Amazon Global Selling has been helping individuals businesses increase sales reach new customers around the globe. Today, more than 50% of Amazons total unit sales come from third-party selection. The Global Selling team in China is responsible frecruiting local businesses to sell on Amazon’s 19+ overseas marketplaces supporting local Sellers’ success growth on the Amazon. Our vision is to be the first choice fall types of Chinese business to go globally.
The Amazon Global Selling Analytics, Intelligence, Technology (AGS-AIT) team serves as the research, automation, insight arm of the International Seller Service data hub, enabling rapid delivery of growth insights through strategic investments in regional data foundations, self-service business intelligence solutions, artificial intelligence tools.
The AGS-AIT team is positioned to establish AI-ready foundational capabilities across the AGSganization while maintaining excellence in business insight generation, self-service BI/AI application development.
AGS-AIT is looking fa Data Engineer to collaborate with cross-functional teams to design develop data infrastructure analytics capabilities fAGS AI Automation initiatives.
Key job responsibilities
? Design implement end-to-end data pipelines (ETL) to ensure efficient data collection, cleansing, transformation, storage, supporting both real-time offline analytics needs.
? Develop automated data monitoring tools interactive dashboards to enhance business teams’ insights core metrics (e.g., user behavior, AI model performance).
? Collaborate with cross-functional teams (e.g., Product, Operations, Tech) to align data logic, integrate multi-source data (e.g., user behavior, transaction logs, AI outputs), build a unified data layer.
? Establish data standardization governance policies to ensure consistency, accuracy, compliance.
? Provide structured data inputs fAI model training inference (e.g., LLM applications, recommendation systems), optimizing feature engineering workflows.
Basic qualifications
1+ years of data engineering experience
Experience with data modeling, warehousing building ETL pipelines
Experience with one more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
Experience with one more scripting language (e.g., Python, KornShell)
Preferred qualifications
Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc.
Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, IAM roles permissions
更新于 2026-01-26
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As a Data Scientist, you will design, develop, deploy predictive models machine learning solutions to solve complex business challenges. You’ll collaborate with data engineers, business analysts, clients to translate requirements scalable data products, from concept to production.
This role is ideal fsomeone passionate about statistical modelling, machine learning, deriving actionable insights from large datasets.
Key Responsibilities
1. Develop robust data models conduct feature engineering to improve model performance
2. Perform exploratory data analysis (EDA) to uncover patterns, trends, insights
3. Collaborate with data engineers to build optimize data pipelines fmodel training inference
4. Evaluate model performance using appropriate metrics validation techniques
5. Deploy models production environments in collaboration with ML engineers DevOps teams
6. Communicate findings recommendations to technical non-technical stakeholders through visualizations reports
7. Stay up to date with emerging trends in AI/ML apply best practices in model interpretability, fairness, MLOps
Requirements
1. Bachelor’s Master’s degree in Data Science, Computer Science, Statistics, Mathematics, a related field
2. 3+ years of experience in data science machine learning roles
Strong proficiency in Python (e.g., pandas, scikit-learn, NumPy, statsmodels)
3. Experience with data modelling, feature engineering, statistical analysis
4. Familiarity with MLOps tools (e.g., MLflow, Kubeflow, Airflow) cloud platforms (AWS, GCP, Azure)
5. Knowledge of SQL database systems
6. Experience with version control (Git) collaborative development workflows
7. Strong analytical thinking problem-solving skills
更新于 2025-12-03
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