字节跳动数据科学家 - Applied AI | Data Scientist - Applied AI

任职要求

1、了解统计学，机器学习和分析的基本数学基础知识；
2、至少 3 年数据分析经验，具有 ML/DL 和 CV/NLP/Speech 之一的行业经验；
3、具有探索性数据分析、统计分析和假设检验以及模型开发的经验；
4、精通 SQL、Hive、Presto 或 Spark，并具有处理大型数据集的经验；
5、熟练掌握Python和SQL，以及tensorflow、pytorch等ML/DL框架；
6、清楚地了解数据pipeline、模型开发、模型测试和部署；
7、有 CI/CD（如 git）和云服务（如 AWS/GCP/Azure）的经验者优先；
8、具备英文沟通能力强；能够以易于理解的方式向技术/非技术同事传达分析和技术内容；
9、具有求知欲以及出色的解决问题和量化能力，包括拆解问题、找出根本原因并提出解决方案。

1. Knowledge of underlying mathematical fundamentals in statistics, machine learning and analytics;
2. At least 3 years experience in data modeling/analysis, with industry experience in ML/DL and one of CV/NLP/Speech;
3. Experience with exploratory data analysis, statistical analysis and hypothesis testing, and model development;
4. Fluency in SQL, Hive, Presto, or Spark and having experience working with large datasets;
5. High proficiency in Python and SQL, and ML/DL frameworks such as tensorflow, pytorch;
6. Clear understanding of data pipeline, model development, model testing and deployment;
7. Experience in CI/CD such as git and cloud services such as AWS/GCP/Azure will be highly desirable;
8. Advanced English with good communications skills; able to communicate analytical and technical content in an easy to understand way to both technical and non-technical audiences;
9. Intellectual curiosity, along with excellent problem-solving and quantitative skills, including the ability to desegregate issues, identify root causes and recommend solutions.

工作职责

我们正在寻找AI/ML技术的技术专家，包括计算机视觉(CV)，自然语言处理(NLP)和音频信号处理。您将负责与各种利益相关者(产品、运营、政策和工程)合作，并开发最先进的模型。
1、利用自然语言处理、机器学习或计算机视觉等内容理解能力设计和构建产品核心能力，提取数据洞察并优化变现策略；
2、基于最新的深度学习、机器学习、统计和优化技术的算法开发创新性解决方案并构建业务问题原型；
3、从0到1管理数据项目，并与产品经理协作定义用户故事和成功指标来指导开发过程；
4、使用不限于AB测试等方法验证项目的商业价值和预计收益；
5、与工程团队合作部署数据模型并将解决方案规模化。

We are looking for generalists and specialists in AI/ML techniques, including computer vision (CV), natural language processing (NLP), and audio signal processing. You will be responsible for partnering with a variety of stakeholders (product, operations, policy, and engineering) and developing state-of-the-art models.
1. Design and build core capabilities by leveraging content understanding; capabilities, such as natural language processing, machine learning, or computer vision, to extract insights and improve monetization strategies;
2. Develop creative solutions and build prototypes for business problems using algorithms based on the latest deep learning, machine learning, statistics, and optimization techniques;
3. Independently manage data projects from 0 to 1, and collaborate with product managers to define user stories, and success metrics to guide the development process;
4. Verify the business value and estimated revenue of the project using methods such as AB testing;
5. Collaborate with engineering teams to deploy and scale data science solutions.