施耐德Data Scientist

1、以数据视角驱动机器人场景的 VLM/VLA研究与落地，负责数据策略、数据工程与数据科学全链路设计与数据闭环迭代。包括仿真/合成数据的构建，真机数据的采集方案制定与规模化采集规划，支撑具身智能原生多模态大模型、世界模型的训练、对齐与部署。

角色定位: 我们正在寻找一位能源管理和工业自动化领域的数据科学家，负责在能源管理和工业自动化领域的特有场景中寻找数据规律，挖掘数据价值，建立数学模型，完成AI模型开发的前期数据工作。作为一名数据科学家，您将参与真实客户项目的数据工作和AI标准化产品的数据工作。你的相关合作方包括：自动化工程师、领域专家，算法产品，算法开发。 主要职责: 1. 能源场景建模：能源管理优化的数据分析，特征工程，设备建模，场景建模（如：数据中心制冷/供电系统节能、微电网调度与储能控制） ； 2. 工业场景建模：针对设备预测性维护、能源消耗优化、生产流程提效等场景，开发机器学习/深度学习模型（如时序预测、异常检测、故障诊断）； 3. 工业数据治理：处理传感器时序数据（SCADA/DCS系统）、设备日志、能源计量数据，解决数据缺失、噪声干扰等工业现场问题； 4. 跨部门协作：与自动化工程师、领域专家合作，理解业务场景，与算法产品，算法研发合作，完成数据建模，设计AI算法模型； 5. 价值闭环验证：设计A/B测试，量化模型对能耗降低、故障率下降、产能提升的实际效益；

1. 研究、开发、迭代和维护营销归因和度量，向数据分析师和业务团队提供专家建议，并与产品、数据分析师和工程师合作，将归因和度量方案产品化 2. 设计和开发因果推断及预算分配方法，为团队的跨渠道投资决策作出数据驱动的指导 3. 使用统计/机器学习模型，完成探索类数据科学项目的立项及落地 4. 宣讲方法论，推动并拓展数据驱动的业务场景，提高业务的整体数据素养和参与度

1. Generative AI Model Development: -Design and develop generative AI models, including language models, image generation models, and multimodal models. -Explore and implement advanced techniques in areas such as transformer architectures, attention mechanisms, and self-supervised learning. -Conduct research and stay up-to-date with the latest advancements in the field of generative AI. 2. Data Acquisition and Preprocessing: -Identify and acquire relevant data sources for training generative AI models. -Develop robust data preprocessing pipelines, ensuring data quality, cleanliness, and compliance with ethical and regulatory standards. -Implement techniques for data augmentation, denoising, and domain adaptation to enhance model performance. 3. Model Training and Optimization: -Design and implement efficient training pipelines for large-scale generative AI models. -Leverage distributed computing resources, such as GPUs and cloud platforms, for efficient model training. -Optimize model architectures, hyperparameters, and training strategies to achieve superior performance and generalization. 4. Model Evaluation and Deployment: -Develop comprehensive evaluation metrics and frameworks to assess the performance, safety, and bias of generative AI models. -Collaborate with cross-functional teams to ensure the successful deployment and integration of generative AI models into client solutions. 5. Collaboration and Knowledge Sharing: -Collaborate with data engineers, software engineers, and subject matter experts to develop innovative solutions leveraging generative AI. -Contribute to the firm's thought leadership by presenting at conferences, and participating in industry events.