About this role
Job Summary: The Data Scientist designs, develops, and implements advanced analytics and generative AI models to deliver predictive and prescriptive insights from large-scale structured and unstructured data. This role partners with cross-functional teams to translate business challenges into data-driven solutions, leveraging industry-standard machine learning, generative AI, and data visualization tools to inform confident decision-making and drive innovative product creation. The Data Scientist applies cutting-edge tools and technologies across on-premises and cloud environments (including GCP Vertex AI and IBM Watsonx) to design descriptive, predictive, and prescriptive solutions. This position also fosters data literacy and promotes the adoption of AI and ML capabilities across UPS. Responsibilities: Define and integrate key data sources 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. Perform exploratory data analysis (EDA) to identify trends, correlations, and actionable patterns. 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. 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. Leverage emerging tools, open-source frameworks, and cloud technologies (including Vertex AI, Databricks, and IBM WatsonX) to create predictive and prescriptive analytics solutions. Required Qualifications: Bachelor’s degree in a quantitative discipline (e.g., Statistics, Mathematics, Computer Science, Engineering, Operations Research, or related field); Master’s degree preferred. Minimum 5+ years of experience in applied data science, machine learning, generative AI, or advanced analytics. Proven experience in building and launching moderate-to-large-scale analytics and AI projects into production. Proficiency in Python, R, and SQL. Strong knowledge of supervised, unsupervised, and generative AI techniques such as regression, classification, clustering, causal inference, and LLMs. Hands-on experience with GCP Vertex AI, IBM WatsonX, Databricks, or SageMaker, and frameworks like TensorFlow, PyTorch, and Keras. Familiarity with data visualization tools (e.g., Tableau, Power BI, Shiny, D3). Experience working with Linux/Unix and Windows environments. Familiarity with Java or C++ is a plus. Preferred Experience: Expertise in cloud AI technologies (GCP, IBM WatsonX, AWS, Azure) and modern data pipelines. Demonstrated success in implementing generative AI (LLMs, text-to-image, summarization, conversational AI) for business use cases. Background in operations research or quantitative social science is a strong plus.