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Open nowPosted 45 days ago

校招-数据科学

nio/campus870 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now校招-数据科学nio/campus · Shanghai, Chinese Mainland

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The clock on this job

Early applications get read.

11.5% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 45 days ago

The posting

岗位目的 我们正在寻找一位能将前沿技术与市场战略思维深度融合的数据科学家，负责构建汽车行业领先的市场全生命周期预测体系。 你将跳出传统的“单点销量预测”，而是从宏观到微观、从总量到结构，系统性地识别和量化未来细分市场的增长机会。你的工具箱将包括： 机器学习与时序模型（XGBoost、时序混合架构、概率预测） —— 捕捉需求的基本面规律与滚动排产脉搏 大语言模型（LLM） —— 从非结构化文本（政策文件、行业报告、社交媒体讨论）中提取消费趋势与心智风向信号 市场仿真与智能体建模 —— 模拟消费者在不同品牌/车型间的性价比博弈、心智流转与反水轨迹，预测长线竞争动态演化与战略定价弹性 你将与战略、产品、营销团队深度协同，确保预测结果既能经得起数据检验，又能被业务决策者理解和信任。

预测模型开发。 基于内外部多路数据，搭建预测模型，完成周/月/季/年的市场需求预测与 SKU 特征级排产预测。对预测结果进行误差分析，持续迭代特征工程与模型融合策略。 特征工程与数据挖掘。 深度挖掘影响市场大盘的多维特征，包括季节性、节假日、竞品动作（降价/新车上市）、宏观经济指标、供应链大宗商品成本及社交媒体舆情等。 模型评估与部署。 建立完善的交叉验证流程，与数据工程团队协作，将预测与定价沙盘算法接入业务决策系统，实现滚动推演。 业务洞察与报告。 解读预测结果中的关键驱动因素，输出可落地的业务建议。定期向管理层汇报预测准确度改进路线图，具备极强的商业翻译能力，能将复杂的概率或高维仿真转化为高管听得懂的战略语言。 技能要求 计算机、数据科学、统计学、计量经济学、运筹学、计算社会科学及相关专业，博士学历。 深入理解混合时序架构（如 Prophet+XGBoost）的设计思想以及落地。 深入理解因果推断与离散选择模型家族（如 MNL、Nested Logit、BLP）的底层数学逻辑，能够熟练处理价格内生性，输出竞品交叉价格弹性矩阵。 能够通过蒙特卡洛模拟或大规模模拟仿真（Micro-simulation / ABM），推演行业颠覆性政策、突发价格战、全新技术破局带来的市场冲击与概率边界。 极其扎实的 Python 功底，熟练使用数据科学栈（Pandas, NumPy, Scikit-Learn）以及领域专属高级库（如 pyblp、larch、pydlm、PyMC 或 GluonTS）。

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