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Explore your next opportunity at a Fortune Global 500 organization. Envision innovative possibilities, experience our rewarding culture, and work with talented teams that help you become better every day. We know what it takes to lead UPS into tomorrow—people with a unique combination of skill + passion. If you have the qualities and drive to lead yourself or teams, there are roles ready to cultivate your skills and take you to the next level.

Job Description:

JOB SUMMARY
This position creates and implements advanced analytics models and solutions to yield predictive and prescriptive insights from large volumes of structured and unstructured data. This position works with a team responsible for the research and implementation of predictive requirements by leveraging industry standard machine learning and data visualization tools to draw insights that empower confident decisions and product creation. This position leverages emerging tools and technologies available in On-prem and Cloud environments. This position utilizes industry standard machine learning and data visualization tools to transform data and analytics requirements into predictive solutions and provide data literacy on a range of machine learning systems at UPS. This position identifies opportunities for driving descriptive to predictive and prescriptive solutions, which become inputs to department and project teams on their decisions supporting projects.

RESPONSIBILITIES

• Define and integrate key data sources (internal UPS data and external datasets) to deliver predictive and generative AI models.
• Develop and implement robust data pipelines for cleansing, transformation, and enrichment of large, multi-source datasets.
• Collaborate with data engineering teams to validate and test data pipelines and models during proof-of-concept and production phases.
• Design and deploy generative AI solutions, integrating them into analytics and product development workflows.
• Define and track model KPIs, ensuring ongoing validation, testing, and retraining of models to align with business objectives.
• Create reusable and scalable solutions through clear documentation, process flows, logs, and clean, well-commented code.
• Communicate findings through concise reports, data visualizations, and storytelling to both technical and non-technical stakeholders.
• Present operationalized insights and provide strategic recommendations to business and executive-level stakeholders.
• Apply best practices in statistical modeling, machine learning, generative AI, distributed computing, cloud-based AI, and performance optimization for production deployment.

QUALIFICATIONS

• Bachelor’s degree in a quantitative discipline (e.g., Statistics, Mathematics, Computer Science, Engineering, Operations Research, or related field).
• Relevant experience – in applied data science, machine learning, generative AI, or advanced analytics.
  • Min 5+ yrs of relevance experience
• Proven experience in building and launching moderate-to-large-scale analytics and AI projects into production.

Requirements: 

• Strong analytical skills and attention to detail. 
• Relevant experience – in applied data science, machine learning, generative AI, r advanced analytics.
• Proven experience in building and launching moderate-t-large-scale analytics and AI projects int production.
• Expertise with statistical techniques, machine learning or operations research and their application in business applications.
• Expertise in Python, Pandas, SQL, data mining, Supervised Learning, Unsupervised Learning, Deep Learning, Computer Vision, experience with large scale agent/model implementations
• Deep understanding f data management pipelines and experience in launching moderate scale advanced analytics projects in production at scale.
• Demonstrated experience in Cloud-AI technologies and knowledge of environments both in Linux/Unix and Windows. 
• Experience implementing pen-source technologies and cloud services; with r without the use f enterprise data science platforms.
• Solid oral and written communication skills, especially around analytical concepts and methods. 
• Ability to communicate data through a story framework t convey data-driven results t technical and non-technical audience.

Employee Type:
 

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