Data Scientist – Python, GCP, Data Mining, Machine Learning, Computer Vision

• 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.

• 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.

• 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.

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