Data Scientist Principals #IN1291

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Lead advanced analytics initiatives to solve complex business problems using quantitative analysis, statistical modeling, and machine learning techniques. Design, develop, and deploy machine learning models to support decision systems, recommendation and next best action frameworks, anomaly detection, pattern recognition, and forecasting/time series analysis. Liaise with business stakeholders and leverage knowledge to industrialize and monetize insights from advanced analytics projects. Research, implement, and validate advanced analytical and algorithmic methods, including causal inference, impact measurement, predictive modeling, clustering, and optimization techniques, to quantify business outcomes and evaluate the effectiveness of data driven interventions. Apply statistical inference and data mining techniques to analyze large, diverse datasets and identify meaningful relationships, trends, and drivers of performance. Develop and support scalable, end to end analytics and machine learning solutions, including data processing pipelines and model lifecycle management practices, using commonly used programming languages and distributed data processing frameworks. Support both batch and near real time/streaming data processing in a cloud based analytics environment. Design and maintain modern data platform architectures, including lakehouse style data architectures, with responsibilities for data quality, schema management, and governance controls. Utilize sampling methods and experimental or quasi experimental approaches as needed to support research, analysis, and decision making. Translate analytical results and model insights into clear business language, communicate findings to technical and non technical stakeholders, and partner with domain experts to ensure alignment with business objectives. Lead requirements analysis, assess interdependencies and priorities, and align analytics initiatives with strategic roadmaps and organizational goals. Apply Agile software development practices to design, develop, and enhance analytics solutions. Provide technical leadership and mentorship to less experienced engineers or analysts on data science methodologies, machine learning solution design, and best practices for scalable analytics systems. Originally posted on Himalayas

领导高级分析计划，运用定量分析、统计建模和机器学习技术解决复杂业务问题。设计、开发和部署机器学习模型，以支持决策系统、推荐与下一最佳行动框架、异常检测、模式识别以及预测/时间序列分析。与业务利益相关者联络，并利用知识将高级分析项目的洞察工业化并实现变现。研究、实施并验证先进的分析和算法方法，包括因果推断、影响测量、预测建模、聚类和优化技术，以量化业务成果并评估数据驱动干预措施的有效性。应用统计推断和数据挖掘技术分析大型、多样化数据集，识别有意义的关系、趋势和绩效驱动因素。使用常用编程语言和分布式数据处理框架，开发并支持可扩展的端到端分析和机器学习解决方案，包括数据处理管道和模型生命周期管理实践。在基于云的分析环境中支持批处理和近实时/流式数据处理。设计并维护现代数据平台架构，包括lakehouse风格的数据架构，负责数据质量、模式管理和治理控制。根据需要利用抽样方法和实验或准实验方法支持研究、分析和决策。将分析结果和模型洞察转化为清晰的业务语言，向技术和非技术利益相关者传达发现，并与领域专家合作以确保与业务目标保持一致。领导需求分析，评估相互依赖关系和优先级，并使分析计划与战略路线图和组织目标保持一致。应用Agile软件开发实践来设计、开发和增强分析解决方案。为经验较少的工程师或分析师提供技术领导和指导，涵盖数据科学方法论、机器学习解决方案设计以及可扩展分析系统的最佳实践。Originally posted on Himalayas

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