小米投资经理/分析师

1.Conduct deep, bottom-up fundamental research on companies in the AI ecosystem, with a primary focus on AI-native applications, agents, and software, as well as other companies where AI is a core driver of business strategy. 2.Continuously monitor existing portfolio positions and stay abreast of key industry developments, technological breakthroughs, and competitive shifts in the AI landscape. 3.Engage with industry experts, attend technology and investment conferences, and build a network to generate unique insights and investment ideas.

1.Conduct research and analysis on the product innovation, market structure, industry trends, and competition dynamics of China & overseas FinTech markets, including analyzing the market performance and trends of listed FinTech stocks. 2.Screen and evaluate various overseas FinTech investment opportunities, including but not limited to payment, neobanks, digital wallets, lending, financial technology, digital currency, etc., conduct investment analysis and due diligence on projects. 3.Communicate and negotiate with target companies, participate in investment negotiations and transactions, and participate in post-investment management and supervision. 4.Regularly write investment reports and industry analysis reports, providing investment advice and decision-making support to the company's senior management. 5.Source potential investment opportunities and build sourcing networks as needed.

团队介绍： 我们在做"AI 在金融投研全链路的落地"，面向研究员、分析师、投资经理等角色，打造生产级的AI智能体系统，为投研业务提供端到端提效能力：从行业深度研究与竞品分析、宏观经济趋势预测与情景推演、投资逻辑构建与论证、研报与投资建议生成，到数据验证与事实核查、多源信息整合与归因，形成可规模化复用的 AI投研生产力体系。岗位方向：Agent Scaffold 研发 × Agent 系统评估 × Agentic RL 模型训练聚焦金融投研场景下的智能体系统构建，将大模型的推理能力与 Agent 的工具使用、任务规划、信息检索与多步推理能力深度结合，构建投研垂类下的 Agent系统：会搜索金融数据、会调用研报API、能分析财务指标、能构建投资逻辑链、能生成可追溯的研究结论，并持续在真实投研业务中提升效果与效率。 岗位职责： 1. 聚焦金融投研全链路提效场景，负责 Agent Scaffold 架构设计与研发，覆盖深度研究、未来预测、投资逻辑构建、长文本内容创作等核心任务； 2. 构建生产级 Agent 能力：多源数据检索与整合（宏观数据、研报库、财报、新闻等）、复杂推理与规划（多步骤任务分解、工具链编排）、证据归因与可追溯引用、长任务执行与失败恢复、结果验证与自我修正等； 3. 设计并实施 Agent 系统评估体系：包括任务完成率、推理准确性、事实一致性、工具调用成功率、成本与延迟等指标，建立可量化的评测基准与持续优化机制； 4. 探索 Agentic RL 模型训练技术：通过强化学习优化 Agent 的决策策略、工具选择、任务规划能力，在数据配比、奖励设计、训练范式上探索最佳实践，推动投研任务效果显著提升； 5. 与投研、产品、工程团队紧密协作，推动 Agent 能力落地到投研产品链路中，实现可持续迭代与规模化应用。

1.负责研究和分析金融科技应用的市场机遇和商业模式，制定公司相关发展策略； 2.进行行业和竞品分析，研究业内最佳实践，为公司竞争策略提供决策依据； 3.组织梳理业务内部目标规划，促进各部门关键举措的实施； 4.协调和推动跨部门的关键合作项目； 5.在公司和各事业群层面就管理问题进行分析和提出解决建议； 6.参与创新金融科技业务的孵化，将个人培育成为参与业务建设的储备人才。