Hybrid Data Science & Asset Optimization Analyst

IBM; City of Poughkeepsie (NY); Hybrid; USD 89,000 - 133,000; Full time

Benefits offered by this job

Healthcare benefits

401(k) plan

Paid time off

Educational resources

Job summary

IBM is hiring an entry-level Cost & Asset Optimization Data Science Analyst 2027 in Poughkeepsie, NY. The role combines cross-functional collaboration with product management, supply chain, and finance to optimize cash flow and processes using data science and AI tools.

The position requires a Bachelor's degree (Master's preferred) and exposure to cloud tech, agile methods, and strong communication. Hybrid work arrangement with three days in the office is typical.

Qualifications

• Bachelor's Degree in Data Science/IT/Computer Science/Engineering.
• Exposure to agile practices and design thinking.
• Strong written and verbal communication skills.
• Problem solving and business analysis skills.
• Ability to work three days from the office.
• Exposure to Cloud & Technology.
• Technical expertise and ability to communicate with stakeholders.
• Exposure to process optimization leveraging AI tools.

Responsibilities

• Collaborate with cross-functional teams (product management, supply chain, procurement, development, engineering and finance).
• Analyze competitors, market trends and industry standards.
• Optimize and improve process through use of automation and AI tools.

Skills

Data Science

Agile

Design Thinking

Communication

Problem Solving

Education

Bachelor's degree

Master's degree

Tools

AI Tools

Job description

IBM is hiring an entry-level Cost & Asset Optimization Data Science Analyst 2027 in Poughkeepsie, NY. The role combines cross-functional collaboration with product management, supply chain, and finance to optimize cash flow and processes using data science and AI tools.

The position requires a Bachelor's degree (Master's preferred) and exposure to cloud tech, agile methods, and strong communication. Hybrid work arrangement with three days in the office is typical.