Senior Data Scientist

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About Care Access Care Access is working to make the future of health better for all. With hundreds of research locations, mobile clinics, and clinicians across the globe, we bring world-class research and health services directly to communities that often face barriers to care. We are dedicated to ensuring that every person has the opportunity to understand their health, access the care they need, and contribute to the medical breakthroughs of tomorrow. With programs like Future of Medicine , which makes advanced health screenings and research opportunities accessible to communities worldwide, and Difference Makers , which supports local leaders to expand their community health and wellbeing efforts, we put people at the heart of medical progress. Through partnerships, technology, and perseverance, we are reimagining how clinical research and health services reach the world. Together, we are building a future of health that is better and more accessible for all. To learn more about Care Access , visit . How This Role Makes a Difference As a Senior Data Scientist , you will play a pivotal role in advancing Reify Health’s data-driven solutions for clinical trials. In this position, you will drive the development of statistical models and machine learning algorithms to improve patient enrollment and trial management. You’ll work in a highly regulated healthcare data environment, ensuring compliance with privacy standards while innovating on predictive analytics. This role involves close collaboration with cross-functional teams (especially ML Engineering) to translate complex data insights into practical, impactful tools for the clinical research community. How You'll Make An Impact Site Randomization Forecasting: Develop/enhance forecasting models for site randomization and enrollment trends, enabling better planning and resource allocation across trial sites. Patient Matching/Ranking Algorithms: Support projects to build algorithms that intelligently match patients to (or rank patients for) appropriate clinical trials, enhancing recruitment efficiency and patient inclusion. Develop Other Advanced Statistical Models: Create and refine predictive models (Bayesian inference, regression analysis, time-series forecasting) to address other key clinical trial challenges and improve decision-making. AI Monitoring and Bias Detection: Implement processes to monitor machine learning models in production, detecting bias or performance drift and ensuring models remain fair, accurate, and compliant. Data Pipeline Tooling Development: Build and optimize data pipelines and analytical workflows using tools like AWS Athena, Redshift, SageMaker, and dbt, enabling scalable model training and deployment. Regulatory Compliance in Data Science: Ensure all data science practices align with HIPAA, GDPR, and other privacy regulations, integrating compliance considerations into model development and data handling. Cross-Functional Collaboration: Work closely with machine learning engineers, product managers, and other stakeholders to integrate models into products and clearly communicate insights and recommendations. The Expertise Required Statistical ML Expertise: Strong foundation in statistical modeling and machine learning techniques, including experience with Bayesian methods, regression analysis, and time-series forecasting. Model Monitoring Fairness: Proficiency in evaluating model performance and bias, with the ability to implement AI monitoring tools and bias mitigation strategies to ensure ethical and reliable outcomes. Technical Toolset: Advanced programming skills in Python (with libraries such as scikit-learn, PyMC, mlforecast, etc.) and SQL, as well as familiarity with data transformation tools like dbt. Cloud Data Infrastructure: Hands-on experience with cloud-based analytics and ML services, especially AWS tools (Athena for querying, Redshift for data warehousing, SageMaker for model development/deployment). Regulated Data Handling: Expe

关于Care Access Care Access致力于让所有人的健康未来更美好。凭借遍布全球的数百个研究中心、移动诊所和临床医生，我们将世界级的研究和健康服务直接带到那些常常面临护理障碍的社区。我们致力于确保每个人都有机会了解自己的健康状况、获得所需的护理，并为明天的医学突破贡献力量。通过“Future of Medicine”等项目——让全球社区都能获得先进的健康筛查和研究机会——以及“Difference Makers”等项目——支持当地领导者扩大其社区健康和福祉工作——我们将人置于医学进步的核心。通过合作、技术和坚持不懈，我们正在重新构想临床研究和健康服务如何触达世界。我们携手共建一个更美好、更可及的健康未来。欲了解更多关于Care Access的信息，请访问。 这个角色如何创造不同 作为高级数据科学家，您将在推进Reify Health面向临床试验的数据驱动解决方案方面发挥关键作用。在这个职位上，您将推动统计模型和机器学习算法的开发，以改善患者入组和试验管理。您将在高度监管的医疗数据环境中工作，在创新预测分析的同时确保符合隐私标准。该角色需要与跨职能团队（尤其是ML Engineering）密切合作，将复杂的数据洞察转化为临床研究社区实用且有影响力的工具。 您将如何创造影响 中心随机化预测：开发/增强中心随机化和入组趋势的预测模型，使试验中心能够更好地规划和分配资源。 患者匹配/排序算法：支持构建算法的项目，以智能地将患者匹配到（或对患者进行排序以进入）合适的临床试验，提高招募效率和患者纳入率。 开发其他高级统计模型：创建和优化预测模型（贝叶斯推断、回归分析、时间序列预测），以应对其他关键临床试验挑战并改进决策。 AI监控和偏差检测：实施流程以监控生产中的机器学习模型，检测偏差或性能漂移，并确保模型保持公平、准确和合规。 数据管道工具开发：使用AWS Athena、Redshift、SageMaker和dbt等工具构建和优化数据管道和分析工作流，实现可扩展的模型训练和部署。 数据科学中的监管合规：确保所有数据科学实践符合HIPAA、GDPR和其他隐私法规，将合规考虑纳入模型开发和数据处理。 跨职能协作：与机器学习工程师、产品经理和其他利益相关者密切合作，将模型集成到产品中，并清晰地传达洞察和建议。 所需专业能力 统计ML专业知识：在统计建模和机器学习技术方面具有扎实基础，包括贝叶斯方法、回归分析和时间序列预测的经验。 模型监控公平性：精通评估模型性能和偏差，能够实施AI监控工具和偏差缓解策略，以确保合乎道德且可靠的结果。 技术工具集：具备Python（包括scikit-learn、PyMC、mlforecast等库）和SQL的高级编程技能，以及熟悉dbt等数据转换工具。 云数据基础设施：具有基于云的分析和ML服务的实践经验，尤其是AWS工具（Athena用于查询、Redshift用于数据仓库、SageMaker用于模型开发/部署）。 受监管数据处理：具

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