data Scientists to develop models and algorithms for detecting Fraud, Waste, and Abuse (FWA) within healthcare claims. Balance responsibilities between team management (40%) and hands-on development and coding (60%).
Leads the analysis of complex structured and unstructured data sets using advanced statistical methods to drive business decision-making. Responsible for managing cross-functional teams, developing custom algorithms, and providing technical mentorship to other data scientists.
The role involves developing and validating classification models to evaluate credit risk and assign credit scores. It also requires monitoring model performance and collaborating with cross-functional teams to turn data insights into business decisions.
Develop predictive analytics and GLM-based rating factors to optimize pricing, profitability, and risk segmentation. Monitor rating plan performance and support regulatory rate filings and product viability analyses.
Develop prototypes and evaluate algorithmic approaches for customer use cases using Machine Learning and NLP. Collaborate with software developers and designers to move models from Jupyter Notebooks into production products.
Design, develop, and deploy machine learning models and predictive analytics solutions to support strategic business decisions. Collaborate with engineering and business teams to build scalable data pipelines and mentor junior data scientists.
The role involves building and deploying ML and LLM-powered features to automate governance, risk, and compliance workflows. You will manage the full lifecycle from problem framing and data exploration to production monitoring and integration.
Lead end-to-end ML project implementations for clients, translating business problems into production-grade technical solutions. Design and maintain scalable ML pipelines and Generative AI applications using MLOps best practices.
Develop a POC platform to transform unstructured contract PDFs into a searchable AI-powered dashboard. This includes building the frontend, backend services, and integrating LLMs for structured data extraction.
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Lead the design, development, and deployment of production-grade machine learning models and data pipelines for aviation software. Collaborate cross-functionally to transform complex datasets into scalable solutions and intelligent automation for global clients.
Lead and manage end-to-end data science projects from inception to production, ensuring alignment with strategic business objectives. Collaborate with cross-functional teams to design AI systems and communicate technical insights to stakeholders.
Lead the end-to-end delivery of advanced statistical and machine learning models for credit risk, pricing, and fraud. Partner with cross-functional teams to integrate these models into applications and mentor team members on technical standards.
Lead the end-to-end delivery of data science initiatives to develop and deploy advanced models for credit risk, pricing, and fraud. Partner with cross-functional teams to integrate these models into applications and mentor team members on technical standards.
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