Hybrid Data Scientist — AI/ML, AWS & Power BI

PSEG; The Ironbound (NJ); Hybride; USD 79 000 - 126 000; Plein temps

Résumé du poste

PSEG is seeking a highly skilled Data Scientist to join our Customer Analytics and IT teams. You will transform raw data into actionable insights, build AI/ML models, and collaborate to improve the customer journey. Proficiency in Python, AWS, and Power BI is essential for delivering data-driven decisions.

The role involves cross-functional collaboration, data pipeline optimization, and effective communication of results to management, with a hybrid onsite/remote work model where applicable.

Qualifications

• Bachelor’s degree in data science, Engineering, Computer Science, IT, Information Systems, Statistics, or related field.
• 2–5 years’ knowledge and experience with data mining techniques, data modeling, statistical analysis, and algorithms.
• 2–5 years’ experience using SQL and developing ETL workflows using SSIS.
• 2–5 years’ experience leveraging AWS services to deploy scalable data pipelines and analytics solutions.
• 2–5 years’ experience in Python for analytics, SQL modeling, and AI/ML model development.
• 2–5 years’ experience in data visualization using Power BI or similar tools.

Responsabilités

• Collect, summarize, analyze, and deliver insights from data across sources.
• Identify valuable data sources and optimize data pipelines for quality, integrity, and security.
• Transform data to uncover patterns and develop analytics solutions, including AI/ML, to improve customer experience.

Connaissances

Data science

Python

Power BI

AWS

SQL

Formation

Bachelor's degree

Master's degree

Outils

SSIS

SQL Server

Description du poste

PSEG is seeking a highly skilled Data Scientist to join our Customer Analytics and IT teams. You will transform raw data into actionable insights, build AI/ML models, and collaborate to improve the customer journey. Proficiency in Python, AWS, and Power BI is essential for delivering data-driven decisions.

The role involves cross-functional collaboration, data pipeline optimization, and effective communication of results to management, with a hybrid onsite/remote work model where applicable.