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 (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.
- Perform exploratory data analysis (EDA) to identify trends, correlations, and actionable patterns that meet business needs.
- 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.
- 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
Education :
Bachelors degree in a quantitative discipline (e.g., Statistics, Mathematics, Computer Science, Engineering, Operations Research, or related field).
Masters degree preferred.
Experience :
- 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.
Technical Skills :
- Proficiency in Python, R, and SQL for data preparation, querying, and model development.
- Strong knowledge of supervised, unsupervised, and generative AI techniques such as regression, classification, clustering, causal inference, and large language models (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 ) to communicate insights effectively.
- Experience working with Linux/Unix and Windows environments.
- Familiarity with Java or C++ is a plus.
Professional Skills :
- Strong analytical skills with attention to detail and a rigorous problem-solving approach.
- Ability to translate complex business problems into high-level AI and analytics solutions.
- Excellent oral and written communication skills, with the ability to explain analytical and generative AI concepts to both technical and non-technical stakeholders.
- Strong storytelling skills to communicate data-driven insights in a clear, impactful way.
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.
- Track record of curiosity and innovation, with the ability to explore complex datasets and generate actionable insights.
- Background in operations research or quantitative social science is a strong plus.
