The project : We are looking for an exceptional Senior Data Analyst, specializing in Power BI, for this fully remote opportunity to work with a US-based client in the Bank Investments services industry. Responsabilities : 1. Design
About Us JINGDONG Logistics (JD Logistics) is the logistics arm of JD.Com and one of the worlds leading technology-driven supply chain solutions providers. Leveraging advanced expertise in automation, intelligent logistics, and data-driven operations, we deliver end-to-end logistics
Overview Se pueden requerir diversas habilidades interpersonales y experiencia para el siguiente puesto. Por favor, asegúrese de consultar la descripción a continuación con atención. The Finance Application Architect (TM1) is a key contributor in designing, developing,
Experteer Overview Descubra más sobre las tareas diarias, las responsabilidades generales y la experiencia requerida para esta oportunidad desplazándose hacia abajo ahora. As Finance Application Architect (TM1), you design, implement and govern scalable TM1-based financial planning
About Us JINGDONG Logistics (JD Logistics) is the logistics arm of JD.Com and one of the worlds leading technology-driven supply chain solutions providers. Leveraging advanced expertise in automation, intelligent logistics, and data-driven operations, we deliver end-to-end logistics
Overview Asegúrese de enviar su solicitud rápidamente para maximizar sus posibilidades de ser considerado para una entrevista. Lea la descripción completa del puesto a continuación. The Finance Application Architect (TM1) is a key contributor in designing,
DESCRIPTION Are you a financial accounting professional who consistently exceeds expectations, eager and ever curious, whose ears perk up at the prospect of a challenge? If so, then Amazon is a place where your career can
Bachelors degree in finance, accounting, business, economics, or a related analytical field (e.g., engineering, math, computer science) - 4+ years of Fortune 500 operational accounting experience - 4+ years of identifying, leading, and executing opportunities to
Stack & Ferramentas Linguagem & libs: Python (pandas, NumPy, scikit-learn, statsmodels), XGBoost, LightGBM Estatística & ML clássico: EDA, modelos estatísticos, regressão, classificação, clustering Deep learning: PyTorch, TensorFlow Causal & temporal: séries temporais/forecasting, inferência causal (ex.: EconML,
Stack & Ferramentas Lakehouse & storage: Apache Iceberg, Amazon S3, Athena + Glue (Trino + Hive na evolução) Streaming & ingestão: Kafka (MSK), Apache Flink, Debezium (CDC), Schema Registry Transformação & orquestração: dbt, Apache Airflow Semantic