锦鲤求职岗位描述
About Us / 关于我们:
Join our Digital Labs, a top-tier digital product team comprising passionate experts in AI, data science, engineering, and product development. Driven by a spirit of craftsmanship, we develop cutting-edge platforms and tools that accelerate innovation and solve complex real-world problems.
加入我们数字化实验室（Digital Labs），这里汇聚了人工智能、数据科学、工程技术和产品开发领域的顶尖人才。我们秉承匠心精神，致力于开发前沿平台和工具，加速创新，解决复杂的实际问题。
Role Description / 职位描述:
As a Data Scientist, you will play a pivotal role in the full lifecycle of machine learning and deep learning projects—from deep-dive data analytics and problem formulation to solution design, development, deployment, and ongoing monitoring. You will translate complex business challenges in areas such as pricing, resource allocation, commercial analysis, and process optimization into robust, production-grade models that deliver measurable impact. While not a mandatory requirement, experience or interest in Generative AI is considered a strong plus.
作为数据科学家，您将在机器学习与深度学习项目的全生命周期中扮演关键角色——从深入的数据分析和问题定义，到解决方案的设计、开发、部署和持续监控。您将把定价、资源分配、商业分析和流程优化等领域的复杂业务挑战，转化为稳健的生产级模型，创造可衡量的业务影响。生成式AI经验虽非必需，但将作为重要加分项。
Key Responsibilities / 核心职责:
Partner with business stakeholders to deeply analyze complex requirements, conduct exploratory data analysis, and translate business problems into well-defined data science and algorithmic solutions.
与业务方合作，深入分析复杂需求，进行探索性数据分析，将业务问题转化为定义清晰的数据科学和算法解决方案。
Design, develop, deploy, and maintain scalable machine learning and deep learning models, ensuring high performance, reliability, and interpretability.
设计、开发、部署和维护可扩展的机器学习和深度学习模型，确保高性能、可靠性和可解释性。
Monitor live model performance, proactively identify drift or degradation, and implement retraining and optimization strategies to sustain business value.
监控线上模型表现，主动发现漂移或衰退现象，实施重训练与优化策略，持续保障业务价值。
Collaborate with cross-functional teams including engineering, product, and business to seamlessly integrate technical solutions into business outcomes.
与工程、产品和业务等跨职能团队紧密协作，将技术解决方案顺畅地转化为业务成果。
Maintain a high standard of code quality, reproducibility, testing, and documentation.
保持高标准的代码质量、可复现性、测试和文档化。
Stay updated on cutting-edge AI/ML technologies and methodologies, and thoughtfully integrate them into projects where appropriate.
持续跟踪AI/ML领域的最新技术和方法，并在合适的场景中加以应用。
Daily Work / 日常工作:
End-to-End Solution Development: Design, develop, and deploy production-grade data science solutions across diverse domains such as predictive modeling, recommendation systems, customer analytics, and business process optimization.
端到端方案开发： 设计、开发和部署生产级的数据科学解决方案，覆盖预测建模、推荐系统、客户分析及业务流程优化等多个领域。
Advanced Algorithm Design: Research, develop, and optimize a variety of machine learning models and algorithms, including deep neural networks, tree-based methods, and classical statistical models. Explore advanced techniques like Large Language Model workflows as a plus.
高级算法设计： 研究、开发并优化各类机器学习模型与算法，包括深度神经网络、树模型和经典统计模型。探索大语言模型工作流等先进技术是加分项。
Complex Business Analytics: Dive deep into intricate business data from areas like pricing strategy, resource allocation, commercial planning, and process analysis. Generate actionable insights and build data-driven models that directly inform strategic decisions.
复杂业务分析： 深入钻研定价策略、资源分配、商业规划和流程分析等领域的复杂业务数据，提炼可执行的洞察，并构建直接支持战略决策的数据驱动模型。
Data Pipeline Collaboration: Work on data architecture design and develop robust data pipelines from multiple sources to support model training, evaluation, and real-time applications.
数据管道协作： 参与数据架构设计，开发来自多数据源的稳健数据管道，以支持模型训练、评估和实时应用。
Insight Communication: Create compelling data visualizations and narratives to effectively communicate complex insights and model outcomes to both technical and non-technical stakeholders.
洞察传达： 创建高质量的数据可视化和分析报告，向技术及非技术利益相关者有效传达复杂洞察和模型结果。
Key Qualifications / 任职要求 (Education, Certificates & Experience):
Master’s degree or higher in Computer Science, Data Science, Statistics, Operations Research, or a related quantitative field.
计算机科学、数据科学、统计学、运筹学或相关量化领域的硕士及以上学历。
At least 5+ years of hands-on experience in AI/ML, data science, and data engineering.
至少5年以上AI/ML、数据科学和数据工程领域的实践经验。
Proven track record in the full lifecycle of ML/DL projects: problem analysis, solution design, development, deployment, and production monitoring.
具备机器学习/深度学习项目全生命周期的可靠经验，涵盖问题分析、方案设计、开发、部署和生产监控。
Strong experience applying data analytics and modeling to complex business problems (e.g., pricing, resource allocation, commercial analysis, process optimization).
在运用数据分析与建模解决复杂业务问题（如定价、资源分配、商业分析、流程优化）方面经验丰富。
Solid foundation in data structures, algorithms, statistics, and system design.
扎实的数据结构、算法、统计学和系统设计基础。
Excellent communication and teamwork skills, with a proactive problem-solving attitude.
优秀的沟通与团队协作能力，积极解决问题的态度。
Proficiency in Mandarin and English is preferred.
优秀的中英文能力者优先。
Plus: Experience in the biopharmaceutical, biologics, or life sciences industry.
加分项： 具备生物制药、生物制品或生命科学行业经验。
Technical Expertise / 技术专长 (Hard Tech Tools & Skills):
Programming languages: Python, R, or Java.
编程语言：Python、R 或 Java。
Machine learning frameworks & libraries: TensorFlow, PyTorch, Scikit-learn.
机器学习框架与库：TensorFlow、PyTorch、Scikit-learn。
Big data technologies: Spark, Hive, and strong familiarity with both relational and NoSQL databases.
大数据技术：Spark、Hive，并非常熟悉关系型与 NoSQL 数据库。
Cloud platforms: hands-on experience with AWS, Azure, or GCP for data processing and model deployment.
云平台：具备利用 AWS、Azure 或 GCP 进行数据处理与模型部署的实战经验。
Data governance: understanding of data quality, security, and metadata management.
数据治理：理解数据质量、安全与元数据管理。
Plus: Familiarity with Generative AI tools and frameworks (e.g., LangChain, vector databases, LLM APIs).
加分项： 熟悉生成式AI工具与框架（如 LangChain、向量数据库、LLM API）。