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The Data Scientist II will build and deploy machine learning models and NLP pipelines to support clinical risk intelligence and AI-enabled products. They will collaborate with cross-functional teams to translate research into production-ready, HIPAA-compliant solutions.
Lead the design and operationalization of generative AI and machine learning solutions within healthcare and life sciences environments. Act as a senior technical advisor to bridge the gap between clinical stakeholders and engineering teams to deliver secure, measurable AI outcomes.
Build and maintain auditable data pipelines for clinical and medical imaging datasets while performing statistical analysis for model evaluation. Develop data observability tools and ensure data integrity for regulatory submissions and clinical audits.
The Senior Data Scientist will develop and evolve AI/ML models to drive personalization initiatives and business value. Responsibilities include end-to-end data science tasks such as data normalization, feature engineering, model deployment, and experimental design.
The Senior Data Scientist will collaborate with product and operations teams to ingest, model, and analyze healthcare data to support care enablement. They will build and maintain scalable data pipelines and models while ensuring internal stakeholders have reliable access to high-quality data.
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Lead end-to-end data science projects from problem framing to model deployment while collaborating with cross-functional teams. Develop advanced machine learning models and translate complex quantitative findings into actionable business insights.
The Principal Data Scientist will lead the data strategy for the Rentals Shopping pillar, partnering with product leadership to drive growth through rigorous analysis and experimentation. They will also mentor team members and apply advanced modeling techniques to optimize the rental search and conversion experience.
The incumbent will lead key projects focused on fraud detection, risk analysis, and loss mitigation to optimize business processes. They will collaborate with cross-functional teams to design, execute, and monitor risk control strategies across the customer lifecycle.
Design and build machine learning systems for enterprise search, recommendations, and AI agents while developing evaluation frameworks for LLM-powered applications. Collaborate with cross-functional teams to translate research into production features and define key performance indicators to guide product strategy.
Design and build machine learning systems for enterprise search, recommendations, and AI agents while developing evaluation frameworks for LLM-powered applications. Collaborate with cross-functional teams to translate research into production features and define analytical frameworks to guide product strategy.
The Principal Data Scientist will lead the design and execution of enterprise-grade AI solutions, focusing on supply chain optimization and agentic AI systems. They will also provide technical leadership, mentor data science talent, and ensure all AI implementations align with security and governance standards.
Design and implement advanced AI solutions, including generative AI, RAG workflows, and analytical models to support business decision-making. Collaborate with stakeholders to translate business objectives into measurable outcomes and advocate for data-driven strategies.
You will build and deploy machine learning models to manage credit and fraud risks while shaping credit risk strategy. Additionally, you will analyze portfolio performance and collaborate with engineering to integrate models into production environments.
You will lead complex data science initiatives to drive business impact and design models that optimize audience selection, marketing, and content strategies. Additionally, you will mentor team members and define the technical roadmap for decision science capabilities while ensuring responsible AI development.
Lead and define the data science strategy for product development while serving as a strategic advisor to senior leadership. Drive the integration of emerging AI technologies and mentor other data scientists to foster a culture of technical excellence.
Lead large-scale data science projects from ideation to enterprise impact while mentoring team members. Collaborate with stakeholders to deliver data-driven solutions that improve patient outcomes and business performance.
You will own and advance core AI models end-to-end, collaborating with product and engineering teams to ship measurable improvements. Your work involves leveraging modern deep learning and traditional data science techniques to solve complex insurance risk challenges.
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The Principal Data Scientist will lead complex modeling initiatives and set the organization's modeling strategy. They will leverage deep learning and large language models to create hybrid models that drive real-world impact and business value.
The Senior Data Scientist develops and applies advanced analytical models to optimize store performance, labor effectiveness, and operational decision-making. They partner with cross-functional teams to translate business problems into actionable insights and establish rigorous measurement standards for operational initiatives.
You will own end-to-end data science and BI projects, from problem definition and experimentation to model deployment and monitoring. You will also build predictive models and machine learning solutions to support business decision-making and operational efficiency.
You will own the full lifecycle of applied machine learning, from exploratory analysis and modeling to production deployment and post-launch measurement. You will build and scale recommendation systems that drive core business KPIs and partner with engineering teams to ensure technical success.
The Senior Data Scientist will lead the end-to-end AI/ML model development lifecycle, from design to deployment, across various business units. They will also provide technical leadership, mentor junior colleagues, and collaborate with cross-functional teams to deliver innovative educational solutions.
You will design, build, and deploy machine learning models for critical cybersecurity use cases such as threat detection, fraud intelligence, and risk scoring. You will own the full model lifecycle, including data preparation, feature engineering, and production monitoring in cloud-native environments.
You will design and implement probabilistic models for identity resolution and advance lift methodologies to improve advertising performance. Additionally, you will lead cross-functional initiatives to define ground truth frameworks and maximize signal quality for predictive bidding models.
You will proactively identify opportunities for data science to drive business value across marketing, product, finance, and operations. You will lead high-stakes investigations from end to end and translate complex analysis into actionable recommendations for leadership.
The Junior Data Scientist will perform foundational data preparation, develop scripts for automation, and execute routine analytical models to support logistics decision-making. They will also assist in creating data visualizations and reports while collaborating with senior analysts on mission-focused projects.
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You will architect advanced machine learning solutions and lead complex initiatives across the global ML ecosystem. Additionally, you will mentor junior engineers and establish high engineering standards for scalable, production-ready systems.
Build and refine NLP/ML models using both classical and modern LLM-based techniques to extract insights from student and educator text. Partner with product and engineering teams to deploy these models into user-facing features that support educators and students.
You will lead the development of custom healthcare foundation models and transformer-based architectures for patient risk prediction. This role involves owning the full machine learning lifecycle, from research prototyping to production deployment, while collaborating with clinicians and engineers.
You will design and build the data and context layer for a procurement AI agent platform, including knowledge graphs, semantic layers, and retrieval pipelines. You are responsible for ensuring production-grade infrastructure, including data ingestion, matching rules, and quality feedback loops.
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